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Han H, Zhang C, Zhu L, Singh P, Hsung RTC, Leung YY, Komura T, Wang W, Gu M. Facial surgery preview based on the orthognathic treatment prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025; 267:108781. [PMID: 40286420 DOI: 10.1016/j.cmpb.2025.108781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2025] [Revised: 03/27/2025] [Accepted: 04/12/2025] [Indexed: 04/29/2025]
Abstract
BACKGROUND AND OBJECTIVE Orthognathic surgery consultations are essential for helping patients understand how their facial appearance may change after surgery. However, current visualization methods are often inefficient and inaccurate due to limited pre- and post-treatment data and the complexity of the treatment. This study aims to develop a fully automated pipeline for generating accurate and efficient 3D previews of postsurgical facial appearances without requiring additional medical images. METHODS The proposed method incorporates novel aesthetic criteria, such as mouth-convexity and asymmetry, to improve prediction accuracy. To address data limitations, a robust data augmentation scheme is implemented. Performance is evaluated against state-of-the-art methods using Chamfer distance and Hausdorff distance metrics. Additionally, a user study involving medical professionals and engineers was conducted to evaluate the effectiveness of the predicted models. Participants performed blinded comparisons of machine learning-generated faces and real surgical outcomes, with McNemar's test used to analyze the robustness of their differentiation. RESULTS Quantitative evaluations showed high prediction accuracy for our method, with a Hausdorff Distance of 9.00 millimeters and Chamfer Distance of 2.50 millimeters, outperforming the state of the art. Even without additional synthesized data, our method achieved competitive results (Hausdorff Distance: 9.43 millimeters, Chamfer Distance: 2.94 millimeters). Qualitative results demonstrated accurate facial predictions. The analysis revealed slightly higher sensitivity (54.20% compared to 53.30%) and precision (50.20% compared to 49.40%) for engineers compared to medical professionals, though both groups had low specificity, approximately 46%. Statistical tests showed no significant difference in distinguishing Machine Learning-Generated faces from Real Surgical Outcomes, with p-values of 0.567 and 0.256, respectively. Ablation tests demonstrated the contribution of our loss functions and data augmentation in enhancing prediction accuracy. CONCLUSION This study provides a practical and effective solution for orthognathic surgery consultations, benefiting both doctors and patients by improving the efficiency and accuracy of 3D postsurgical facial appearance previews. The proposed method has the potential for practical application in pre-surgical visualization and aiding in decision-making.
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Affiliation(s)
- Huijun Han
- Discipline of Orthodontics, Faculty of Dentistry, the University of Hong Kong, Hong Kong Special Administrative Region of China; Department of Computer Science and Engineering, Texas A&M University, TX, USA
| | - Congyi Zhang
- Department of Computer Science, Faculty of Engineering, the University of Hong Kong, Hong Kong Special Administrative Region of China.
| | - Lifeng Zhu
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Pradeep Singh
- Discipline of Orthodontics, Faculty of Dentistry, the University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Richard Tai-Chiu Hsung
- Discipline of Oral and Maxillofacial Surgery, the University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Yiu Yan Leung
- Discipline of Oral and Maxillofacial Surgery, the University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Taku Komura
- Department of Computer Science, Faculty of Engineering, the University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Wenping Wang
- Department of Computer Science and Engineering, Texas A&M University, TX, USA
| | - Min Gu
- Discipline of Orthodontics, Faculty of Dentistry, the University of Hong Kong, Hong Kong Special Administrative Region of China.
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Naskar S, Sharma S, Kuotsu K, Halder S, Pal G, Saha S, Mondal S, Biswas UK, Jana M, Bhattacharjee S. The biomedical applications of artificial intelligence: an overview of decades of research. J Drug Target 2025; 33:717-748. [PMID: 39744873 DOI: 10.1080/1061186x.2024.2448711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Revised: 12/13/2024] [Accepted: 12/26/2024] [Indexed: 01/11/2025]
Abstract
A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.
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Affiliation(s)
- Sweet Naskar
- Department of Pharmaceutics, Institute of Pharmacy, Kalyani, West Bengal, India
| | - Suraj Sharma
- Department of Pharmaceutics, Sikkim Professional College of Pharmaceutical Sciences, Sikkim, India
| | - Ketousetuo Kuotsu
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Suman Halder
- Medical Department, Department of Indian Railway, Kharagpur Division, Kharagpur, West Bengal, India
| | - Goutam Pal
- Service Dispensary, ESI Hospital, Hoogly, West Bengal, India
| | - Subhankar Saha
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Shubhadeep Mondal
- Department of Pharmacology, Momtaz Begum Pharmacy College, Rajarhat, West Bengal, India
| | - Ujjwal Kumar Biswas
- School of Pharmaceutical Science (SPS), Siksha O Anusandhan (SOA) University, Bhubaneswar, Odisha, India
| | - Mayukh Jana
- School of Pharmacy, Centurion University of Technology and Management, Centurion University, Bhubaneswar, Odisha, India
| | - Sunirmal Bhattacharjee
- Department of Pharmaceutics, Bharat Pharmaceutical Technology, Amtali, Agartala, Tripura, India
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Sadr H, Nazari M, Khodaverdian Z, Farzan R, Yousefzadeh-Chabok S, Ashoobi MT, Hemmati H, Hendi A, Ashraf A, Pedram MM, Hasannejad-Bibalan M, Yamaghani MR. Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches. Eur J Med Res 2025; 30:418. [PMID: 40414894 DOI: 10.1186/s40001-025-02680-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2024] [Accepted: 05/11/2025] [Indexed: 05/27/2025] Open
Abstract
The rapid advancement of Machine Learning (ML) and Deep Learning (DL) technologies has revolutionized healthcare, particularly in the domains of disease prediction and diagnosis. This study provides a comprehensive review of ML and DL applications across sixteen diverse diseases, synthesizing findings from research conducted between 2015 and 2024. We explore these technologies' methodologies, effectiveness, and clinical outcomes, highlighting their transformative potential in healthcare settings. Although ML and DL demonstrate remarkable accuracy and efficiency in disease prediction and diagnosis, challenges including quality of data, interpretability of models, and their integration into clinical workflows remain significant barriers. By evaluating advanced approaches and their outcomes, this review not only underscores the current capabilities of ML and DL but also identifies key areas for future research. Ultimately, this work aims to serve as a roadmap for advancing healthcare practices, enhancing clinical decision making, and strengthening patient outcomes through the effective and responsible implementation of AI-driven technologies.
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Affiliation(s)
- Hossein Sadr
- Department of Artificial Intelligence in Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
- Neuroscience Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran.
| | - Mojdeh Nazari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Cardiovascular Disease Research Center, Department of Cardiology, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
| | - Zeinab Khodaverdian
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ramyar Farzan
- Department of Plastic and Reconstructive Surgery, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | | | - Mohammad Taghi Ashoobi
- Razi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | - Hossein Hemmati
- Razi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | - Amirreza Hendi
- Dental Sciences Research Center, Department of Prosthodontics, School of Dentistry, Guilan University of Medical Sciences, Rasht, Iran
| | - Ali Ashraf
- Clinical Research Development Unit of Poursina Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
| | | | - Mohammad Reza Yamaghani
- Department of Computer Engineering and Information Technology, La.C., Islamic Azad University, Lahijan, Iran
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Harrison LM, Edison RL, Hallac RR. Artificial Intelligence Applications in Pediatric Craniofacial Surgery. Diagnostics (Basel) 2025; 15:829. [PMID: 40218180 PMCID: PMC11989140 DOI: 10.3390/diagnostics15070829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2025] [Revised: 03/09/2025] [Accepted: 03/19/2025] [Indexed: 04/14/2025] Open
Abstract
Artificial intelligence is rapidly transforming pediatric craniofacial surgery by enhancing diagnostic accuracy, improving surgical precision, and optimizing postoperative care. Machine learning and deep learning models are increasingly used to analyze complex craniofacial imaging, enabling early detection of congenital anomalies such as craniosynostosis, and cleft lip and palate. AI-driven algorithms assist in preoperative planning by identifying anatomical abnormalities, predicting surgical outcomes, and guiding personalized treatment strategies. In cleft lip and palate care, AI enhances prenatal detection, severity classification, and the design of custom therapeutic devices, while also refining speech evaluation. For craniosynostosis, AI supports automated morphology classification, severity scoring, and the assessment of surgical indications, thereby promoting diagnostic consistency and predictive outcome modeling. In orthognathic surgery, AI-driven analyses, including skeletal maturity evaluation and cephalometric assessment, inform optimal timing and diagnosis. Furthermore, in cases of craniofacial microsomia and microtia, AI improves phenotypic classification and surgical planning through precise intraoperative navigation. These advancements underscore AI's transformative role in diagnostic accuracy, and clinical decision-making, highlighting its potential to significantly enhance evidence-based pediatric craniofacial care.
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Affiliation(s)
- Lucas M. Harrison
- Department of Plastic Surgery, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ragan L. Edison
- Analytical Imaging and Modeling Center, Children’s Health Medical Center, Dallas, TX 75235, USA
| | - Rami R. Hallac
- Department of Plastic Surgery, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Analytical Imaging and Modeling Center, Children’s Health Medical Center, Dallas, TX 75235, USA
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Wu R, Liao S, Dai P, Han F, Kui X, Song X. Unsupervised generative model for simulating post-operative double eyelid image. Phys Eng Sci Med 2025; 48:49-58. [PMID: 39432232 DOI: 10.1007/s13246-024-01488-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 10/01/2024] [Indexed: 10/22/2024]
Abstract
Simulating the outcome of double eyelid surgery is a challenging task. Many existing approaches rely on complex and time-consuming 3D digital models to reconstruct facial features for simulating facial plastic surgery outcomes. Some recent research performed a simple affine transformation approach based on 2D images to simulate double eyelid surgery outcomes. However, these methods have faced challenges, such as generating unnatural simulation outcomes and requiring manual removal of masks from images. To address these issues, we have pioneered the use of an unsupervised generative model to generate post-operative double eyelid images. Firstly, we created a dataset involving pre- and post-operative 2D images of double eyelid surgery. Secondly, we proposed a novel attention-class activation map module, which was embedded in a generative adversarial model to facilitate translating a single eyelid image to a double eyelid image. This innovative module enables the generator to selectively focus on the eyelid region that differentiates between the source and target domain, while enhancing the discriminator's ability to discern differences between real and generated images. Finally, we have adjusted the adversarial consistency loss to guide the generator in preserving essential features from the source image and eliminating any masks when generating the double eyelid image. Experimental results have demonstrated the superiority of our approach over existing state-of-the-art techniques.
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Affiliation(s)
- Renzhong Wu
- School of Computer Science and Engineering, Central South University, Changsha, 410000, China
| | - Shenghui Liao
- School of Computer Science and Engineering, Central South University, Changsha, 410000, China
| | - Peishan Dai
- School of Computer Science and Engineering, Central South University, Changsha, 410000, China.
| | - Fuchang Han
- School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha, 410013, China
| | - Xiaoyan Kui
- School of Computer Science and Engineering, Central South University, Changsha, 410000, China
| | - Xuefei Song
- Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200011, China
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Sankar H, Alagarsamy R, Lal B, Rana SS, Roychoudhury A, Agrawal A, Wankhar S. Role of artificial intelligence in treatment planning and outcome prediction of jaw corrective surgeries by using 3-D imaging: a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2025; 139:299-310. [PMID: 39701860 DOI: 10.1016/j.oooo.2024.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2024] [Revised: 08/21/2024] [Accepted: 09/17/2024] [Indexed: 12/21/2024]
Abstract
OBJECTIVE Artificial intelligence (AI) has been increasingly utilized in diagnosis of skeletal deformities, while its role in treatment planning and outcome prediction of jaw corrective surgeries with 3-dimensional (3D) imaging remains underexplored. METHODS The comprehensive search was done in PubMed, Google scholar, Semantic scholar and Cochrane Library between January 2000 and May 2024. Inclusion criteria encompassed studies on AI applications in treatment planning and outcome prediction for jaw corrective surgeries using 3D imaging. Data extracted included study details, AI algorithms, and performance metrics. Modified PROBAST tool was used to assess the risk of bias (ROB). RESULTS Fourteen studies were included. 11 studies used deep learning algorithms, and 3 employed machine learning on CT data. In treatment planning the prediction error was 0.292 to 3.32 mm (N = 5), and Dice score was 92.24 to 96% (N = 2). Accuracy of outcome predictions varied from 85.7% to 99.98% (N = 2). ROB was low in most of the included studies. A meta-analysis was not conducted due to significant heterogeneity and insufficient data reporting in the included studies. CONCLUSION 3D imaging-based AI models in treatment planning and outcome prediction for jaw corrective surgeries show promise but remain at proof-of-concept. Further, prospective multicentric studies are needed to validate these findings.
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Affiliation(s)
- Hariram Sankar
- Department of Dentistry, All India Institute of Medical Sciences, Bathinda, Punjab, India
| | - Ragavi Alagarsamy
- Department of Burns, Plastic and Maxillofacial Surgery, VMMC and Safdarjung hospital, New Delhi, India
| | - Babu Lal
- Department of Trauma and Emergency Medicine, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India.
| | - Shailendra Singh Rana
- Department of Dentistry, All India Institute of Medical Sciences, Bathinda, Punjab, India
| | - Ajoy Roychoudhury
- Department of Oral & Maxillofacial Surgery, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Agrawal
- Department of Neurosurgery, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
| | - Syrpailyne Wankhar
- Department of Translational Medicine, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
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Tian WM, Porras Fimbres DC, Tran MM, Zeng SL, Gnaedinger AG, Kaplan S, Marcus JR, Patel A. Quality and Reliability of 2D and 3D Clinical Photographs in Plastic Surgery: A Scoping Review. Aesthetic Plast Surg 2025:10.1007/s00266-025-04751-w. [PMID: 40016579 DOI: 10.1007/s00266-025-04751-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2024] [Accepted: 02/03/2025] [Indexed: 03/01/2025]
Abstract
BACKGROUND Clinical photography is a critical tool in plastic surgery practice. The primary aim of this scoping review was to assess the quality and reliability of various 2D and 3D photography modalities in plastic surgery. METHODS The databases, MEDLINE via PubMed, Embase via Elsevier, and Scopus via Elsevier, were searched from inception to March 15, 2023, for any papers focused on evaluation of quality and/or reliability of photography in plastic surgery-related applications. Data regarding general article characteristics, study characteristics, participants, and study results were extracted by two reviewers and qualitatively synthesized. RESULTS A total of 17 studies published from 1999 to 2023 were eligible for inclusion in the final analysis. Thirteen (76.5%) were cross-sectional and four (23.5%) were cohort studies. Photographed locations included craniomaxillofacial and oral (n = 11, 64.7%), scars (n = 3, 17.6%), breast and torso (n = 2, 11.8%), and wounds (n = 1, 5.9%). Assessed photographic modalities were found to be on par or better than comparators in almost all applicable cases (n = 13/14, 92.9%). CONCLUSIONS Multiple photographic modalities were found to have adequate quality and reliability. 3D photography was found to be a superior imaging modality for craniomaxillofacial and oral applications. For scar and wound assessment and images of the breast and torso region, 2D photography was sufficient. Overall, 2D photography using smartphone devices is sufficient for reliable and quality images in most clinical settings. With continued advances in technology, the ability to capture clinical images will undoubtedly improve, and there is a need for future studies to assess and compare these modalities. LEVEL OF EVIDENCE III This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
| | - Denisse C Porras Fimbres
- Division of Plastic, Maxillofacial, and Oral Surgery, Duke University, DUMC 3974, Durham, NC, 27710, USA
| | | | | | | | | | - Jeffrey R Marcus
- Division of Plastic, Maxillofacial, and Oral Surgery, Duke University, DUMC 3974, Durham, NC, 27710, USA
| | - Ashit Patel
- Division of Plastic, Maxillofacial, and Oral Surgery, Duke University, DUMC 3974, Durham, NC, 27710, USA.
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Savage SA, Seth I, Angus ZG, Rozen WM. Advancements in microsurgery: A comprehensive systematic review of artificial intelligence applications. J Plast Reconstr Aesthet Surg 2025; 101:65-76. [PMID: 39708634 DOI: 10.1016/j.bjps.2024.11.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Revised: 11/18/2024] [Accepted: 11/21/2024] [Indexed: 12/23/2024]
Abstract
Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and discern patterns without direct instruction. This review explores cutting-edge developments in microsurgery through the lens of AI applications. By analyzing a wide range of studies, this paper highlights AI's transformative role in enhancing microsurgical techniques and decision-making processes. A systematic literature search was conducted using Ovid MEDLINE, Ovid Embase, Web of Science, and PubMed (2005-2023). Extensive data on ML model function and composition, as well as broader study characteristics, were collected from each study. Study quality was assessed across 7 methodological areas of AI research using an adapted methodological index of nonrandomized studies (MINORS) tool. Seventeen studies met the inclusion criteria. ML was used primarily for prognosis (35%), postoperative assessment (29%), and intraoperative assistance/robotic surgery (24%). Only 2 studies were conducted beyond phase 0 of AI research. Fourteen studies included a training group, but only one of these reported both validation and training sets. ML model performance was assessed most frequently using accuracy, specificity, and sensitivity. Scores for the adapted MINORS criteria ranged from 10 to 14 out of 14, with a median of 12. Through collation of all available preclinical and clinical trials, this review suggests the efficacy of ML for various microsurgical applications. Despite this, widespread adoption of this technology remains scarce, currently limited by methodological flaws of individual studies and structural barriers to disruptive technologies. However, with growing evidence supporting its use, microsurgeons should be receptive to implementing ML-incorporated technologies or may risk falling behind other specialties.
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Affiliation(s)
- Simon A Savage
- Department of Plastic Surgery, Frankston Hospital, Peninsula Health, 2 Hastings Road, Frankston 3199, Australia; Department of Surgery, Peninsula Clinical School, Central Clinical School, Faculty of Medicine, Monash University, 2 Hastings Road, Frankston 3199, Australia.
| | - Ishith Seth
- Department of Plastic Surgery, Frankston Hospital, Peninsula Health, 2 Hastings Road, Frankston 3199, Australia; Department of Surgery, Peninsula Clinical School, Central Clinical School, Faculty of Medicine, Monash University, 2 Hastings Road, Frankston 3199, Australia
| | - Zachary G Angus
- Department of Surgery, Peninsula Clinical School, Central Clinical School, Faculty of Medicine, Monash University, 2 Hastings Road, Frankston 3199, Australia
| | - Warren M Rozen
- Department of Plastic Surgery, Frankston Hospital, Peninsula Health, 2 Hastings Road, Frankston 3199, Australia; Department of Surgery, Peninsula Clinical School, Central Clinical School, Faculty of Medicine, Monash University, 2 Hastings Road, Frankston 3199, Australia
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Singh P, Hsung RTC, Ajmera DH, Said NA, Leung YY, McGrath C, Gu M. Smartphone-generated 3D facial images: reliable for routine assessment of the oronasal region of patients with cleft or mere convenience? A validation study. BMC Oral Health 2024; 24:1517. [PMID: 39702086 DOI: 10.1186/s12903-024-05280-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Accepted: 11/29/2024] [Indexed: 12/21/2024] Open
Abstract
OBJECTIVES To evaluate the validity and reliability of smartphone-generated three-dimensional (3D) facial images for routine evaluation of the oronasal region of patients with cleft by comparing their accuracy to that of direct anthropometry (DA) and 3dMD. MATERIALS AND METHODS Eighteen soft-tissue facial landmarks were manually labelled on each of the 17 (9 males and 8 females; mean age 23.3 ± 5.4 years) cleft lip and palate (CLP) patients' faces. Two surface imaging systems, 3dMDface and Bellus3D FaceApp, were used to perform two imaging operations on each labelled face. Subsequently, 32 inter-landmark facial measurements were directly measured on the labelled faces and digitally measured on the 3D facial images. Statistical comparisons were made between smartphone-generated 3D facial images (SGI), DA, and 3dMD measurements. RESULTS The SGI measurements were slightly higher than those from DA and 3dMD, but the mean differences between inter-landmark measurements were not statistically significant across all three methods. In terms of clinical acceptability, 16% and 59% of measures showed differences of ≤ 3 mm or ≤ 5º, with good agreement between DA and SGI and 3dMD and SGI, respectively. A small systematic bias of ± 0.2 mm was observed generally among the three methods. Additionally, the mean absolute difference between the DA and SGI methods was the highest for linear measurements (1.31 ± 0.34 mm) and angular measurements (4.11 ± 0.76º). CONCLUSIONS SGI displayed fair trueness compared to DA and 3dMD. It exhibited high accuracy in the orolabial area and specific central and flat areas within the oronasal region. Notwithstanding this, it has limited clinical applicability for assessing the entire oronasal region of patients with CLP. From a clinical application perspective, SGI should accurately encompass the entire oronasal region for optimal clinical use. CLINICAL RELEVANCE SGI can be considered for macroscopic oronasal analysis or for patient education where accuracy within 3 mm and 5º may not be critical.
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Affiliation(s)
- Pradeep Singh
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Richard Tai-Chiu Hsung
- Department of Computer Science, Hong Kong Chu Hai College, Hong Kong SAR, China
- Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Deepal Haresh Ajmera
- Discipline of Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Noha A Said
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Colman McGrath
- Discipline of Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Min Gu
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
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Takeshita WM, Silva TP, de Souza LLT, Tenorio JM. State of the art and prospects for artificial intelligence in orthognathic surgery: A systematic review with meta-analysis. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024; 125:101787. [PMID: 38302057 DOI: 10.1016/j.jormas.2024.101787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 01/19/2024] [Accepted: 01/25/2024] [Indexed: 02/03/2024]
Abstract
OBJECTIVE To present a systematic review of the state of the art regarding clinical applications, main features, and outcomes of artificial intelligence (AI) in orthognathic surgery. METHODS The PICOS strategy was performed on a systematic review (SR) to answer the following question: "What are the state of the art, characteristics and outcomes of applications with artificial intelligence for orthognathic surgery?" After registering in PROSPERO (CRD42021270789) a systematic search was performed in the databases: PubMed (including MedLine), Scopus, Embase, LILACS, MEDLINE EBSCOHOST and Cochrane Library. 195 studies were selected, after screening titles and abstracts, of which thirteen manuscripts were included in the qualitative analysis and six in the quantitative analysis. The treatment effects were plotted in a Forest-plot. JBI questionnaire for observational studies was used to asses the risk of bias. The quality of the SR evidence was assessed using the GRADE tool. RESULTS AI studies on 2D cephalometry for orthognathic surgery, the Tau2 = 0.00, Chi2 = 3.78, p = 1.00 and I² of 0 %, indicating low heterogeneity, AI did not differ statistically from control (p = 0.79). AI studies in the diagnosis of the decision of whether or not to perform orthognathic surgery showed heterogeneity, and therefore meta-analysis was not peformed. CONCLUSION The outcome of AI is similar to the control group, with a low degree of bias, highlighting its potential for use in various applications.
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Affiliation(s)
- Wilton Mitsunari Takeshita
- Department of Diagnosis and Surgery, São Paulo State University (Unesp), School of Dentistry, Araçatuba, 16015-050 Araçatuba, São Paulo, Brazil
| | - Thaísa Pinheiro Silva
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), 13414-903 Piracicaba, Sao Paulo, Brazil.
| | | | - Josceli Maria Tenorio
- Department of Information technology and health, Federal Institute of São Paulo, 01109-010 São Paulo, São Paulo, Brazil
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Stephanian B, Karki S, Debnath K, Saltychev M, Rossi-Meyer M, Kandathil CK, Most SP. Role of Artificial Intelligence and Machine Learning in Facial Aesthetic Surgery: A Systematic Review. Facial Plast Surg Aesthet Med 2024; 26:679-705. [PMID: 39591584 DOI: 10.1089/fpsam.2024.0204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2024] Open
Abstract
Objective: To analyze the quality of artificial intelligence (AI) and machine learning (ML) tools developed for facial aesthetic surgery. Data Sources: Medline, Embase, CINAHL, Central, Scopus, and Web of Science databases were searched in February 2024. Study Selection: All original research in adults undergoing facial aesthetic surgery was included. Pilot reports, case reports, case series (n < 5), conference proceedings, letters (except research letters and brief reports), and editorials were excluded. Main Outcomes and Measures: Facial aesthetic surgery procedures employing AI and ML tools to measure improvements in diagnostic accuracy, predictive outcomes, precision patient counseling, and the scope of facial aesthetic surgery procedures where these tools have been implemented. Results: Out of 494 initial studies, 66 were included in the qualitative analysis. Of these, 42 (63.6%) were of "good" quality, 20 (30.3%) were of "fair" quality, and 4 (6.1%) were of "poor" quality. Conclusion: AI improves diagnostic accuracy, predictive capabilities, patient counseling, and facial aesthetic surgery treatment planning.
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Affiliation(s)
| | - Sabin Karki
- Indiana University School of Medicine, Indianapolis Indiana, USA
| | | | - Mikhail Saltychev
- Department of Physical and Rehabilitation Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Monica Rossi-Meyer
- Division of Facial Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, California, USA
| | - Cherian Kurian Kandathil
- Division of Facial Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, California, USA
| | - Sam P Most
- Division of Facial Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, California, USA
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12
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Foti S, Rickart AJ, Koo B, O' Sullivan E, van de Lande LS, Papaioannou A, Khonsari R, Stoyanov D, Jeelani NUO, Schievano S, Dunaway DJ, Clarkson MJ. Latent disentanglement in mesh variational autoencoders improves the diagnosis of craniofacial syndromes and aids surgical planning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 256:108395. [PMID: 39213899 DOI: 10.1016/j.cmpb.2024.108395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 05/29/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND AND OBJECTIVE The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level. METHODS In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. The model is trained on a dataset of 3D meshes of healthy and syndromic patients which was increased in size with a novel data augmentation technique based on spectral interpolation. Thanks to its semantically meaningful and disentangled latent representation, SD-VAE is used to analyse and generate head shapes while considering the influence of different anatomical sub-units. RESULTS Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to approximate the outcome of a range of craniofacial surgical procedures. CONCLUSION This work opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes. Our code is available at github.com/simofoti/CraniofacialSD-VAE.
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Affiliation(s)
- Simone Foti
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Centre For Medical Image Computing, University College London, London, UK; Imperial College London, Department of Computing, London, UK.
| | - Alexander J Rickart
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Bongjin Koo
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Centre For Medical Image Computing, University College London, London, UK; University of California, Santa Barbara, Department of Electrical & Computer Engineering, Santa Barbara, USA
| | - Eimear O' Sullivan
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK; Imperial College London, Department of Computing, London, UK
| | - Lara S van de Lande
- Department of Oral and Maxillofacial Surgery, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Athanasios Papaioannou
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK; Imperial College London, Department of Computing, London, UK
| | - Roman Khonsari
- Department of Maxillofacial Surgery and Plastic Surgery, Necker - Enfants Malades University Hospital, Paris, France
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Centre For Medical Image Computing, University College London, London, UK
| | - N U Owase Jeelani
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Silvia Schievano
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - David J Dunaway
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Matthew J Clarkson
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Centre For Medical Image Computing, University College London, London, UK
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13
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Chang JS, Ma CY, Ko EWC. Prediction of surgery-first approach orthognathic surgery using deep learning models. Int J Oral Maxillofac Surg 2024; 53:942-949. [PMID: 38821731 DOI: 10.1016/j.ijom.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 04/24/2024] [Accepted: 05/08/2024] [Indexed: 06/02/2024]
Abstract
The surgery-first approach (SFA) orthognathic surgery can be beneficial due to reduced overall treatment time and earlier profile improvement. The objective of this study was to utilize deep learning to predict the treatment modality of SFA or the orthodontics-first approach (OFA) in orthognathic surgery patients and assess its clinical accuracy. A supervised deep learning model using three convolutional neural networks (CNNs) was trained based on lateral cephalograms and occlusal views of 3D dental model scans from 228 skeletal Class III malocclusion patients (114 treated by SFA and 114 by OFA). An ablation study of five groups (lateral cephalogram only, mandible image only, maxilla image only, maxilla and mandible images, and all data combined) was conducted to assess the influence of each input type. The results showed the average validation accuracy, precision, recall, F1 score, and AUROC for the five folds were 0.978, 0.980, 0.980, 0.980, and 0.998 ; the average testing results for the five folds were 0.906, 0.986, 0.828, 0.892, and 0.952. The lateral cephalogram only group had the least accuracy, while the maxilla image only group had the best accuracy. Deep learning provides a novel method for an accelerated workflow, automated assisted decision-making, and personalized treatment planning.
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Affiliation(s)
- J-S Chang
- Graduate Institute of Dental and Craniofacial Science, Chang Gung University, Taoyuan, Taiwan; Department of Craniofacial Orthodontics, Chang Gung Memorial Hospital, Taipei, Taiwan
| | - C-Y Ma
- Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan; Artificial Intelligence Research Center, Chang Gung University, Taoyuan, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - E W-C Ko
- Graduate Institute of Dental and Craniofacial Science, Chang Gung University, Taoyuan, Taiwan; Department of Craniofacial Orthodontics, Chang Gung Memorial Hospital, Taipei, Taiwan; Craniofacial Research Center, Chang Gung Memorial Hospital, Linkou, Taiwan.
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14
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Godden AR, Micha A, Barry PA, Krupa KDC, Pitches CA, Kirby AM, Rusby JE. Preoperative three-dimensional simulation of the breast appearance after wide local excision or level one oncoplastic techniques for breast-conserving treatment does not set unrealistic expectations for aesthetic outcome: One-year follow-up of a randomised controlled trial. J Plast Reconstr Aesthet Surg 2024; 97:230-236. [PMID: 39168032 DOI: 10.1016/j.bjps.2024.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/03/2024] [Accepted: 07/08/2024] [Indexed: 08/23/2024]
Abstract
INTRODUCTION Simulation of aesthetic outcomes of wide local excision and level one oncoplastic breast conserving treatment (BCT) using 3-dimensional surface imaging (3D-SI) prepares women for their aesthetic outcome. It remains unknown whether women's memory of this information at the one-year follow-up matches their perception of reality or affects the quality of life. METHODS With ethical approval, a prospective 3-arm RCT was conducted and it included 3D-simulation, viewing post-operative 2D photographs of other women and standard care. At one-year post-surgery, the participants completed a visual analogue scale (VAS) for the question "How well do you think the information about how your breasts are likely to look after surgery reflects how they actually look today?" and the BCT BREAST-Q module. The Kruskal-Wallis test was used to examine between-group differences at a 5% significance level. RESULTS From 2017 to 2019, 117 women completed the primary endpoint of being informed about the aesthetic outcome via verbal description, photographs or simulation. Seventy-eight (74%) of the 106 women who remained eligible attended the one-year follow-up. The standardised preoperative 3D-SI simulation did not affect the patient's perception of the aesthetic outcome compared to standard care or viewing 2D photographs as measured using the VAS (p = 0.40) or BREAST-Q scores for satisfaction with information (p = 0.76), satisfaction with breasts (p = 0.70), and psychosocial wellbeing domains (p = 0.81). DISCUSSION Viewing their own 3D-SI standardised simulation did not significantly affect how the participants perceived their aesthetic outcome. In addition, it did not alter the patient-reported satisfaction. These results demonstrated that simulation for wide local excision or level one oncoplastic surgery does not set unrealistic expectations of the aesthetic outcome when used in a preoperative setting. SYNOPSIS The use of a non-bespoke three-dimensional simulation of the aesthetic outcome for breast conserving treatment in the preoperative setting does not over-inflate expectations compared to standard care.
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Affiliation(s)
- Amy R Godden
- Royal Marsden Hospital, Downs Road, London SM2 5PT, UK; Institute of Cancer Research, Cotswold Road, London SM2 5NG, UK
| | | | - Peter A Barry
- Royal Marsden Hospital, Downs Road, London SM2 5PT, UK
| | | | | | - Anna M Kirby
- Royal Marsden Hospital, Downs Road, London SM2 5PT, UK; Institute of Cancer Research, Cotswold Road, London SM2 5NG, UK
| | - Jennifer E Rusby
- Royal Marsden Hospital, Downs Road, London SM2 5PT, UK; Royal Marsden Hospital, Fulham Road, London SW3 6 JJ, UK.
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15
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Dong F, Yan J, Zhang X, Zhang Y, Liu D, Pan X, Xue L, Liu Y. Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery. Heliyon 2024; 10:e35742. [PMID: 39170321 PMCID: PMC11336844 DOI: 10.1016/j.heliyon.2024.e35742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 07/27/2024] [Accepted: 08/02/2024] [Indexed: 08/23/2024] Open
Abstract
Application of deep learning (DL) and machine learning (ML) is rapidly increasing in the medical field. DL is gaining significance for medical image analysis, particularly, in oral and maxillofacial surgeries. Owing to the ability to accurately identify and categorize both diseased and normal soft- and hard-tissue structures, DL has high application potential in the diagnosis and treatment of tumors and in orthognathic surgeries. Moreover, DL and ML can be used to develop prediction models that can aid surgeons to assess prognosis by analyzing the patient's medical history, imaging data, and surgical records, develop more effective treatment strategies, select appropriate surgical modalities, and evaluate the risk of postoperative complications. Such prediction models can play a crucial role in the selection of treatment strategies for oral and maxillofacial surgeries. Their practical application can improve the utilization of medical staff, increase the treatment accuracy and efficiency, reduce surgical risks, and provide an enhanced treatment experience to patients. However, DL and ML face limitations, such as data drift, unstable model results, and vulnerable social trust. With the advancement of social concepts and technologies, the use of these models in oral and maxillofacial surgery is anticipated to become more comprehensive and extensive.
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Affiliation(s)
- Fanqiao Dong
- School of Stomatology, China Medical University, Shenyang, China
| | - Jingjing Yan
- Hospital of Stomatology, China Medical University, Shenyang, China
| | - Xiyue Zhang
- School of Stomatology, China Medical University, Shenyang, China
| | - Yikun Zhang
- School of Stomatology, China Medical University, Shenyang, China
| | - Di Liu
- School of Stomatology, China Medical University, Shenyang, China
| | - Xiyun Pan
- School of Stomatology, China Medical University, Shenyang, China
| | - Lei Xue
- School of Stomatology, China Medical University, Shenyang, China
- Hospital of Stomatology, China Medical University, Shenyang, China
| | - Yu Liu
- First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
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16
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Fung E, Patel D, Tatum S. Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery. Curr Opin Otolaryngol Head Neck Surg 2024; 32:257-262. [PMID: 38837245 DOI: 10.1097/moo.0000000000000983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
PURPOSE OF REVIEW To provide a current review of artificial intelligence and its subtypes in maxillofacial and facial plastic surgery including a discussion of implications and ethical concerns. RECENT FINDINGS Artificial intelligence has gained popularity in recent years due to technological advancements. The current literature has begun to explore the use of artificial intelligence in various medical fields, but there is limited contribution to maxillofacial and facial plastic surgery due to the wide variance in anatomical facial features as well as subjective influences. In this review article, we found artificial intelligence's roles, so far, are to automatically update patient records, produce 3D models for preoperative planning, perform cephalometric analyses, and provide diagnostic evaluation of oropharyngeal malignancies. SUMMARY Artificial intelligence has solidified a role in maxillofacial and facial plastic surgery within the past few years. As high-quality databases expand with more patients, the role for artificial intelligence to assist in more complicated and unique cases becomes apparent. Despite its potential, ethical questions have been raised that should be noted as artificial intelligence continues to thrive. These questions include concerns such as compromise of the physician-patient relationship and healthcare justice.
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Affiliation(s)
| | | | - Sherard Tatum
- Department of Otolaryngology
- Department of Pediatrics, SUNY Upstate Medical University, Syracuse, New York, USA
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17
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Bouhouita-Guermech S, Haidar H. Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context. Asian Bioeth Rev 2024; 16:315-344. [PMID: 39022380 PMCID: PMC11250714 DOI: 10.1007/s41649-024-00292-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 07/20/2024] Open
Abstract
The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to "responsibility" and "AI in healthcare", and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders' responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle.
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Affiliation(s)
| | - Hazar Haidar
- Ethics Programs, Department of Letters and Humanities, University of Quebec at Rimouski, Rimouski, Québec Canada
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18
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Zhu C, Attaluri PK, Wirth PJ, Shaffrey EC, Friedrich JB, Rao VK. Current Applications of Artificial Intelligence in Billing Practices and Clinical Plastic Surgery. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2024; 12:e5939. [PMID: 38957712 PMCID: PMC11216662 DOI: 10.1097/gox.0000000000005939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 05/10/2024] [Indexed: 07/04/2024]
Abstract
Integration of artificial intelligence (AI), specifically with natural language processing and machine learning, holds tremendous potential to enhance both clinical practices and administrative workflows within plastic surgery. AI has been applied to various aspects of patient care in plastic surgery, including postoperative free flap monitoring, evaluating preoperative risk assessments, and analyzing clinical documentation. Previous studies have demonstrated the ability to interpret current procedural terminology codes from clinical documentation using natural language processing. Various automated medical billing companies have used AI to improve the revenue management cycle at hospitals nationwide. Additionally, AI has been piloted by insurance companies to streamline the prior authorization process. AI implementation holds potential to enhance billing practices and maximize healthcare revenue for practicing physicians.
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Affiliation(s)
- Christina Zhu
- From the Division of Plastic and Reconstructive Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wis
- Texas Tech University Health Sciences Center School of Medicine, Lubbock, Tex
| | - Pradeep K Attaluri
- From the Division of Plastic and Reconstructive Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wis
| | - Peter J Wirth
- From the Division of Plastic and Reconstructive Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wis
| | - Ellen C Shaffrey
- From the Division of Plastic and Reconstructive Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wis
| | | | - Venkat K Rao
- From the Division of Plastic and Reconstructive Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wis
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19
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Bao J, Zhang X, Xiang S, Liu H, Cheng M, Yang Y, Huang X, Xiang W, Cui W, Lai HC, Huang S, Wang Y, Qian D, Yu H. Deep Learning-Based Facial and Skeletal Transformations for Surgical Planning. J Dent Res 2024; 103:809-819. [PMID: 38808566 DOI: 10.1177/00220345241253186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024] Open
Abstract
The increasing application of virtual surgical planning (VSP) in orthognathic surgery implies a critical need for accurate prediction of facial and skeletal shapes. The craniofacial relationship in patients with dentofacial deformities is still not understood, and transformations between facial and skeletal shapes remain a challenging task due to intricate anatomical structures and nonlinear relationships between the facial soft tissue and bones. In this study, a novel bidirectional 3-dimensional (3D) deep learning framework, named P2P-ConvGC, was developed and validated based on a large-scale data set for accurate subject-specific transformations between facial and skeletal shapes. Specifically, the 2-stage point-sampling strategy was used to generate multiple nonoverlapping point subsets to represent high-resolution facial and skeletal shapes. Facial and skeletal point subsets were separately input into the prediction system to predict the corresponding skeletal and facial point subsets via the skeletal prediction subnetwork and facial prediction subnetwork. For quantitative evaluation, the accuracy was calculated with shape errors and landmark errors between the predicted skeleton or face with corresponding ground truths. The shape error was calculated by comparing the predicted point sets with the ground truths, with P2P-ConvGC outperforming existing state-of-the-art algorithms including P2P-Net, P2P-ASNL, and P2P-Conv. The total landmark errors (Euclidean distances of craniomaxillofacial landmarks) of P2P-ConvGC in the upper skull, mandible, and facial soft tissues were 1.964 ± 0.904 mm, 2.398 ± 1.174 mm, and 2.226 ± 0.774 mm, respectively. Furthermore, the clinical feasibility of the bidirectional model was validated using a clinical cohort. The result demonstrated its prediction ability with average surface deviation errors of 0.895 ± 0.175 mm for facial prediction and 0.906 ± 0.082 mm for skeletal prediction. To conclude, our proposed model achieved good performance on the subject-specific prediction of facial and skeletal shapes and showed clinical application potential in postoperative facial prediction and VSP for orthognathic surgery.
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Affiliation(s)
- J Bao
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - X Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - S Xiang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - H Liu
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - M Cheng
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - Y Yang
- Shanghai Lanhui Medical Technology Co., Ltd, Shanghai, China
| | - X Huang
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - W Xiang
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - W Cui
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - H C Lai
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - S Huang
- Department of Oral and Maxillofacial Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Y Wang
- Qingdao Stomatological Hospital Affiliated to Qingdao University, Qingdao, Shandong, China
| | - D Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - H Yu
- Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
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Surendran A, Daigavane P, Shrivastav S, Kamble R, Sanchla AD, Bharti L, Shinde M. The Future of Orthodontics: Deep Learning Technologies. Cureus 2024; 16:e62045. [PMID: 38989357 PMCID: PMC11234326 DOI: 10.7759/cureus.62045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 06/09/2024] [Indexed: 07/12/2024] Open
Abstract
Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, the field of dentistry has widely adopted information technology (IT), resulting in several benefits, including decreased expenses, increased efficiency, decreased need for human expertise, and reduced errors. The transition from preset rules to learning from real-world examples, particularly machine learning (ML) and artificial intelligence (AI), has greatly benefited the organization, analysis, and storage of medical data. Deep learning, a type of AI, enables robots to mimic human neural networks, allowing them to learn and make decisions independently without the need for explicit programming. Its ability to automate cephalometric analysis and enhance diagnosis through 3D imaging has revolutionized orthodontic operations. Deep learning models have the potential to significantly improve treatment outcomes and reduce human errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, the use of 3D imaging technologies such as cone-beam computed tomography (CBCT) can facilitate precise treatment planning, allowing for comprehensive examinations of craniofacial architecture, tooth movements, and airway dimensions. In today's era of personalized medicine, deep learning's ability to customize treatments for individual patients has propelled the field of orthodontics forward tremendously. However, it is essential to address issues related to data privacy, model interpretability, and ethical considerations before orthodontic practices can use deep learning in an ethical and responsible manner. Modern orthodontics is evolving, thanks to the ability of deep learning to deliver more accurate, effective, and personalized orthodontic treatments, improving patient care as technology develops.
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Affiliation(s)
- Aathira Surendran
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Pallavi Daigavane
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Sunita Shrivastav
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Ranjit Kamble
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Abhishek D Sanchla
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Lovely Bharti
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Mrudula Shinde
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
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21
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Grillo R, Balel Y, Reis BAQ, Stanbouly D, Samieirad S, Melhem-Elias F. The online attention analysis on orthognathic surgery research. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024; 125:101826. [PMID: 38484842 DOI: 10.1016/j.jormas.2024.101826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/01/2024] [Accepted: 03/11/2024] [Indexed: 03/19/2024]
Abstract
OBJECTIVES Altmetrics is one of the fields of bibliometrics that seeks to assess the impact and interest of a given subject through Internet users. The aim of this study is to make an altmetric analysis of the orthognathic surgery literature. METHODS A literature search was conducted using Dimensions app up to December 2023. A list of the 100 most mentioned articles on the topic was compiled. A Google Trends search was performed with same strategy to visualize important data regarding internet search. Charts and tables were created using Microsoft Excel and VOSviewer software to allow bibliometric visualization. RESULTS There was a very poor correlation between the number of mentions and the number of citations (r = 0.0202). Most articles discussed on technical innovations associated to orthognathic surgery, majority related to virtual planning (n = 26). Other topics considered interesting to internet readers were complications (n = 18), surgical technique (n = 14), and psychological aspects/quality of life (n = 13). CONCLUSION Online interest in orthognathic surgery closely aligns with the level of academic interest but is also influenced by factors such as location and economic status. The internet is a powerful tool for disseminating scientific research to a broad audience, making it more accessible and engaging than traditional academic channels.
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Affiliation(s)
- Ricardo Grillo
- Department of Oral & Maxillofacial Surgery, School of Dentistry of the University of São Paulo, São Paulo-SP, Brazil; Department of Oral & Maxillofacial Surgery, Faculdade Patos de Minas, Brasília-DF, Brazil.
| | - Yunus Balel
- Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Tokat Gaziosmanpaşa University, Tokat, Turkey
| | | | - Dani Stanbouly
- Columbia University College of Dental Medicine, New York, NY, USA
| | - Sahand Samieirad
- Department of Oral & Maxillofacial surgery, Mashhad dental school, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fernando Melhem-Elias
- Department of Oral & Maxillofacial Surgery, School of Dentistry of the University of São Paulo, São Paulo-SP, Brazil; Private Practice in Oral and Maxillofacial Surgery, São Paulo-SP, Brazil
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22
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Olejnik A, Verstraete L, Croonenborghs TM, Politis C, Swennen GRJ. The Accuracy of Three-Dimensional Soft Tissue Simulation in Orthognathic Surgery-A Systematic Review. J Imaging 2024; 10:119. [PMID: 38786573 PMCID: PMC11122049 DOI: 10.3390/jimaging10050119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 04/26/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Three-dimensional soft tissue simulation has become a popular tool in the process of virtual orthognathic surgery planning and patient-surgeon communication. To apply 3D soft tissue simulation software in routine clinical practice, both qualitative and quantitative validation of its accuracy are required. The objective of this study was to systematically review the literature on the accuracy of 3D soft tissue simulation in orthognathic surgery. The Web of Science, PubMed, Cochrane, and Embase databases were consulted for the literature search. The systematic review (SR) was conducted according to the PRISMA statement, and 40 articles fulfilled the inclusion and exclusion criteria. The Quadas-2 tool was used for the risk of bias assessment for selected studies. A mean error varying from 0.27 mm to 2.9 mm for 3D soft tissue simulations for the whole face was reported. In the studies evaluating 3D soft tissue simulation accuracy after a Le Fort I osteotomy only, the upper lip and paranasal regions were reported to have the largest error, while after an isolated bilateral sagittal split osteotomy, the largest error was reported for the lower lip and chin regions. In the studies evaluating simulation after bimaxillary osteotomy with or without genioplasty, the highest inaccuracy was reported at the level of the lips, predominantly the lower lip, chin, and, sometimes, the paranasal regions. Due to the variability in the study designs and analysis methods, a direct comparison was not possible. Therefore, based on the results of this SR, guidelines to systematize the workflow for evaluating the accuracy of 3D soft tissue simulations in orthognathic surgery in future studies are proposed.
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Affiliation(s)
- Anna Olejnik
- Division of Maxillofacial Surgery, Department of Surgery, AZ Sint-Jan, Ruddershove 10, 8000 Bruges, Belgium
- Maxillofacial Surgery Unit, Department of Head and Neck Surgery, Craniomaxillofacial Center for Children and Young Adults, Regional Specialized Children’s Hospital, ul. Zolnierska 18A, 10-561 Olsztyn, Poland
| | - Laurence Verstraete
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Tomas-Marijn Croonenborghs
- Division of Maxillofacial Surgery, Department of Surgery, AZ Sint-Jan, Ruddershove 10, 8000 Bruges, Belgium
| | - Constantinus Politis
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Gwen R. J. Swennen
- Division of Maxillofacial Surgery, Department of Surgery, AZ Sint-Jan, Ruddershove 10, 8000 Bruges, Belgium
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23
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Li R, Shu F, Zhen Y, Song Z, An Y, Jiang Y. Artificial Intelligence for Rhinoplasty Design in Asian Patients. Aesthetic Plast Surg 2024; 48:1557-1564. [PMID: 37580565 DOI: 10.1007/s00266-023-03534-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 07/17/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND Rhinoplasty is one of the most challenging plastic surgeries because it lacks a uniform standard for preoperative design or implementation. For a long time, rhinoplasties were done without an accurate consensus of aesthetic design between surgeons and patients before surgery and consequently brought unsatisfactory appearance for patients. In recent years, three-dimensional (3D) simulation has been used to visualize the preoperative design of rhinoplasty, and good results have been achieved. However, it still relied on individual aesthetics and experience. The preoperative design remained a huge challenge for inexperienced surgeons and could be time-consuming to perform manually. Therefore, we adopted artificial intelligence (AI) in this work to provide a new idea for automated and efficient preoperative nasal contour design. METHODS We collected a dataset of 3D facial images from 209 patients. For each patient, both the original face and the manually designed face using 3D simulation software were included. The 3D images were transformed into point clouds, based on which we used the modified FoldingNet model for deep neural network training (by pytorch 1.12). RESULTS The trained AI model gained the ability to perform aesthetic design automatically and achieved similar results to manual design. We analysed the 1027 facial features captured by the AI model and concluded two of its possible cognitive modes. One is to resemble the human aesthetic considerations while the other is to fulfil the given task in a special way of the machine. CONCLUSION We presented the first AI model for automated preoperative 3D simulation of rhinoplasty in this study. It provided a new idea for the automated, individual and efficient preoperative design, which was expected to bring a new paradigm for rhinoplasty and even the whole field of plastic surgery. LEVEL OF EVIDENCE IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
- Ruoyu Li
- Department of Plastic Surgery, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Fan Shu
- Department of Plastic Surgery, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Yonghuan Zhen
- Department of Plastic Surgery, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China
| | - Zhexiang Song
- Department of Physics, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China
| | - Yang An
- Department of Plastic Surgery, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China.
| | - Yin Jiang
- Department of Physics, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China.
- Beihang Hangzhou Innovation Institute, Yuhang, Hangzhou, 310023, China.
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Singh P, Bornstein MM, Hsung RTC, Ajmera DH, Leung YY, Gu M. Frontiers in Three-Dimensional Surface Imaging Systems for 3D Face Acquisition in Craniofacial Research and Practice: An Updated Literature Review. Diagnostics (Basel) 2024; 14:423. [PMID: 38396462 PMCID: PMC10888365 DOI: 10.3390/diagnostics14040423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/02/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Digitalizing all aspects of dental care is a contemporary approach to ensuring the best possible clinical outcomes. Ongoing advancements in 3D face acquisition have been driven by continuous research on craniofacial structures and treatment effects. An array of 3D surface-imaging systems are currently available for generating photorealistic 3D facial images. However, choosing a purpose-specific system is challenging for clinicians due to variations in accuracy, reliability, resolution, and portability. Therefore, this review aims to provide clinicians and researchers with an overview of currently used or potential 3D surface imaging technologies and systems for 3D face acquisition in craniofacial research and daily practice. Through a comprehensive literature search, 71 articles meeting the inclusion criteria were included in the qualitative analysis, investigating the hardware, software, and operational aspects of these systems. The review offers updated information on 3D surface imaging technologies and systems to guide clinicians in selecting an optimal 3D face acquisition system. While some of these systems have already been implemented in clinical settings, others hold promise. Furthermore, driven by technological advances, novel devices will become cost-effective and portable, and will also enable accurate quantitative assessments, rapid treatment simulations, and improved outcomes.
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Affiliation(s)
- Pradeep Singh
- Discipline of Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (P.S.); (D.H.A.)
| | - Michael M. Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Mattenstrasse 40, 4058 Basel, Switzerland;
| | - Richard Tai-Chiu Hsung
- Department of Computer Science, Hong Kong Chu Hai College, Hong Kong SAR, China;
- Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Deepal Haresh Ajmera
- Discipline of Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (P.S.); (D.H.A.)
| | - Yiu Yan Leung
- Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Min Gu
- Discipline of Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (P.S.); (D.H.A.)
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Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Nożewski J, Janiszewska-Olszowska J. AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review. J Clin Med 2024; 13:344. [PMID: 38256478 PMCID: PMC10816993 DOI: 10.3390/jcm13020344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.
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Affiliation(s)
- Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Wojciech Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Paweł Nowicki
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Jakub Nożewski
- Department of Emeregncy Medicine, University Hospital No 2 in Bydgoszcz, Ujejskiego 75, 85-168 Bydgoszcz, Poland
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Yang X, Huang K, Yang D, Zhao W, Zhou X. Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review. GLOBAL CHALLENGES (HOBOKEN, NJ) 2024; 8:2300163. [PMID: 38223896 PMCID: PMC10784210 DOI: 10.1002/gch2.202300163] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/20/2023] [Indexed: 01/16/2024]
Abstract
The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.
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Affiliation(s)
- Xue Yang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Kexin Huang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Dewei Yang
- College of Advanced Manufacturing EngineeringChongqing University of Posts and TelecommunicationsChongqingChongqing400000China
| | - Weiling Zhao
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
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27
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Singh P, Hsung RTC, Ajmera DH, Leung YY, McGrath C, Gu M. Can smartphones be used for routine dental clinical application? A validation study for using smartphone-generated 3D facial images. J Dent 2023; 139:104775. [PMID: 37944629 DOI: 10.1016/j.jdent.2023.104775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/01/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023] Open
Abstract
OBJECTIVES To compare the accuracy of smartphone-generated three-dimensional (3D) facial images to that of direct anthropometry (DA) and 3dMD with the aim of assessing the validity and reliability of smartphone-generated 3D facial images for routine clinical applications. MATERIALS AND METHODS Twenty-five anthropometric soft-tissue facial landmarks were labelled manually on 22 orthognathic surgery patients (11 males and 11 females; mean age 26.2 ± 5.3 years). For each labelled face, two imaging operations were performed using two different surface imaging systems: 3dMDface and Bellus3D FaceApp. Next, 42 inter-landmark facial measurements amongst the identified facial landmarks were measured directly on each labelled face and also digitally on 3D facial images. The measurements obtained from smartphone-generated 3D facial images (SGI) were statistically compared with those from DA and 3dMD. RESULTS SGI had slightly higher measurement values than DA and 3dMD, but there was no statistically significant difference between the mean values of inter-landmark measures across the three methods. Clinically acceptable differences (≤3 mm or ≤5°) were observed for 67 % and 74 % of measurements with good agreement between DA and SGI, and 3dMD and SGI, respectively. An overall small systematic bias of ± 0.2 mm was observed between the three methods. Furthermore, the mean absolute difference between DA and SGI methods was highest for linear (1.41 ± 0.33 mm) as well as angular measurements (3.07 ± 0.73°). CONCLUSIONS SGI demonstrated fair trueness compared to DA and 3dMD. The central region and flat areas of the face in SGI are more accurate. Despite this, SGI have limited clinical application, and the panfacial accuracy of the SGI would be more desirable from a clinical application standpoint. CLINICAL SIGNIFICANCE The usage of SGI in clinical practice for region-specific macro-proportional facial assessment involving central and flat regions of the face or for patient education purposes, which does not require accuracy within 3 mm and 5° can be considered.
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Affiliation(s)
- Pradeep Singh
- Discipline of Orthodontics, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Richard Tai-Chiu Hsung
- Department of Computer Science, Hong Kong Chu Hai College, Hong Kong SAR, China; Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Deepal Haresh Ajmera
- Discipline of Orthodontics, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Colman McGrath
- Discipline of Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Min Gu
- Discipline of Orthodontics, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China.
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Jacob J, Bozkurt S. Automated surgical planning in spring-assisted sagittal craniosynostosis correction using finite element analysis and machine learning. PLoS One 2023; 18:e0294879. [PMID: 38015830 PMCID: PMC10684009 DOI: 10.1371/journal.pone.0294879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
Sagittal synostosis is a condition caused by the fused sagittal suture and results in a narrowed skull in infants. Spring-assisted cranioplasty is a correction technique used to expand skulls with sagittal craniosynostosis by placing compressed springs on the skull before six months of age. Proposed methods for surgical planning in spring-assisted sagittal craniosynostosis correction provide information only about the skull anatomy or require iterative finite element simulations. Therefore, the selection of surgical parameters such as spring dimensions and osteotomy sizes may remain unclear and spring-assisted cranioplasty may yield sub-optimal surgical results. The aim of this study is to develop the architectural structure of an automated tool to predict post-operative surgical outcomes in sagittal craniosynostosis correction with spring-assisted cranioplasty using machine learning and finite element analyses. Six different machine learning algorithms were tested using a finite element model which simulated a combination of various mechanical and geometric properties of the calvarium, osteotomy sizes, spring characteristics, and spring implantation positions. Also, a statistical shape model representing an average sagittal craniosynostosis calvarium in 5-month-old patients was used to assess the machine learning algorithms. XGBoost algorithm predicted post-operative cephalic index in spring-assisted sagittal craniosynostosis correction with high accuracy. Finite element simulations confirmed the prediction of the XGBoost algorithm. The presented architectural structure can be used to develop a tool to predict the post-operative cephalic index in spring-assisted cranioplasty in patients with sagittal craniosynostosis can be used to automate surgical planning and improve post-operative surgical outcomes in spring-assisted cranioplasty.
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Affiliation(s)
- Jenson Jacob
- Ulster University, School of Engineering, Belfast, United Kingdom
| | - Selim Bozkurt
- Ulster University, School of Engineering, Belfast, United Kingdom
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Nachmani O, Saun T, Huynh M, Forrest CR, McRae M. "Facekit"-Toward an Automated Facial Analysis App Using a Machine Learning-Derived Facial Recognition Algorithm. Plast Surg (Oakv) 2023; 31:321-329. [PMID: 37915352 PMCID: PMC10617451 DOI: 10.1177/22925503211073843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/14/2021] [Accepted: 11/22/2021] [Indexed: 11/03/2023] Open
Abstract
Introduction: Multiple tools have been developed for facial feature measurements and analysis using facial recognition machine learning techniques. However, several challenges remain before these will be useful in the clinical context for reconstructive and aesthetic plastic surgery. Smartphone-based applications utilizing open-access machine learning tools can be rapidly developed, deployed, and tested for use in clinical settings. This research compares a smartphone-based facial recognition algorithm to direct and digital measurement performance for use in facial analysis. Methods: Facekit is a camera application developed for Android that utilizes ML Kit, an open-access computer vision Application Programing Interface developed by Google. Using the facial landmark module, we measured 4 facial proportions in 15 healthy subjects and compared them to direct surface and digital measurements using intraclass correlation (ICC) and Pearson correlation. Results: Measurement of the naso-facial proportion achieved the highest ICC of 0.321, where ICC > 0.75 is considered an excellent agreement between methods. Repeated measures analysis of variance of proportion measurements between ML Kit, direct and digital methods, were significantly different (F[2,14] = 6-26, P<<.05). Facekit measurements of orbital, orbitonasal, naso-oral, and naso-facial ratios had overall low correlation and agreement to both direct and digital measurements (R<<0.5, ICC<<0.75). Conclusion: Facekit is a smartphone camera application for rapid facial feature analysis. Agreement between Facekit's machine learning measurements and direct and digital measurements was low. We conclude that the chosen pretrained facial recognition software is not accurate enough for conducting a clinically useful facial analysis. Custom models trained on accurate and clinically relevant landmarks may provide better performance.
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Affiliation(s)
- Omri Nachmani
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Tomas Saun
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Minh Huynh
- Division of Plastic and Reconstructive Surgery, McMaster University, Hamilton, Ontario, Canada
| | | | - Mark McRae
- Division of Plastic and Reconstructive Surgery, McMaster University, Hamilton, Ontario, Canada
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Liu J, Zhang C, Shan Z. Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives. Healthcare (Basel) 2023; 11:2760. [PMID: 37893833 PMCID: PMC10606213 DOI: 10.3390/healthcare11202760] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.
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Affiliation(s)
- Junqi Liu
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Chengfei Zhang
- Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Zhiyi Shan
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
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Bonny T, Al Nassan W, Obaideen K, Al Mallahi MN, Mohammad Y, El-damanhoury HM. Contemporary Role and Applications of Artificial Intelligence in Dentistry. F1000Res 2023; 12:1179. [PMID: 37942018 PMCID: PMC10630586 DOI: 10.12688/f1000research.140204.1] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 11/10/2023] Open
Abstract
Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.
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Affiliation(s)
- Talal Bonny
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Wafaa Al Nassan
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Khaled Obaideen
- Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Maryam Nooman Al Mallahi
- Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain City, Abu Dhabi, 27272, United Arab Emirates
| | - Yara Mohammad
- College of Engineering and Information Technology, Ajman University, Ajman University, Ajman, Ajman, United Arab Emirates
| | - Hatem M. El-damanhoury
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates
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关 舒, 刘 殿, 张 庆. [Pediatric oral maxillofacial management and artificial intelligence]. LIN CHUANG ER BI YAN HOU TOU JING WAI KE ZA ZHI = JOURNAL OF CLINICAL OTORHINOLARYNGOLOGY, HEAD, AND NECK SURGERY 2023; 37:658-661. [PMID: 37551576 PMCID: PMC10645521 DOI: 10.13201/j.issn.2096-7993.2023.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Indexed: 08/09/2023]
Abstract
With the enhancement of aesthetic awareness of children's oral maxillofacial development, multi-disciplinary doctors pay attention to children's oral maxillofacial management. Artificial intelligence (AI) technology has been gradually applied to all fields of children's oral maxillofacial management because of its outstanding advantages in medical screening and auxiliary decision-making. This article reviews the application of AI technology in the screening, diagnosis, treatment and follow-up of oral maxillofacial management in children.
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Affiliation(s)
- 舒文 关
- 深圳大学总医院 深圳大学临床医学科学院 耳鼻咽喉头颈外科(广东深圳，518055)Department of Otorhinolaryngology Head and Neck Surgery, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, 518055, China
| | - 殿全 刘
- 深圳大学总医院 深圳大学临床医学科学院 耳鼻咽喉头颈外科(广东深圳，518055)Department of Otorhinolaryngology Head and Neck Surgery, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, 518055, China
| | - 庆丰 张
- 深圳大学总医院 深圳大学临床医学科学院 耳鼻咽喉头颈外科(广东深圳，518055)Department of Otorhinolaryngology Head and Neck Surgery, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, 518055, China
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Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning. Clin Oral Investig 2023; 27:897-906. [PMID: 36323803 DOI: 10.1007/s00784-022-04706-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 08/29/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES The aim of this review was to investigate the application of artificial intelligence (AI) in maxillofacial computer-assisted surgical planning (CASP) workflows with the discussion of limitations and possible future directions. MATERIALS AND METHODS An in-depth search of the literature was undertaken to review articles concerned with the application of AI for segmentation, multimodal image registration, virtual surgical planning (VSP), and three-dimensional (3D) printing steps of the maxillofacial CASP workflows. RESULTS The existing AI models were trained to address individual steps of CASP, and no single intelligent workflow was found encompassing all steps of the planning process. Segmentation of dentomaxillofacial tissue from computed tomography (CT)/cone-beam CT imaging was the most commonly explored area which could be applicable in a clinical setting. Nevertheless, a lack of generalizability was the main issue, as the majority of models were trained with the data derived from a single device and imaging protocol which might not offer similar performance when considering other devices. In relation to registration, VSP and 3D printing, the presence of inadequate heterogeneous data limits the automatization of these tasks. CONCLUSION The synergy between AI and CASP workflows has the potential to improve the planning precision and efficacy. However, there is a need for future studies with big data before the emergent technology finds application in a real clinical setting. CLINICAL RELEVANCE The implementation of AI models in maxillofacial CASP workflows could minimize a surgeon's workload and increase efficiency and consistency of the planning process, meanwhile enhancing the patient-specific predictability.
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A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future. Plast Reconstr Surg Glob Open 2022; 10:e4608. [PMID: 36479133 PMCID: PMC9722565 DOI: 10.1097/gox.0000000000004608] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 08/24/2022] [Indexed: 01/25/2023]
Abstract
UNLABELLED Artificial intelligence (AI) is presently employed in several medical specialties, particularly those that rely on large quantities of standardized data. The integration of AI in surgical subspecialties is under preclinical investigation but is yet to be widely implemented. Plastic surgeons collect standardized data in various settings and could benefit from AI. This systematic review investigates the current clinical applications of AI in plastic and reconstructive surgery. METHODS A comprehensive literature search of the Medline, EMBASE, Cochrane, and PubMed databases was conducted for AI studies with multiple search terms. Articles that progressed beyond the title and abstract screening were then subcategorized based on the plastic surgery subspecialty and AI application. RESULTS The systematic search yielded a total of 1820 articles. Forty-four studies met inclusion criteria warranting further analysis. Subcategorization of articles by plastic surgery subspecialties revealed that most studies fell into aesthetic and breast surgery (27%), craniofacial surgery (23%), or microsurgery (14%). Analysis of the research study phase of included articles indicated that the current research is primarily in phase 0 (discovery and invention; 43.2%), phase 1 (technical performance and safety; 27.3%), or phase 2 (efficacy, quality improvement, and algorithm performance in a medical setting; 27.3%). Only one study demonstrated translation to clinical practice. CONCLUSIONS The potential of AI to optimize clinical efficiency is being investigated in every subfield of plastic surgery, but much of the research to date remains in the preclinical status. Future implementation of AI into everyday clinical practice will require collaborative efforts.
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Facial Scanners in Dentistry: An Overview. PROSTHESIS 2022. [DOI: 10.3390/prosthesis4040053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Purpose: This narrative review aims to explore the current status of facial scanning technology in the dental field; outlining the history, mechanisms, and current evidence regarding its use and limitations within digital dentistry. Methods: Subtopics within facial scanner technology in dentistry were identified and divided among four reviewers. Electronic searches of the Medline (PubMed) database were performed with the following search terms: facial scanner, dentistry, prosthodontics, virtual patient, sleep apnea, maxillofacial prosthetics, accuracy. For this review only studies or review papers evaluating facial scanning technology for dental or medical applications were included. A total of 44 articles were included. Due to the narrative nature of this review, no formal evidence-based quality assessment was performed and the search was limited to the English language. No further restrictions were applied. Results: The significance, applications, limitations, and future directions of facial scanning technology were reviewed. Specific subtopics include significant history of facial scanner use and development for dentistry, different types and mechanisms used in facial scanning technology, accuracy of scanning technology, use as a diagnostic tool, use in creating a virtual patient, virtual articulation, smile design, diagnosing and treating obstructive sleep apnea, limitations of scanning technology, and future directions with artificial intelligence. Conclusions: Despite limitations in scan quality and software operation, 3D facial scanners are rapid and non-invasive tools that can be utilized in multiple facets of dental care. Facial scanners can serve an invaluable role in the digital workflow by capturing facial records to facilitate interdisciplinary communication, virtual articulation, smile design, and obstructive sleep apnea diagnosis and treatment. Looking into the future, facial scanning technology has promising applications in the fields of craniofacial research, and prosthodontic diagnosis and treatment planning.
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van Riet TC, Klop C, Becking AG, Nolte JW. Management of Asymmetry. Oral Maxillofac Surg Clin North Am 2022; 35:11-21. [DOI: 10.1016/j.coms.2022.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Mohaideen K, Negi A, Verma DK, Kumar N, Sennimalai K, Negi A. Applications of artificial intelligence and machine learning in orthognathic surgery: A scoping review. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2022; 123:e962-e972. [PMID: 35803558 DOI: 10.1016/j.jormas.2022.06.027] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/24/2022] [Accepted: 06/30/2022] [Indexed: 11/28/2022]
Abstract
Over the recent years, Artificial Intelligence (AI) has been progressing rapidly with its ability to mimic human cognitive functions. The potential applications of AI technology in diagnosis, treatment planning, and prognosis prediction have been demonstrated in various studies. The present scoping review aimed to provide an overview of AI and Machine Learning (ML) algorithms and their applications in orthognathic surgery. A comprehensive search was conducted in databases including PubMed, Embase, Scopus, Web of Science and OVID Medline until November 2021. This scoping review was conducted following the PRISMA-ScR guidelines. After applying the inclusion and exclusion criteria, a total of 19 studies were included for final review. AI has profoundly impacted the diagnosis and prediction of orthognathic surgeries with a clinically acceptable accuracy range. Furthermore, AI reduces the work burden of the clinician by eliminating the tedious registration procedures, thereby helping in efficient and automated planning. However, focussing on the research gaps, there is a need to foster the AI models/algorithms to contemporize their efficiency in clinical decision making, diagnosis and surgical planning in future studies.
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Affiliation(s)
- Kaja Mohaideen
- Department of Dentistry, AIIMS Bilaspur, Himachal Pradesh, India
| | - Anurag Negi
- Department of Dentistry, AIIMS Bilaspur, Himachal Pradesh, India.
| | | | - Neeraj Kumar
- Department of Dentistry, AIIMS Bilaspur, Himachal Pradesh, India
| | | | - Amita Negi
- Medical Officer (Dental) Bilaspur, Himachal Pradesh, India
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Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review. BMC Bioinformatics 2022; 23:431. [PMID: 36253726 PMCID: PMC9575250 DOI: 10.1186/s12859-022-04979-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 10/04/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Predicting morphological changes to anatomical structures from 3D shapes such as blood vessels or appearance of the face is a growing interest to clinicians. Machine learning (ML) has had great success driving predictions in 2D, however, methods suitable for 3D shapes are unclear and the use cases unknown. OBJECTIVE AND METHODS This systematic review aims to identify the clinical implementation of 3D shape prediction and ML workflows. Ovid-MEDLINE, Embase, Scopus and Web of Science were searched until 28th March 2022. RESULTS 13,754 articles were identified, with 12 studies meeting final inclusion criteria. These studies involved prediction of the face, head, aorta, forearm, and breast, with most aiming to visualize shape changes after surgical interventions. ML algorithms identified were regressions (67%), artificial neural networks (25%), and principal component analysis (8%). Meta-analysis was not feasible due to the heterogeneity of the outcomes. CONCLUSION 3D shape prediction is a nascent but growing area of research in medicine. This review revealed the feasibility of predicting 3D shapes using ML clinically, which could play an important role for clinician-patient visualization and communication. However, all studies were early phase and there were inconsistent language and reporting. Future work could develop guidelines for publication and promote open sharing of source code.
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Response to: "One step forward: How do you measure accuracy?". Plast Reconstr Surg 2022; 150:485e-487e. [PMID: 35724415 DOI: 10.1097/prs.0000000000009308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Lahoud P, Jacobs R, Boisse P, EzEldeen M, Ducret M, Richert R. Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice. Clin Oral Investig 2022; 26:5117-5128. [PMID: 35687196 DOI: 10.1007/s00784-022-04572-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 05/30/2022] [Indexed: 12/25/2022]
Abstract
The dental practice has largely evolved in the last 50 years following a better understanding of the biomechanical behaviour of teeth and its supporting structures, as well as developments in the fields of imaging and biomaterials. However, many patients still encounter treatment failures; this is related to the complex nature of evaluating the biomechanical aspects of each clinical situation due to the numerous patient-specific parameters, such as occlusion and root anatomy. In parallel, the advent of cone beam computed tomography enabled researchers in the field of odontology as well as clinicians to gather and model patient data with sufficient accuracy using image processing and finite element technologies. These developments gave rise to a new precision medicine concept that proposes to individually assess anatomical and biomechanical characteristics and adapt treatment options accordingly. While this approach is already applied in maxillofacial surgery, its implementation in dentistry is still restricted. However, recent advancements in artificial intelligence make it possible to automate several parts of the laborious modelling task, bringing such user-assisted decision-support tools closer to both clinicians and researchers. Therefore, the present narrative review aimed to present and discuss the current literature investigating patient-specific modelling in dentistry, its state-of-the-art applications, and research perspectives.
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Affiliation(s)
- Pierre Lahoud
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU, Leuven, Belgium.,Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.,Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Leuven, Belgium
| | - Reinhilde Jacobs
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU, Leuven, Belgium.,Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.,Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Philippe Boisse
- Laboratoire de Mécanique Des Contacts Et Structures, UMR 5259, CNRS/INSA, Villeurbanne, France
| | - Mostafa EzEldeen
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU, Leuven, Belgium.,Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.,Department of Oral Health Sciences, KU Leuven and Paediatric Dentistry and Special Dental Care, University Hospitals Leuven, Leuven, Belgium
| | - Maxime Ducret
- Hospices Civils de Lyon, PAM d'Odontologie, Lyon, France.,Faculty of Odontology, Lyon 1 University, Lyon, France.,Laboratoire de Biologie Tissulaire Et Ingénierie Thérapeutique, UMR5305 CNRS/UCBL, Lyon, France
| | - Raphael Richert
- Laboratoire de Mécanique Des Contacts Et Structures, UMR 5259, CNRS/INSA, Villeurbanne, France. .,Hospices Civils de Lyon, PAM d'Odontologie, Lyon, France. .,Faculty of Odontology, Lyon 1 University, Lyon, France.
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Performance of Artificial Intelligence Models Designed for Diagnosis, Treatment Planning and Predicting Prognosis of Orthognathic Surgery (OGS)—A Scoping Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The technological advancements in the field of medical science have led to an escalation in the development of artificial intelligence (AI) applications, which are being extensively used in health sciences. This scoping review aims to outline the application and performance of artificial intelligence models used for diagnosing, treatment planning and predicting the prognosis of orthognathic surgery (OGS). Data for this paper was searched through renowned electronic databases such as PubMed, Google Scholar, Scopus, Web of science, Embase and Cochrane for articles related to the research topic that have been published between January 2000 and February 2022. Eighteen articles that met the eligibility criteria were critically analyzed based on QUADAS-2 guidelines and the certainty of evidence of the included studies was assessed using the GRADE approach. AI has been applied for predicting the post-operative facial profiles and facial symmetry, deciding on the need for OGS, predicting perioperative blood loss, planning OGS, segmentation of maxillofacial structures for OGS, and differential diagnosis of OGS. AI models have proven to be efficient and have outperformed the conventional methods. These models are reported to be reliable and reproducible, hence they can be very useful for less experienced practitioners in clinical decision making and in achieving better clinical outcomes.
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Growth patterns and shape development of the paediatric mandible – A 3D statistical model. Bone Rep 2022; 16:101528. [PMID: 35399871 PMCID: PMC8987800 DOI: 10.1016/j.bonr.2022.101528] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 11/24/2022] Open
Abstract
Background/aim To develop a 3D morphable model of the normal paediatric mandible to analyse shape development and growth patterns for males and females. Methods Computed tomography (CT) data was collected for 242 healthy children referred for CT scan between 2011 and 2018 aged between 0 and 47 months (mean, 20.6 ± 13.4 months, 59.9% male). Thresholding techniques were used to segment the mandible from the CT scans. All mandible meshes were annotated using a defined set of 52 landmarks and processed such that all meshes followed a consistent triangulation. Following this, the mandible meshes were rigidly aligned to remove translation and rotation effects, while size effects were retained. Principal component analysis (PCA) was applied to the processed meshes to construct a generative 3D morphable model. Partial least squares (PLS) regression was also applied to the processed data to extract the shape modes with which to evaluate shape differences for age and sex. Growth curves were constructed for anthropometric measurements. Results A 3D morphable model of the paediatric mandible was constructed and validated with good generalisation, compactness, and specificity. Growth curves of the assessed anthropometric measurements were plotted without significant differences between male and female subjects. The first principal component was dominated by size effects and is highly correlated with age at time of scan (Spearman's r = 0.94, p < 0.01). As with PCA, the first extracted PLS mode captures much of the size variation within the dataset and is highly correlated with age (Spearman's r = −0.94, p < 0.01). Little correlation was observed between extracted shape modes and sex with either PCA or PLS for this study population. Conclusion The presented 3D morphable model of the paediatric mandible enables an understanding of mandibular shape development and variation by age and sex. It allowed for the construction of growth curves, which contains valuable information that can be used to enhance our understanding of various disorders that affect the mandibular development. Knowledge of shape changes in the growing mandible has potential to improve diagnostic accuracy for craniofacial conditions that impact the mandibular morphology, objective evaluation, surgical planning, and patient follow-up.
Shape and development patterns of the paediatric mandible (0 – 4 years) were evaluated using a dataset of 242 CT scans. A 3D morphable model of the paediatric mandible was constructed using principal component analysis (PCA). Validation experiments demonstrated that the 3D morphable model can produce realistic novel mandible samples. Partial least squares (PLS) regression was applied to the dataset to evaluate shape differences for age and sex. The first shape model correlated strongly with age for PCA and PLS, though little correlation was seen between shape and sex.
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Trenfield SJ, Awad A, McCoubrey LE, Elbadawi M, Goyanes A, Gaisford S, Basit AW. Advancing pharmacy and healthcare with virtual digital technologies. Adv Drug Deliv Rev 2022; 182:114098. [PMID: 34998901 DOI: 10.1016/j.addr.2021.114098] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 02/07/2023]
Abstract
Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.
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O' Sullivan E, van de Lande LS, Papaioannou A, Breakey RWF, Jeelani NO, Ponniah A, Duncan C, Schievano S, Khonsari RH, Zafeiriou S, Dunaway DJ. Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis. Sci Rep 2022; 12:2230. [PMID: 35140239 PMCID: PMC8828904 DOI: 10.1038/s41598-021-02411-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 11/09/2021] [Indexed: 11/27/2022] Open
Abstract
Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In general, these systems use 2D images and analyse texture and colour. They are powerful tools for photographic analysis but are not suitable for use with medical imaging modalities such as ultrasound, MRI or CT, and are unable to take shape information into consideration when making a diagnostic prediction. 3D morphable models (3DMMs), and their recently proposed successors, mesh autoencoders, analyse surface topography rather than texture enabling analysis from photography and all common medical imaging modalities and present an alternative to image-based analysis. We present a craniofacial analysis framework for syndrome identification using Convolutional Mesh Autoencoders (CMAs). The models were trained using 3D photographs of the general population (LSFM and LYHM), computed tomography data (CT) scans from healthy infants and patients with 3 genetically distinct craniofacial syndromes (Muenke, Crouzon, Apert). Machine diagnosis outperformed expert clinical diagnosis with an accuracy of 99.98%, sensitivity of 99.95% and specificity of 100%. The diagnostic precision of this technique supports its potential inclusion in clinical decision support systems. Its reliance on 3D topography characterisation make it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting.
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Affiliation(s)
- Eimear O' Sullivan
- UCL Great Ormond Street Institute of Child Health, London, UK.,Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France.,Department of Computing, Imperial College London, London, UK
| | - Lara S van de Lande
- UCL Great Ormond Street Institute of Child Health, London, UK. .,Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France. .,Department of Maxillofacial Surgery and Plastic Surgery, Necker - Enfants Malades University Hospital, Paris, France.
| | - Athanasios Papaioannou
- UCL Great Ormond Street Institute of Child Health, London, UK.,Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France.,Department of Computing, Imperial College London, London, UK
| | - Richard W F Breakey
- UCL Great Ormond Street Institute of Child Health, London, UK.,Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France
| | - N Owase Jeelani
- UCL Great Ormond Street Institute of Child Health, London, UK.,Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France
| | - Allan Ponniah
- Department of Plastic Surgery, Royal. Free Hospital, London, UK
| | | | - Silvia Schievano
- UCL Great Ormond Street Institute of Child Health, London, UK.,Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France
| | - Roman H Khonsari
- Department of Maxillofacial Surgery and Plastic Surgery, Necker - Enfants Malades University Hospital, Paris, France
| | | | - David J Dunaway
- UCL Great Ormond Street Institute of Child Health, London, UK.,Assistance Publique - Hôpitaux de Paris, Faculty of Medicine, University of Paris, Paris, France
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Yan KX, Liu L, Li H. Application of machine learning in oral and maxillofacial surgery. Artif Intell Med Imaging 2021; 2:104-114. [DOI: 10.35711/aimi.v2.i6.104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 02/06/2023] Open
Abstract
Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.
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Affiliation(s)
- Kai-Xin Yan
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lei Liu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hui Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
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O' Sullivan E, van de Lande LS, Oosting AJC, Papaioannou A, Jeelani NO, Koudstaal MJ, Khonsari RH, Dunaway DJ, Zafeiriou S, Schievano S. The 3D skull 0-4 years: A validated, generative, statistical shape model. Bone Rep 2021; 15:101154. [PMID: 34917697 PMCID: PMC8645852 DOI: 10.1016/j.bonr.2021.101154] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 11/20/2021] [Accepted: 11/22/2021] [Indexed: 11/16/2022] Open
Abstract
Background This study aims to capture the 3D shape of the human skull in a healthy paediatric population (0-4 years old) and construct a generative statistical shape model. Methods The skull bones of 178 healthy children (55% male, 20.8 ± 12.9 months) were reconstructed from computed tomography (CT) images. 29 anatomical landmarks were placed on the 3D skull reconstructions. Rotation, translation and size were removed, and all skull meshes were placed in dense correspondence using a dimensionless skull mesh template and a non-rigid iterative closest point algorithm. A 3D morphable model (3DMM) was created using principal component analysis, and intrinsically and geometrically validated with anthropometric measurements. Synthetic skull instances were generated exploiting the 3DMM and validated by comparison of the anthropometric measurements with the selected input population. Results The 3DMM of the paediatric skull 0-4 years was successfully constructed. The model was reasonably compact - 90% of the model shape variance was captured within the first 10 principal components. The generalisation error, quantifying the ability of the 3DMM to represent shape instances not encountered during training, was 0.47 mm when all model components were used. The specificity value was <0.7 mm demonstrating that novel skull instances generated by the model are realistic. The 3DMM mean shape was representative of the selected population (differences <2%). Overall, good agreement was observed in the anthropometric measures extracted from the selected population, and compared to normative literature data (max difference in the intertemporal distance) and to the synthetic generated cases. Conclusion This study presents a reliable statistical shape model of the paediatric skull 0-4 years that adheres to known skull morphometric measures, can accurately represent unseen skull samples not used during model construction and can generate novel realistic skull instances, thus presenting a solution to limited availability of normative data in this field.
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Affiliation(s)
- Eimear O' Sullivan
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
- Department of Computing, Imperial College London, London, UK
| | - Lara S. van de Lande
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Anne-Jet C. Oosting
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
- Department of Oral and Maxillofacial Surgery, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - Athanasios Papaioannou
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
- Department of Computing, Imperial College London, London, UK
| | - N. Owase Jeelani
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Maarten J. Koudstaal
- Department of Oral and Maxillofacial Surgery, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - Roman H. Khonsari
- Oral and Maxillofacial Surgery Department, Hospital Necker, Enfants Malades, Paris, France
| | - David J. Dunaway
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | | | - Silvia Schievano
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
- Corresponding author at: The Zayad Centre for Research, 20 Guilford St, London WC1N 1DZ, UK.
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Singh GD, Singh M. Virtual Surgical Planning: Modeling from the Present to the Future. J Clin Med 2021; 10:jcm10235655. [PMID: 34884359 PMCID: PMC8658225 DOI: 10.3390/jcm10235655] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/19/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022] Open
Abstract
Virtual surgery planning is a non-invasive procedure, which uses digital clinical data for diagnostic, procedure selection and treatment planning purposes, including the forecast of potential outcomes. The technique begins with 3D data acquisition, using various methods, which may or may not utilize ionizing radiation, such as 3D stereophotogrammetry, 3D cone-beam CT scans, etc. Regardless of the imaging technique selected, landmark selection, whether it is manual or automated, is the key to transforming clinical data into objects that can be interrogated in virtual space. As a prerequisite, the data require alignment and correspondence such that pre- and post-operative configurations can be compared in real and statistical shape space. In addition, these data permit predictive modeling, using either model-based, data-based or hybrid modeling. These approaches provide perspectives for the development of customized surgical procedures and medical devices with accuracy, precision and intelligence. Therefore, this review briefly summarizes the current state of virtual surgery planning.
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Affiliation(s)
- G. Dave Singh
- Virtual Craniofacial Laboratory, Stanford University, Stanford, CA 94301, USA
- Correspondence: ; Tel.: +1-720-924-9929
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Godden AR, Micha A, Wolf LM, Pitches C, Barry PA, Khan AA, Krupa KDC, Kirby AM, Rusby JE. Three-dimensional simulation of aesthetic outcome from breast-conserving surgery compared with viewing photographs or standard care: randomized clinical trial. Br J Surg 2021; 108:1181-1188. [PMID: 34370833 PMCID: PMC10364871 DOI: 10.1093/bjs/znab217] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022]
Abstract
INTRODUCTION Over half of women with surgically managed breast cancer in the UK undergo breast-conserving treatment (BCT). While photographs are shown prior to reconstructive surgery or complex oncoplastic procedures, standard practice prior to breast conservation is to simply describe the likely aesthetic changes. Patients have expressed the desire for more personalized information about likely appearance after surgery. The hypothesis was that viewing a three-dimensional (3D) simulation improves patients' confidence in knowing their likely aesthetic outcome after surgery. METHODS A randomized, controlled trial of 117 women planning unilateral BCT was undertaken. The randomization was three-way: standard of care (verbal description alone, control group), viewing two-dimensional (2D) photographs, or viewing a 3D simulation before surgery. The primary endpoint was the comparison between groups' median answer on a visual analogue scale (VAS) for the question administered before surgery: 'How confident are you that you know how your breasts are likely to look after treatment?' RESULTS The median VAS in the control group was 5.2 (i.q.r. 2.6-7.8); 8.0 (i.q.r. 5.7-8.7) for 2D photography, and 8.9 (i.q.r. 8.2-9.5) for 3D simulation. There was a significant difference between groups (P < 0.010) with post-hoc pairwise comparisons demonstrating a statistically significant difference between 3D simulation and both standard care and viewing 2D photographs (P < 0.010 and P = 0.012, respectively). CONCLUSION This RCT has demonstrated that women who viewed an individualized 3D simulation of likely aesthetic outcome for BCT were more confident going into surgery than those who received standard care or who were shown 2D photographs of other women. The impact on longer-term satisfaction with outcome remains to be determined.Registration number: NCT03250260 (http://www.clinicaltrials.gov).
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Affiliation(s)
- A R Godden
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
- Independent patient co-designer, Institute of Cancer Research, Sutton, Surrey, UK
| | - A Micha
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - L M Wolf
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - C Pitches
- Independent patient co-designer, Institute of Cancer Research, Sutton, Surrey, UK
| | - P A Barry
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - A A Khan
- Department of Plastic Surgery, The Royal Marsden NHS Foundation Trust, London, UK
| | - K D C Krupa
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - A M Kirby
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
- Independent patient co-designer, Institute of Cancer Research, Sutton, Surrey, UK
| | - J E Rusby
- Department of Breast Surgery, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
- Independent patient co-designer, Institute of Cancer Research, Sutton, Surrey, UK
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Maxillary Changes Following Facial Bipartition - A Three-Dimensional Quantification. J Craniofac Surg 2021; 32:2053-2057. [PMID: 33770039 DOI: 10.1097/scs.0000000000007632] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION Children with Apert syndrome have hypertelorism and midfacial hypoplasia, which can be treated with facial bipartition (FB), often aided by rigid external distraction. The technique involves a midline osteotomy that lateralizes the maxillary segments, resulting in posterior cross-bites and midline diastema. Varying degrees of spontaneous realignment of the dental arches occurs postoperatively. This study aims to quantify these movements and assess whether they occur as part of a wider skeletal relapse or as dental compensation. METHODS Patients who underwent FB and had high quality computed tomography scans at the preoperative stage, immediately postsurgery, and later postoperatively were reviewed. DICOM files were converted to three-dimensional bone meshes and anatomical point-to-point displacements were quantified using nonrigid iterative closest point registration. Displacements were visualized using arrow maps, thereby providing an overview of the movements of the facial skeleton and dentition. RESULTS Five patients with Apert syndrome were included. In all cases, the arrow maps demonstrated initial significant anterior movement of the frontofacial segment coupled with medial rotation of the orbits and transverse divergence of the maxillary arches. The bony position following initial surgery was shown to be largely stable, with primary dentoalveolar relapse correcting the dental alignment. CONCLUSIONS This study showed that spontaneous dental compensation occurs following FB without compromising the surgical result. It may be appropriate to delay active orthodontic for 6-months postoperatively until completion of this early compensatory phase.
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A Case of Complex Facial Reconstruction Illuminates Paul Tessier's Surgical State of Mind. J Craniofac Surg 2021; 32:e584-e586. [PMID: 34054098 DOI: 10.1097/scs.0000000000007782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
ABSTRACT Successful head and neck reconstructions tackle both morphological and functional issues within treatment plans involving multiple coordinated steps. Nowadays, biomaterials, computer-assisted surgery, and free tissue transfers have greatly increased the potentialities of craniofacial surgeons. In the 1970s, when Paul Tessier, one of the founders of modern plastic surgery, was at the peak of his career, complex reconstructions had little technology to rely on. Here we report a case of facial reconstruction after gunshot trauma performed by Paul Tessier based on his "craniofacial autarchy" principle, that is using solely local flaps and grafts harvested in the head and neck area. This case involved 30 procedures on the mandible, maxilla, chin, lips, and nose. Based on data from the archives from the "Association Française des Chirurgiens de la Face" (Amiens, France) we provide details on Tessier's approach to surgical planning and on his global conception of treatment plans in reconstructive surgery.
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