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Mercurio M, Denami F, Melissaridou D, Corona K, Cerciello S, Laganà D, Gasparini G, Minici R. Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review. Diagnostics (Basel) 2025; 15:776. [PMID: 40150118 PMCID: PMC11941175 DOI: 10.3390/diagnostics15060776] [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: 02/02/2025] [Revised: 03/14/2025] [Accepted: 03/17/2025] [Indexed: 03/29/2025] Open
Abstract
Magnetic resonance imaging (MRI) is routinely used to confirm the suspected diagnosis of anterior cruciate ligament (ACL) injury. Recently, many studies explored the role of artificial intelligence (AI) and deep learning (DL), a sub-category of AI, in the musculoskeletal field and medical imaging. The aim of this study was to review the current applications of DL models to detect ACL injury on MRI, thus providing an updated and critical synthesis of the existing literature and identifying emerging trends and challenges in the field. A total of 23 relevant articles were identified and included in the review. Articles originated from 10 countries, with China having the most contributions (n = 9), followed by the United State of America (n = 4). Throughout the article, we analyzed the concept of DL in ACL tears and provided examples of how these tools can impact clinical practice and patient care. DL models for MRI detection of ACL injury reported high values of accuracy, especially helpful for less experienced clinicians. Time efficiency was also demonstrated. Overall, the deep learning models have proven to be a valid resource, although still requiring technological developments for implementation in daily practice.
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Affiliation(s)
- Michele Mercurio
- Department of Orthopaedic and Trauma Surgery, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy; (M.M.); (G.G.)
- Research Center on Musculoskeletal Health, MusculoSkeletal Health@UMG, Magna Graecia University, 88100 Catanzaro, Italy
| | - Federica Denami
- Department of Orthopaedic and Trauma Surgery, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy; (M.M.); (G.G.)
| | - Dimitra Melissaridou
- 1st Department of Orthopaedic Surgery, National and Kapodistrian University of Athens, Attikon Hospital, 12462 Athens, Greece;
| | - Katia Corona
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Simone Cerciello
- School of Medicine, Saint Camillus University, 00131 Rome, Italy;
| | - Domenico Laganà
- Department of Experimental and Clinical Medicine, Magna Graecia University, 88100 Catanzaro, Italy;
- Radiology Unit, Department of Experimental and Clinical Medicine, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy;
| | - Giorgio Gasparini
- Department of Orthopaedic and Trauma Surgery, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy; (M.M.); (G.G.)
- Research Center on Musculoskeletal Health, MusculoSkeletal Health@UMG, Magna Graecia University, 88100 Catanzaro, Italy
| | - Roberto Minici
- Radiology Unit, Department of Experimental and Clinical Medicine, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy;
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Ruitenbeek HC, Oei EHG, Visser JJ, Kijowski R. Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade. Skeletal Radiol 2024; 53:1849-1868. [PMID: 38902420 DOI: 10.1007/s00256-024-04684-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 06/22/2024]
Abstract
This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.
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Affiliation(s)
- Huibert C Ruitenbeek
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Edwin H G Oei
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA.
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Botnari A, Kadar M, Patrascu JM. A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2024; 14:1090. [PMID: 38893617 PMCID: PMC11172202 DOI: 10.3390/diagnostics14111090] [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: 04/11/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
OBJECTIVES This study delves into the cutting-edge field of deep learning techniques, particularly deep convolutional neural networks (DCNNs), which have demonstrated unprecedented potential in assisting radiologists and orthopedic surgeons in precisely identifying meniscal tears. This research aims to evaluate the effectiveness of deep learning models in recognizing, localizing, describing, and categorizing meniscal tears in magnetic resonance images (MRIs). MATERIALS AND METHODS This systematic review was rigorously conducted, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Extensive searches were conducted on MEDLINE (PubMed), Web of Science, Cochrane Library, and Google Scholar. All identified articles underwent a comprehensive risk of bias analysis. Predictive performance values were either extracted or calculated for quantitative analysis, including sensitivity and specificity. The meta-analysis was performed for all prediction models that identified the presence and location of meniscus tears. RESULTS This study's findings underscore that a range of deep learning models exhibit robust performance in detecting and classifying meniscal tears, in one case surpassing the expertise of musculoskeletal radiologists. Most studies in this review concentrated on identifying tears in the medial or lateral meniscus and even precisely locating tears-whether in the anterior or posterior horn-with exceptional accuracy, as demonstrated by AUC values ranging from 0.83 to 0.94. CONCLUSIONS Based on these findings, deep learning models have showcased significant potential in analyzing knee MR images by learning intricate details within images. They offer precise outcomes across diverse tasks, including segmenting specific anatomical structures and identifying pathological regions. Contributions: This study focused exclusively on DL models for identifying and localizing meniscus tears. It presents a meta-analysis that includes eight studies for detecting the presence of a torn meniscus and a meta-analysis of three studies with low heterogeneity that localize and classify the menisci. Another novelty is the analysis of arthroscopic surgery as ground truth. The quality of the studies was assessed against the CLAIM checklist, and the risk of bias was determined using the QUADAS-2 tool.
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Affiliation(s)
- Alexei Botnari
- Department of Orthopedics, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Manuella Kadar
- Department of Computer Science, Faculty of Informatics and Engineering, “1 Decembrie 1918” University of Alba Iulia, 510009 Alba Iulia, Romania
| | - Jenel Marian Patrascu
- Department of Orthopedics-Traumatology, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania;
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Andriollo L, Picchi A, Sangaletti R, Perticarini L, Rossi SMP, Logroscino G, Benazzo F. The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives. Healthcare (Basel) 2024; 12:300. [PMID: 38338185 PMCID: PMC10855330 DOI: 10.3390/healthcare12030300] [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: 12/31/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.
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Affiliation(s)
- Luca Andriollo
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
- Department of Orthopedics, Catholic University of the Sacred Heart, 00168 Rome, Italy
| | - Aurelio Picchi
- Unit of Orthopedics, Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy; (A.P.); (G.L.)
| | - Rudy Sangaletti
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Loris Perticarini
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Stefano Marco Paolo Rossi
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Giandomenico Logroscino
- Unit of Orthopedics, Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy; (A.P.); (G.L.)
| | - Francesco Benazzo
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
- Biomedical Sciences Area, IUSS University School for Advanced Studies, 27100 Pavia, Italy
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Chang MC. Use of artificial intelligence in the field of pain medicine. World J Clin Cases 2024; 12:236-239. [PMID: 38313631 PMCID: PMC10835690 DOI: 10.12998/wjcc.v12.i2.236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/06/2023] [Accepted: 12/21/2023] [Indexed: 01/11/2024] Open
Abstract
In this editorial we comment on the article “Potential and limitations of ChatGPT and generative artificial intelligence in medial safety education” published in the recent issue of the World Journal of Clinical Cases. This article described the usefulness of artificial intelligence (AI) in medial safety education. Herein, we focus specifically on the use of AI in the field of pain medicine. AI technology has emerged as a powerful tool, and is expected to play an important role in the healthcare sector and significantly contribute to pain medicine as further developments are made. AI may have several applications in pain medicine. First, AI can assist in selecting testing methods to identify causes of pain and improve diagnostic accuracy. Entry of a patient’s symptoms into the algorithm can prompt it to suggest necessary tests and possible diagnoses. Based on the latest medical information and recent research results, AI can support doctors in making accurate diagnoses and setting up an effective treatment plan. Second, AI assists in interpreting medical images. For neural and musculoskeletal disorders, imaging tests are of vital importance. AI can analyze a variety of imaging data, including that from radiography, computed tomography, and magnetic resonance imaging, to identify specific patterns, allowing quick and accurate image interpretation. Third, AI can predict the outcomes of pain treatments, contributing to setting up the optimal treatment plan. By predicting individual patient responses to treatment, AI algorithms can assist doctors in establishing a treatment plan tailored to each patient, further enhancing treatment effectiveness. For efficient utilization of AI in the pain medicine field, it is crucial to enhance the accuracy of AI decision-making by using more medical data, while issues related to the protection of patient personal information and responsibility for AI decisions will have to be addressed. In the future, AI technology is expected to be innovatively applied in the field of pain medicine. The advancement of AI is anticipated to have a positive impact on the entire medical field by providing patients with accurate and effective medical services.
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Affiliation(s)
- Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu 705-717, South Korea
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