|
1
|
Vadlamudi S, Kumar V, Ghosh D, Abraham A. Artificial intelligence-powered precision: Unveiling the landscape of liver disease diagnosis—A comprehensive review. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024; 138:109452. [DOI: 10.1016/j.engappai.2024.109452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
|
|
2
|
Papachristou K, Katsakiori PF, Papadimitroulas P, Strigari L, Kagadis GC. Digital Twins' Advancements and Applications in Healthcare, Towards Precision Medicine. J Pers Med 2024; 14:1101. [PMID: 39590593 PMCID: PMC11595921 DOI: 10.3390/jpm14111101] [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: 09/06/2024] [Revised: 10/29/2024] [Accepted: 11/08/2024] [Indexed: 11/28/2024] Open
Abstract
This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics-such as tissues, organs, and biokinetic data-of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental influences. By establishing dynamic, bidirectional connections between the DTs of physical objects and their digital counterparts, these technologies enable real-time data exchange, thereby transforming electronic health records. Leveraging the increasing availability of extensive historical datasets from clinical trials and real-world sources, AI models can now generate comprehensive predictions of future health outcomes for specific patients in the form of AI-generated DTs. Such models can also offer insights into potential diagnoses, disease progression, and treatment responses. This remarkable progression in healthcare paves the way for precision medicine and personalized health, allowing for high-level individualized medical interventions and therapies. However, the integration of DTs into healthcare faces several challenges, including data security, accessibility, bias, and quality. Addressing these obstacles is crucial to realizing the full potential of DHTs, heralding a new era of personalized, precise, and accurate medicine.
Collapse
Affiliation(s)
- Konstantinos Papachristou
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece; (K.P.); (P.F.K.); (P.P.)
| | - Paraskevi F. Katsakiori
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece; (K.P.); (P.F.K.); (P.P.)
| | - Panagiotis Papadimitroulas
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece; (K.P.); (P.F.K.); (P.P.)
- Bioemission Technology Solutions, BIOEMTECH, 15344 Athens, Greece
| | - Lidia Strigari
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - George C. Kagadis
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece; (K.P.); (P.F.K.); (P.P.)
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| |
Collapse
|
|
3
|
Karthiga R, Narasimhan K, V T, M H, Amirtharajan R. Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities. MULTIMEDIA TOOLS AND APPLICATIONS 2024. [DOI: 10.1007/s11042-024-20271-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 08/27/2024] [Accepted: 09/11/2024] [Indexed: 01/02/2025]
|
|
4
|
Dong M, Wang Y, Todo Y, Hua Y. A Novel Feature Selection Strategy Based on the Harris Hawks Optimization Algorithm for the Diagnosis of Cervical Cancer. ELECTRONICS 2024; 13:2554. [DOI: 10.3390/electronics13132554] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Abstract
Cervical cancer is the fourth most commonly diagnosed cancer and one of the leading causes of cancer-related deaths among females worldwide. Early diagnosis can greatly increase the cure rate for cervical cancer. However, due to the need for substantial medical resources, it is difficult to implement in some areas. With the development of machine learning, utilizing machine learning to automatically diagnose cervical cancer has currently become one of the main research directions in the field. Such an approach typically involves a large number of features. However, a portion of these features is redundant or irrelevant. The task of eliminating redundant or irrelevant features from the entire feature set is known as feature selection (FS). Feature selection methods can roughly be divided into three types, including filter-based methods, wrapper-based methods, and embedded-based methods. Among them, wrapper-based methods are currently the most commonly used approach, and many researchers have demonstrated that these methods can reduce the number of features while improving the accuracy of diagnosis. However, this method still has some issues. Wrapper-based methods typically use heuristic algorithms for FS, which can result in significant computational time. On the other hand, heuristic algorithms are often sensitive to parameters, leading to instability in performance. To overcome this challenge, a novel wrapper-based method named the Binary Harris Hawks Optimization (BHHO) algorithm is proposed in this paper. Compared to other wrapper-based methods, the BHHO has fewer hyper-parameters, which contributes to better stability. Furthermore, we have introduced a rank-based selection mechanism into the algorithm, which endows BHHO with enhanced optimization capabilities and greater generalizability. To comprehensively evaluate the performance of the proposed BHHO, we conducted a series of experiments. The experimental results show that the proposed BHHO demonstrates better accuracy and stability compared to other common wrapper-based FS methods on the cervical cancer dataset. Additionally, even on other disease datasets, the proposed algorithm still provides competitive results, proving its generalizability.
Collapse
Affiliation(s)
- Minhui Dong
- Division of Electrical Engineering and Computer Science, Graduate School of Natural Science & Technology, Kanazawa University, Kakuma-Machi, Kanazawa 920-1192, Japan
| | - Yu Wang
- Division of Electrical Engineering and Computer Science, Graduate School of Natural Science & Technology, Kanazawa University, Kakuma-Machi, Kanazawa 920-1192, Japan
| | - Yuki Todo
- Faculty of Electrical, Information and Communication Engineering, Kanazawa University, Kakuma-Machi, Kanazawa 920-1192, Japan
| | - Yuxiao Hua
- Division of Electrical Engineering and Computer Science, Graduate School of Natural Science & Technology, Kanazawa University, Kakuma-Machi, Kanazawa 920-1192, Japan
| |
Collapse
|
|
5
|
Raj H, G N, Kodipalli A, Rao T. Prediction of Chronic Liver Disease Using Machine Learning Algorithms and Interpretation with SHAP Kernels. 2024 IEEE INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, ELECTRONICS AND INTELLIGENT COMMUNICATION SYSTEMS (ICITEICS) 2024:1-6. [DOI: 10.1109/iciteics61368.2024.10625550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Affiliation(s)
- Harini Raj
- Global Academy of Technology,Dept. Artificial Intelligence & Data Science,Bangalore,Karnataka
| | - Niharika G
- Global Academy of Technology,Dept. Artificial Intelligence & Data Science,Bangalore,Karnataka
| | - Ashwini Kodipalli
- Global Academy of Technology,Dept. Artificial Intelligence & Data Science,Bangalore,Karnataka
| | - Trupthi Rao
- Global Academy of Technology,Dept. Artificial Intelligence & Data Science,Bangalore,Karnataka
| |
Collapse
|
|
6
|
Chang Z, Peng CH, Chen KJ, Xu GK. Enhancing liver fibrosis diagnosis and treatment assessment: a novel biomechanical markers-based machine learning approach. Phys Med Biol 2024; 69:115046. [PMID: 38749471 DOI: 10.1088/1361-6560/ad4c4e] [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: 01/16/2024] [Accepted: 05/15/2024] [Indexed: 05/31/2024]
Abstract
Accurate diagnosis and treatment assessment of liver fibrosis face significant challenges, including inherent limitations in current techniques like sampling errors and inter-observer variability. Addressing this, our study introduces a novel machine learning (ML) framework, which integrates light gradient boosting machine and multivariate imputation by chained equations to enhance liver status assessment using biomechanical markers. Building upon our previously established multiscale mechanical characteristics in fibrotic and treated livers, this framework employs Gaussian Bayesian optimization for post-imputation, significantly improving classification performance. Our findings indicate a marked increase in the precision of liver fibrosis diagnosis and provide a novel, quantitative approach for assessing fibrosis treatment. This innovative combination of multiscale biomechanical markers with advanced ML algorithms represents a transformative step in liver disease diagnostics and treatment evaluation, with potential implications for other areas in medical diagnostics.
Collapse
Affiliation(s)
- Zhuo Chang
- Laboratory for Multiscale Mechanics and Medical Science, Department of Engineering Mechanics, SVL, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Chen-Hao Peng
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 41170, Taiwan, R.O.C
| | - Kai-Jung Chen
- Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan, R.O.C
| | - Guang-Kui Xu
- Laboratory for Multiscale Mechanics and Medical Science, Department of Engineering Mechanics, SVL, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| |
Collapse
|
|
7
|
Zhong X, Salahuddin Z, Chen Y, Woodruff HC, Long H, Peng J, Xie X, Lin M, Lambin P. An Interpretable Radiomics Model Based on Two-Dimensional Shear Wave Elastography for Predicting Symptomatic Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:5303. [PMID: 37958476 PMCID: PMC10647503 DOI: 10.3390/cancers15215303] [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: 09/23/2023] [Revised: 10/26/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
OBJECTIVE The aim of this study was to develop and validate an interpretable radiomics model based on two-dimensional shear wave elastography (2D-SWE) for symptomatic post-hepatectomy liver failure (PHLF) prediction in patients undergoing liver resection for hepatocellular carcinoma (HCC). METHODS A total of 345 consecutive patients were enrolled. A five-fold cross-validation was performed during training, and the models were evaluated in the independent test cohort. A multi-patch radiomics model was established based on the 2D-SWE images for predicting symptomatic PHLF. Clinical features were incorporated into the models to train the clinical-radiomics model. The radiomics model and the clinical-radiomics model were compared with the clinical model comprising clinical variables and other clinical predictive indices, including the model for end-stage liver disease (MELD) score and albumin-bilirubin (ALBI) score. Shapley Additive exPlanations (SHAP) was used for post hoc interpretability of the radiomics model. RESULTS The clinical-radiomics model achieved an AUC of 0.867 (95% CI 0.787-0.947) in the five-fold cross-validation, and this score was higher than that of the clinical model (AUC: 0.809; 95% CI: 0.715-0.902) and the radiomics model (AUC: 0.746; 95% CI: 0.681-0.811). The clinical-radiomics model showed an AUC of 0.822 in the test cohort, higher than that of the clinical model (AUC: 0.684, p = 0.007), radiomics model (AUC: 0.784, p = 0.415), MELD score (AUC: 0.529, p < 0.001), and ALBI score (AUC: 0.644, p = 0.016). The SHAP analysis showed that the first-order radiomics features, including first-order maximum 64 × 64, first-order 90th percentile 64 × 64, and first-order 10th percentile 32 × 32, were the most important features for PHLF prediction. CONCLUSION An interpretable clinical-radiomics model based on 2D-SWE and clinical variables can help in predicting symptomatic PHLF in HCC.
Collapse
Affiliation(s)
- Xian Zhong
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China; (X.Z.); (H.L.); (J.P.); (X.X.)
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6220 MD Maastricht, The Netherlands; (Z.S.); (Y.C.); (H.C.W.); (P.L.)
| | - Zohaib Salahuddin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6220 MD Maastricht, The Netherlands; (Z.S.); (Y.C.); (H.C.W.); (P.L.)
| | - Yi Chen
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6220 MD Maastricht, The Netherlands; (Z.S.); (Y.C.); (H.C.W.); (P.L.)
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6220 MD Maastricht, The Netherlands; (Z.S.); (Y.C.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology and Reproduction, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands
| | - Haiyi Long
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China; (X.Z.); (H.L.); (J.P.); (X.X.)
| | - Jianyun Peng
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China; (X.Z.); (H.L.); (J.P.); (X.X.)
| | - Xiaoyan Xie
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China; (X.Z.); (H.L.); (J.P.); (X.X.)
| | - Manxia Lin
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China; (X.Z.); (H.L.); (J.P.); (X.X.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology and Reproduction, Maastricht University, 6220 MD Maastricht, The Netherlands; (Z.S.); (Y.C.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology and Reproduction, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands
| |
Collapse
|
|
8
|
Huang X, Li Y, Yuan S, Wu X, Xu P, Zhou A. Shear wave elastography-based deep learning model for prognosis of patients with acutely decompensated cirrhosis. JOURNAL OF CLINICAL ULTRASOUND : JCU 2023; 51:1568-1578. [PMID: 37883118 DOI: 10.1002/jcu.23577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/16/2023] [Accepted: 09/21/2023] [Indexed: 10/27/2023]
Abstract
PURPOSE This study aimed to develop and validate a deep learning model based on two-dimensional (2D) shear wave elastography (SWE) for predicting prognosis in patients with acutely decompensated cirrhosis. METHODS We prospectively enrolled 288 acutely decompensated cirrhosis patients with a minimum 1-year follow-up, divided into a training cohort (202 patients, 1010 2D SWE images) and a test cohort (86 patients, 430 2D SWE images). Using transfer learning by Resnet-50 to analyze 2D SWE images, a SWE-based deep learning signature (DLswe) was developed for 1-year mortality prediction. A combined nomogram was established by incorporating deep learning SWE information and laboratory data through a multivariate Cox regression analysis. The performance of the nomogram was evaluated with respect to predictive discrimination, calibration, and clinical usefulness in the training and test cohorts. RESULTS The C-index for DLswe was 0.748 (95% CI 0.666-0.829) and 0.744 (95% CI 0.623-0.864) in the training and test cohorts, respectively. The combined nomogram significantly improved the C-index, accuracy, sensitivity, and specificity of DLswe to 0.823 (95% CI 0.763-0.883), 86%, 75%, and 89% in the training cohort, and 0.808 (95% CI 0.707-0.909), 83%, 74%, and 85% in the test cohort (both p < 0.05). Calibration curves demonstrated good calibration of the combined nomogram. Decision curve analysis indicated that the nomogram was clinically valuable. CONCLUSIONS The 2D SWE-based deep learning model holds promise as a noninvasive tool to capture valuable prognostic information, thereby improving outcome prediction in patients with acutely decompensated cirrhosis.
Collapse
Affiliation(s)
- Xingzhi Huang
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yaohui Li
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Songsong Yuan
- Department of Infectious Disease, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaoping Wu
- Department of Infectious Disease, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Pan Xu
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Aiyun Zhou
- Department of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China
| |
Collapse
|
|
9
|
Kamalanathan A, Muthu B, Kuniyil Kaleena P. Artificial Intelligence (AI) Game Changer in Cancer Biology. MARVELS OF ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE IN LIFE SCIENCES 2023:62-87. [DOI: 10.2174/9789815136807123010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Abstract
Healthcare is one of many industries where the most modern technologies,
such as artificial intelligence and machine learning, have shown a wide range of
applications. Cancer, one of the most prevalent non-communicable diseases in modern
times, accounts for a sizable portion of worldwide mortality. Investigations are
continuously being conducted to find ways to reduce cancer mortality and morbidity.
Artificial Intelligence (AI) is currently being used in cancer research, with promising
results. Two main features play a vital role in improving cancer prognosis: early
detection and proper diagnosis using imaging and molecular techniques. AI's use as a
tool in these sectors has demonstrated its capacity to precisely detect and diagnose,
which is one of AI's many applications in cancer research. The purpose of this chapter
is to review the literature and find AI applications in a range of cancers that are
commonly seen.
Collapse
Affiliation(s)
- Ashok Kamalanathan
- Department of Microbiology and Biotechnology, Faculty of Arts and Science, Bharath Institute
of Higher Education and Research (BIHER), Chennai- 600 073, Tamil Nadu, India
| | - Babu Muthu
- Department of Microbiology and Biotechnology, Faculty of Arts and Science, Bharath Institute
of Higher Education and Research (BIHER), Chennai- 600 073, Tamil Nadu, India
| | | |
Collapse
|
|
10
|
Zhang XY, Wei Q, Wu GG, Tang Q, Pan XF, Chen GQ, Zhang D, Dietrich CF, Cui XW. Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review. Front Oncol 2023; 13:1197447. [PMID: 37333814 PMCID: PMC10272784 DOI: 10.3389/fonc.2023.1197447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/22/2023] [Indexed: 06/20/2023] Open
Abstract
Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.
Collapse
Affiliation(s)
- Xian-Ya Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wei
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ge-Ge Wu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Tang
- Department of Ultrasonography, The First Hospital of Changsha, Changsha, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China
| | - Di Zhang
- Department of Medical Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | | | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
|
11
|
Lei P, Hu N, Wu Y, Tang M, Lin C, Kong L, Zhang L, Luo P, Chan LW. Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases. IRADIOLOGY 2023; 1:167-189. [DOI: 10.1002/ird3.24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 05/18/2023] [Indexed: 01/04/2025]
Abstract
AbstractThe liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.
Collapse
Affiliation(s)
- Pinggui Lei
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
- School of Public Health Guizhou Medical University Guiyang Guizhou China
| | - Na Hu
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Yuhui Wu
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Maowen Tang
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Chong Lin
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Luoyi Kong
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
| | - Lingfeng Zhang
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
| | - Peng Luo
- School of Public Health Guizhou Medical University Guiyang Guizhou China
| | - Lawrence Wing‐Chi Chan
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
| |
Collapse
|
|
12
|
Popa SL, Ismaiel A, Abenavoli L, Padureanu AM, Dita MO, Bolchis R, Munteanu MA, Brata VD, Pop C, Bosneag A, Dumitrascu DI, Barsan M, David L. Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59050992. [PMID: 37241224 DOI: 10.3390/medicina59050992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/04/2023] [Accepted: 05/19/2023] [Indexed: 05/28/2023]
Abstract
Background and Objectives: The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. The recent developments of artificial intelligence (AI) applications show a high potential for improving the accuracy of diagnosis, involving large sets of clinical data. For this reason, the aim of this systematic review is to provide a comprehensive overview of current AI applications and analyze the accuracy of these systems to perform an automated diagnosis of liver fibrosis. Materials and Methods: We searched PubMed, Cochrane Library, EMBASE, and WILEY databases using predefined keywords. Articles were screened for relevant publications about AI applications capable of diagnosing liver fibrosis. Exclusion criteria were animal studies, case reports, abstracts, letters to the editor, conference presentations, pediatric studies, studies written in languages other than English, and editorials. Results: Our search identified a total of 24 articles analyzing the automated imagistic diagnosis of liver fibrosis, out of which six studies analyze liver ultrasound images, seven studies analyze computer tomography images, five studies analyze magnetic resonance images, and six studies analyze liver biopsies. The studies included in our systematic review showed that AI-assisted non-invasive techniques performed as accurately as human experts in detecting and staging liver fibrosis. Nevertheless, the findings of these studies need to be confirmed through clinical trials to be implemented into clinical practice. Conclusions: The current systematic review provides a comprehensive analysis of the performance of AI systems in diagnosing liver fibrosis. Automatic diagnosis, staging, and risk stratification for liver fibrosis is currently possible considering the accuracy of the AI systems, which can overcome the limitations of non-invasive diagnosis methods.
Collapse
Affiliation(s)
- Stefan Lucian Popa
- 2nd Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Abdulrahman Ismaiel
- 2nd Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Ludovico Abenavoli
- Department of Health Sciences, University "Magna Graecia", 88100 Catanzaro, Italy
| | | | - Miruna Oana Dita
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Roxana Bolchis
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Mihai Alexandru Munteanu
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410087 Oradea, Romania
| | - Vlad Dumitru Brata
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Cristina Pop
- Department of Pharmacology, Physiology, and Pathophysiology, Faculty of Pharmacy, Iuliu Hatieganu University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Andrei Bosneag
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Dinu Iuliu Dumitrascu
- Department of Anatomy, UMF "Iuliu Hatieganu" Cluj-Napoca, 400000 Cluj-Napoca, Romania
| | - Maria Barsan
- Department of Occupational Health, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Liliana David
- 2nd Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| |
Collapse
|
|
13
|
Manjunath RV, Ghanshala A, Kwadiki K. Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-18. [PMID: 37362702 PMCID: PMC10183675 DOI: 10.1007/s11042-023-15627-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/10/2022] [Accepted: 04/22/2023] [Indexed: 06/28/2023]
Abstract
To diagnose the liver diseases computed tomography images are used. Most of the time even experienced radiologists find it very tough to note the type, size, and severity of the tumor from computed tomography images due to various complexities involved around the liver. In recent years it is very much essential to develop a computer-assisted imaging technique to diagnose liver disease in turn which improves the diagnosis of a doctor. This paper explains a novel deep learning model for detecting a liver disease tumor and its classification. Tumor from computed tomography images has been classified between Metastasis and Cholangiocarcinoma. We demonstrate that our model predominantly performs very well concerning the accuracy, dice similarity coefficient, and specificity parameters compared to well-known existing algorithms, and adapts very well for different datasets. A dice similarity coefficient value of 98.59% indicates the supremacy of the model.
Collapse
Affiliation(s)
- R. V. Manjunath
- Department of Electronics &Communication Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore-82, India
| | | | - Karibasappa Kwadiki
- Department of CS&IT, Graphic Era Deemed to be University, Dehradun, 248002 India
| |
Collapse
|
|
14
|
Gheorghe EC, Nicolau C, Kamal A, Udristoiu A, Gruionu L, Saftoiu A. Artificial Intelligence (AI)-Enhanced Ultrasound Techniques Used in Non-Alcoholic Fatty Liver Disease: Are They Ready for Prime Time? APPLIED SCIENCES 2023; 13:5080. [DOI: 10.3390/app13085080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic liver disease, affecting approximately 2 billion individuals worldwide with a spectrum that can range from simple steatosis to cirrhosis. Typically, the diagnosis of NAFLD is based on imaging studies, but the gold standard remains liver biopsies. Hence, the use of artificial intelligence (AI) in this field, which has recently undergone rapid development in various aspects of medicine, has the potential to accurately diagnose NAFLD and steatohepatitis (NASH). This paper provides an overview of the latest research that employs AI for the diagnosis and staging of NAFLD, as well as applications for future developments in this field.
Collapse
Affiliation(s)
- Elena Codruta Gheorghe
- Department of Family Medicine, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania
| | - Carmen Nicolau
- Lotus Image Medical Center, ActaMedica SRL Târgu Mureș, 540084 Târgu Mureș, Romania
| | - Adina Kamal
- Department of Internal Medicine, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania
| | - Anca Udristoiu
- Faculty of Automation, Computers and Electronics, University of Craiova, 200776 Craiova, Romania
| | - Lucian Gruionu
- Faculty of Mechanics, University of Craiova, 200512 Craiova, Romania
| | - Adrian Saftoiu
- Department of Gastroenterology and Hepatology, University of Medicine and Pharmacy Carol Davila Bucharest, 050474 Bucharest, Romania
- Department of Gastroenterology, Ponderas Academic Hospital, 014142 Bucharest, Romania
| |
Collapse
|
|
15
|
Nishida N, Kudo M. Artificial intelligence models for the diagnosis and management of liver diseases. Ultrasonography 2023; 42:10-19. [PMID: 36443931 PMCID: PMC9816706 DOI: 10.14366/usg.22110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023] Open
Abstract
With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
Collapse
Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan,Correspondence to: Naoshi Nishida, MD, PhD, Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan Tel. +81-72-366-0221 Fax. +81-72-367-8220 E-mail:
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| |
Collapse
|
|
16
|
Kalapala R, Rughwani H, Reddy DN. Artificial Intelligence in Hepatology- Ready for the Primetime. J Clin Exp Hepatol 2023; 13:149-161. [PMID: 36647407 PMCID: PMC9840075 DOI: 10.1016/j.jceh.2022.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/23/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.
Collapse
Key Words
- ACLF, acute on chronic liver failure
- AI, artificial intelligence
- ALD, alcoholic liver disease
- ALT, alanine transaminase
- ANN, artificial neural network
- AST, aspartate aminotransferase
- AUD, alcohol use disorder
- CHB, chronic hepatitis B
- CHC, chronic hepatitis C
- CLD, chronic liver disease
- CNN, convolutional neural network
- DL, deep learning
- FIB-4, fibrosis-4 score
- GGTP, gamma glutamyl transferase
- HCC, hepatocellular carcinoma
- HDL, high density lipoprotein
- ML, machine learning
- MLR, multi-nomial logistic regressions
- NAFLD
- NAFLD, non-alcoholic fatty liver disease
- NASH, non-alcoholic steatohepatitis
- NLP, natural language processing
- RF, random forest
- RTE, real-time tissue elastography
- SOLs, space-occupying lesions
- SVM, support vector machine
- artificial intelligence
- deep learning
- hepatology
- machine learning
Collapse
Affiliation(s)
- Rakesh Kalapala
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
| | - Hardik Rughwani
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
| | | |
Collapse
|
|
17
|
Zhang L, Mao Y. Artificial Intelligence in NAFLD: Will Liver Biopsy Still Be Necessary in the Future? Healthcare (Basel) 2022; 11:117. [PMID: 36611577 PMCID: PMC9818843 DOI: 10.3390/healthcare11010117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/03/2022] [Accepted: 12/26/2022] [Indexed: 01/03/2023] Open
Abstract
As the advanced form of nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH) will significantly increase the risks of liver fibrosis, cirrhosis, and HCC. However, there is no non-invasive method to distinguish NASH from NAFLD so far. Additionally, liver biopsy remains the gold standard to diagnose NASH, which is not appropriate for routine screening. Recently, artificial intelligence (AI) is under rapid development in many aspects of medicine. Additionally, the application of AI in clinical information may have the potential to diagnose NASH non-invasively. This review summarizes the latest research using AI, specifically machine learning, to facilitate the diagnosis, prognosis, and monitoring of NAFLD. Additionally, according to our prior results, this work proposes future development in this area.
Collapse
Affiliation(s)
- Lei Zhang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Yilei Mao
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| |
Collapse
|
|
18
|
Wang C, Zheng L, Li Y, Xia S, Lv J, Hu X, Zhan W, Yan F, Li R, Ren X. Noninvasive Assessment of Liver Fibrosis and Inflammation in Chronic Hepatitis B: A Dual-task Convolutional Neural Network (DtCNN) Model Based on Ultrasound Shear Wave Elastography. J Clin Transl Hepatol 2022; 10:1077-1085. [PMID: 36381093 PMCID: PMC9634761 DOI: 10.14218/jcth.2021.00447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 02/22/2022] [Accepted: 03/03/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND AND AIMS Liver stiffness (LS) measured by shear wave elastography (SWE) is often influenced by hepatic inflammation. The aim was to develop a dual-task convolutional neural network (DtCNN) model for the simultaneous staging of liver fibrosis and inflammation activity using 2D-SWE. METHODS A total of 532 patients with chronic hepatitis B (CHB) were included to develop and validate the DtCNN model. An additional 180 consecutive patients between December 2019 and April 2021 were prospectively included for further validation. All patients underwent 2D-SWE examination and serum biomarker assessment. A DtCNN model containing two pathways for the staging of fibrosis and inflammation was used to improve the classification of significant fibrosis (≥F2), advanced fibrosis (≥F3) as well as cirrhosis (F4). RESULTS Both fibrosis and inflammation affected LS measurements by 2D-SWE. The proposed DtCNN performed the best among all the classification models for fibrosis stage [significant fibrosis AUC=0.89 (95% CI: 0.87-0.92), advanced fibrosis AUC=0.87 (95% CI: 0.84-0.90), liver cirrhosis AUC=0.85 (95% CI: 0.81-0.89)]. The DtCNN-based prediction of inflammation activity achieved AUCs of 0.82 (95% CI: 0.78-0.86) for grade ≥A1, 0.88 (95% CI: 0.85-0.90) grade ≥A2 and 0.78 (95% CI: 0.75-0.81) for grade ≥A3, which were significantly higher than the AUCs of the single-task groups. Similar findings were observed in the prospective study. CONCLUSIONS The proposed DtCNN improved diagnostic performance compared with existing fibrosis staging models by including inflammation in the model, which supports its potential clinical application.
Collapse
Affiliation(s)
- Chengyan Wang
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Lili Zheng
- Ultrasound Department, Ruijin Hospital Wuxi Branch, Shanghai Jiao Tong University School of Medicine, Wuxi, Jiangsu, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shujun Xia
- Ultrasound Department, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Lv
- School of Computer and Control Engineering, Yantai University, Yantai, Shandong, China
| | - Xumei Hu
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Weiwei Zhan
- Ultrasound Department, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Correspondence to: Xinping Ren, Ultrasound Department, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2 Road, Huangpu District, Shanghai 200020, China. ORCID: https://orcid.org/0000-0002-7999-4065. Tel: +86-18930819785, Fax: +86-31265738, E-mail: ; Ruokun Li, Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2 Road, Huangpu District, Shanghai 200020, Chian. ORCID: https://orcid.org/0000-0002-6929-0013. Tel: +86-18930819782, Fax: +86-31265443, E-mail:
| | - Xinping Ren
- Ultrasound Department, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Correspondence to: Xinping Ren, Ultrasound Department, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2 Road, Huangpu District, Shanghai 200020, China. ORCID: https://orcid.org/0000-0002-7999-4065. Tel: +86-18930819785, Fax: +86-31265738, E-mail: ; Ruokun Li, Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2 Road, Huangpu District, Shanghai 200020, Chian. ORCID: https://orcid.org/0000-0002-6929-0013. Tel: +86-18930819782, Fax: +86-31265443, E-mail:
| |
Collapse
|
|
19
|
Paskali F, Simantzik J, Dieterich A, Kohl M. Specification of Neck Muscle Dysfunction through Digital Image Analysis Using Machine Learning. Diagnostics (Basel) 2022; 13:diagnostics13010007. [PMID: 36611299 PMCID: PMC9818408 DOI: 10.3390/diagnostics13010007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/17/2022] [Accepted: 12/18/2022] [Indexed: 12/24/2022] Open
Abstract
Everyone has or will have experienced some degree of neck pain. Typically, neck pain is associated with the sensation of tense, tight, or stiff neck muscles. However, it is unclear whether the neck muscles are objectively stiffer with neck pain. This study used 1099 ultrasound elastography images (elastograms) obtained from 38 adult women, 20 with chronic neck pain and 18 asymptomatic. For training machine learning algorithms, 28 numerical characteristics were extracted from both the original and transformed shear wave velocity color-coded images as well as from respective image segments. Overall, a total number of 323 distinct features were generated from the data. A supervised binary classification was performed, using six machine-learning algorithms. The random forest algorithm produced the most accurate model to distinguish the elastograms of women with chronic neck pain from asymptomatic women with an AUC of 0.898. When evaluating features that can be used as biomarkers for muscle dysfunction in neck pain, the region of the deepest neck muscles (M. multifidus) provided the most features to support the correct classification of elastograms. By constructing summary images and associated Hotelling's T2 maps, we enabled the visualization of group differences and their statistical confirmation.
Collapse
Affiliation(s)
- Filip Paskali
- Institute of Precision Medicine, Medical and Life Sciences, Hochschule Furtwangen, 78054 Villingen-Schwenningen, Germany
| | - Jonathan Simantzik
- Institute of Precision Medicine, Medical and Life Sciences, Hochschule Furtwangen, 78054 Villingen-Schwenningen, Germany
| | - Angela Dieterich
- Physiotherapie, Fakultät Gesundheit, Sicherheit, Gesellschaft, Hochschule Furtwangen, Studienzentrum Freiburg, 79110 Freiburg, Germany
| | - Matthias Kohl
- Institute of Precision Medicine, Medical and Life Sciences, Hochschule Furtwangen, 78054 Villingen-Schwenningen, Germany
- Correspondence:
| |
Collapse
|
|
20
|
Gatos I, Yarmenitis S, Theotokas I, Koskinas J, Manesis E, Zoumpoulis SP, Zoumpoulis PS. Comparison of Visual Transient Elastography, Vibration Controlled Transient Elastography, Shear Wave Elastography and Sound Touch Elastography in Chronic liver Disease assessment using liver biopsy as 'Gold Standard'. Eur J Radiol 2022; 157:110557. [PMID: 36274360 DOI: 10.1016/j.ejrad.2022.110557] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/15/2022] [Accepted: 10/10/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE Chronic liver disease (CLD) is considered one of the main causes of death. Ultrasound Elastography (USE) is a CLD assessment imaging method. This study aims to evaluate a recently introduced commercial alternative of USE, Visual Transient Elastography (ViTE), and to compare it with three established USE methods, Vibration Controlled Transient Elastography (VCTE), Shear Wave Elastography (SWE) and Sound Touch Elastography (STE), using Liver Biopsy (LB) as 'Gold Standard'. METHOD 152 consecutive subjects underwent a liver ViTE, VCTE, SWE and STE examination. A Receiver Operator Characteristic (ROC) analysis was performed on the measured stiffness values of each method. An inter- intra-observer analysis was also performed. RESULTS The ViTE, VCTE, SWE and STE ROC analysis resulted in an AUC of 0.9481, 0.9900, 0.9621 and 0.9683 for F ≥ F1, 0.9698, 0.9767, 0.9931 and 0.9834 for F ≥ F2, 0.9846, 0.9651, 0.9835 and 0.9763 for F ≥ F3, and 0.9524, 0.9645, 0.9656, and 0.9509 for F = F4, respectively. ICC scores were 0.98 for Inter-observer and 0.97 for Intra-observer variability analysis. CONCLUSION ViTE performance in CLD stage differentiation is comparable to the performance of VCTE, SWE and STE.
Collapse
Affiliation(s)
- Ilias Gatos
- Diagnostic Echotomography SA, 317C Kifissias Ave., GR 14561, Kifissia, Greece; Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece.
| | - Spyros Yarmenitis
- Diagnostic Echotomography SA, 317C Kifissias Ave., GR 14561, Kifissia, Greece.
| | - Ioannis Theotokas
- Diagnostic Echotomography SA, 317C Kifissias Ave., GR 14561, Kifissia, Greece.
| | - John Koskinas
- 2(nd) Academic Department of Medicine, Medical School of Athens, National and Kapodistrian University of Athens, Mikras Asias 75, Athens, GR 115 27, Greece; Hippokrateion General Hospital, Vasilissis Sofias 114, Athens, GR 115 27, Greece.
| | | | - Spyros P Zoumpoulis
- Diagnostic Echotomography SA, 317C Kifissias Ave., GR 14561, Kifissia, Greece.
| | - Pavlos S Zoumpoulis
- Diagnostic Echotomography SA, 317C Kifissias Ave., GR 14561, Kifissia, Greece.
| |
Collapse
|
|
21
|
Liu JQ, Ren JY, Xu XL, Xiong LY, Peng YX, Pan XF, Dietrich CF, Cui XW. Ultrasound-based artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2022; 28:5530-5546. [PMID: 36304086 PMCID: PMC9594013 DOI: 10.3748/wjg.v28.i38.5530] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/12/2022] [Accepted: 09/22/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI), especially deep learning, is gaining extensive attention for its excellent performance in medical image analysis. It can automatically make a quantitative assessment of complex medical images and help doctors to make more accurate diagnoses. In recent years, AI based on ultrasound has been shown to be very helpful in diffuse liver diseases and focal liver lesions, such as analyzing the severity of nonalcoholic fatty liver and the stage of liver fibrosis, identifying benign and malignant liver lesions, predicting the microvascular invasion of hepatocellular carcinoma, curative transarterial chemoembolization effect, and prognoses after thermal ablation. Moreover, AI based on endoscopic ultrasonography has been applied in some gastrointestinal diseases, such as distinguishing gastric mesenchymal tumors, detection of pancreatic cancer and intraductal papillary mucinous neoplasms, and predicting the preoperative tumor deposits in rectal cancer. This review focused on the basic technical knowledge about AI and the clinical application of AI in ultrasound of liver and gastroenterology diseases. Lastly, we discuss the challenges and future perspectives of AI.
Collapse
Affiliation(s)
- Ji-Qiao Liu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Jia-Yu Ren
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Xiao-Lan Xu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Li-Yan Xiong
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Yue-Xiang Peng
- Department of Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan 430030, Hubei Province, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian 116000, Liaoning Province, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern 3003, Switzerland
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| |
Collapse
|
|
22
|
Li H, Bhatt M, Qu Z, Zhang S, Hartel MC, Khademhosseini A, Cloutier G. Deep learning in ultrasound elastography imaging: A review. Med Phys 2022; 49:5993-6018. [PMID: 35842833 DOI: 10.1002/mp.15856] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 02/04/2022] [Accepted: 07/06/2022] [Indexed: 11/11/2022] Open
Abstract
It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Hongliang Li
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.,Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada
| | - Manish Bhatt
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada
| | - Zhen Qu
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada
| | - Shiming Zhang
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Martin C Hartel
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Ali Khademhosseini
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Guy Cloutier
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.,Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada.,Department of Radiology, Radio-Oncology and Nuclear Medicine, University of Montreal, Montréal, Québec, Canada
| |
Collapse
|
|
23
|
Zhou B, Yang X, Curran WJ, Liu T. Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:1329-1342. [PMID: 34467542 DOI: 10.1002/jum.15819] [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: 12/29/2020] [Revised: 08/01/2021] [Accepted: 08/16/2021] [Indexed: 06/13/2023]
Abstract
Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magnetic resonance imaging, QUS suffers from several major drawbacks: poor image quality and inter- and intra-observer variability. Therefore, there is a great need to develop automated methods to improve the image quality of QUS. In recent years, there has been increasing interest in artificial intelligence (AI) applications in medical imaging, and a large number of research studies in AI in QUS have been conducted. The purpose of this review is to describe and categorize recent research into AI applications in QUS. We first introduce the AI workflow and then discuss the various AI applications in QUS. Finally, challenges and future potential AI applications in QUS are discussed.
Collapse
Affiliation(s)
- Boran Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| |
Collapse
|
|
24
|
Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review. Comput Biol Med 2022; 146:105580. [PMID: 35551012 DOI: 10.1016/j.compbiomed.2022.105580] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/14/2022] [Accepted: 04/30/2022] [Indexed: 02/07/2023]
Abstract
Being a second most cause of mortality worldwide, cancer has been identified as a perilous disease for human beings, where advance stage diagnosis may not help much in safeguarding patients from mortality. Thus, efforts to provide a sustainable architecture with proven cancer prevention estimate and provision for early diagnosis of cancer is the need of hours. Advent of machine learning methods enriched cancer diagnosis area with its overwhelmed efficiency & low error-rate then humans. A significant revolution has been witnessed in the development of machine learning & deep learning assisted system for segmentation & classification of various cancers during past decade. This research paper includes a review of various types of cancer detection via different data modalities using machine learning & deep learning-based methods along with different feature extraction techniques and benchmark datasets utilized in the recent six years studies. The focus of this study is to review, analyse, classify, and address the recent development in cancer detection and diagnosis of six types of cancers i.e., breast, lung, liver, skin, brain and pancreatic cancer, using machine learning & deep learning techniques. Various state-of-the-art technique are clustered into same group and results are examined through key performance indicators like accuracy, area under the curve, precision, sensitivity, dice score on benchmark datasets and concluded with future research work challenges.
Collapse
Affiliation(s)
- Deepak Painuli
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India.
| | - Suyash Bhardwaj
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India
| | - Utku Köse
- Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey
| |
Collapse
|
|
25
|
Prakash K, Saradha S. A Deep Learning Approach for Classification and Prediction of Cirrhosis Liver: Non Alcoholic Fatty Liver Disease (NAFLD). 2022 6TH INTERNATIONAL CONFERENCE ON TRENDS IN ELECTRONICS AND INFORMATICS (ICOEI) 2022:1277-1284. [DOI: 10.1109/icoei53556.2022.9777239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Affiliation(s)
- K. Prakash
- Vel's Institute of Science, Technology & Advanced Studies(VISTAS),Department of Computer Science,Chennai,Tamil Nadu,India
| | - S. Saradha
- Vel's Institute of Science, Technology & Advanced Studies(VISTAS),Department of Computer Science,Chennai,Tamil Nadu,India
| |
Collapse
|
|
26
|
Simsek C, Lee LS. Machine learning in endoscopic ultrasonography and the pancreas: The new frontier? Artif Intell Gastroenterol 2022; 3:54-65. [DOI: 10.35712/aig.v3.i2.54] [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: 02/01/2022] [Revised: 03/28/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic diseases have a substantial burden on society which is predicted to increase further over the next decades. Endoscopic ultrasonography (EUS) remains the best available diagnostic method to assess the pancreas, however, there remains room for improvement. Artificial intelligence (AI) approaches have been adopted to assess pancreatic diseases for over a decade, but this methodology has recently reached a new era with the innovative machine learning algorithms which can process, recognize, and label endosonographic images. Our review provides a targeted summary of AI in EUS for pancreatic diseases. Included studies cover a wide spectrum of pancreatic diseases from pancreatic cystic lesions to pancreatic masses and diagnosis of pancreatic cancer, chronic pancreatitis, and autoimmune pancreatitis. For these, AI models seemed highly successful, although the results should be evaluated carefully as the tasks, datasets and models were greatly heterogenous. In addition to use in diagnostics, AI was also tested as a procedural real-time assistant for EUS-guided biopsy as well as recognition of standard pancreatic stations and labeling anatomical landmarks during routine examination. Studies thus far have suggested that the adoption of AI in pancreatic EUS is highly promising and further opportunities should be explored in the field.
Collapse
Affiliation(s)
- Cem Simsek
- Department of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, United States
| | - Linda S Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
| |
Collapse
|
|
27
|
Huang K, Li Q, Zeng W, Chen X, Liu L, Wan X, Feng C, Li Z, Liu Z, Dong C. Ultrasound score combined with liver stiffness measurement by sound touch elastography for staging liver fibrosis in patients with chronic hepatitis B: a clinical prospective study. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:271. [PMID: 35434021 PMCID: PMC9011233 DOI: 10.21037/atm-22-505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/16/2022] [Indexed: 11/06/2022]
Abstract
Background A noninvasive and precise diagnosis of liver fibrosis in patients with chronic hepatitis B (CHB) is crucial for establishing the optimal time and strategy of therapy and for predicting treatment response. This study aimed to assess the diagnostic performance of ultrasound (US) score and liver stiffness measurement (LSM) of sound touch elastography (STE) in diagnosing liver fibrosis stages and to investigate whether combining these methods would improve liver fibrosis staging. Methods US and STE examinations were performed in CHB patients included. Liver biopsy was used as a reference standard. A diagnostic marker with the optimal linear combination (LC) of US score and LSM of STE, namely LC marker, was established for noninvasive assessment of liver fibrosis stages. The diagnostic performance of the LC marker was evaluated by using receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC). Results A total of 291 subjects, including 242 patients with CHB and 49 healthy volunteers, were included. Correlation analysis showed that the correlation of liver fibrosis stages to the LC marker (Spearman's r=0.846, P<0.001) was higher than that of LSM (r=0.771, P<0.001) or US score (r=0.825, P<0.001) alone. The results showed that the overall diagnostic performance of the LC marker in predicting a fibrosis stage of ≥F1, ≥F2, ≥F3, and =F4 {AUCs: 0.943 [95% confidence interval (CI): 0.917-0.948], 0.906 (0.871-0.915), 0.953 (0.923-0.969), and 0.961 (0.922-0.973), respectively} were better than those of the US score [AUCs: 0.916 (0.883-0.948, P=0.014), 0.875 (0.835-0.915, P<0.001), 0.934 (0.898-0.969, P=0.001), and 0.918 (0.864-0.973, P<0.001), respectively] or LSM [AUCs: 0.858 (0.812-0.948, P<0.001), 0.867 (0.826-0.915, P=0.006), 0.930 (0.894-0.969, P<0.023), and 0.958 (0.918-0.973, P=0.778), respectively]. Conclusions The LC marker with the optimal combination of LSM and US score may be considered as a promising diagnostic model for noninvasive staging of liver fibrosis.
Collapse
Affiliation(s)
- Kun Huang
- National Clinical Research Center for Infectious Disease, Department of Ultrasound, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Qinyuan Li
- Ultrasound Imaging Department, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Weimei Zeng
- First Medical College of Guangdong Medical University, Zhanjiang, China
| | - Xin Chen
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Li Liu
- Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong, Shenzhen, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong, Shenzhen, China
| | - Cheng Feng
- National Clinical Research Center for Infectious Disease, Department of Ultrasound, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Zhiyan Li
- National Clinical Research Center for Infectious Disease, Department of Ultrasound, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Zhong Liu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Changfeng Dong
- National Clinical Research Center for Infectious Disease, Department of Ultrasound, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| |
Collapse
|
|
28
|
Destrempes F, Gesnik M, Chayer B, Roy-Cardinal MH, Olivié D, Giard JM, Sebastiani G, Nguyen BN, Cloutier G, Tang A. Quantitative ultrasound, elastography, and machine learning for assessment of steatosis, inflammation, and fibrosis in chronic liver disease. PLoS One 2022; 17:e0262291. [PMID: 35085294 PMCID: PMC8794185 DOI: 10.1371/journal.pone.0262291] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 12/21/2021] [Indexed: 12/12/2022] Open
Abstract
Objective To develop a quantitative ultrasound (QUS)- and elastography-based model to improve classification of steatosis grade, inflammation grade, and fibrosis stage in patients with chronic liver disease in comparison with shear wave elastography alone, using histopathology as the reference standard. Methods This ancillary study to a prospective institutional review-board approved study included 82 patients with non-alcoholic fatty liver disease, chronic hepatitis B or C virus, or autoimmune hepatitis. Elastography measurements, homodyned K-distribution parametric maps, and total attenuation coefficient slope were recorded. Random forests classification and bootstrapping were used to identify combinations of parameters that provided the highest diagnostic accuracy. Receiver operating characteristic (ROC) curves were computed. Results For classification of steatosis grade S0 vs. S1-3, S0-1 vs. S2-3, S0-2 vs. S3, area under the receiver operating characteristic curve (AUC) were respectively 0.60, 0.63, and 0.62 with elasticity alone, and 0.90, 0.81, and 0.78 with the best tested model combining QUS and elastography features. For classification of inflammation grade A0 vs. A1-3, A0-1 vs. A2-3, A0-2 vs. A3, AUCs were respectively 0.56, 0.62, and 0.64 with elasticity alone, and 0.75, 0.68, and 0.69 with the best model. For classification of liver fibrosis stage F0 vs. F1-4, F0-1 vs. F2-4, F0-2 vs. F3-4, F0-3 vs. F4, AUCs were respectively 0.66, 0.77, 0.72, and 0.74 with elasticity alone, and 0.72, 0.77, 0.77, and 0.75 with the best model. Conclusion Random forest models incorporating QUS and shear wave elastography increased the classification accuracy of liver steatosis, inflammation, and fibrosis when compared to shear wave elastography alone.
Collapse
Affiliation(s)
- François Destrempes
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center (CRCHUM), Montréal, Québec, Canada
| | - Marc Gesnik
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center (CRCHUM), Montréal, Québec, Canada
| | - Boris Chayer
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center (CRCHUM), Montréal, Québec, Canada
| | - Marie-Hélène Roy-Cardinal
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center (CRCHUM), Montréal, Québec, Canada
| | - Damien Olivié
- Department of Radiology, Radiation oncology and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada
- Department of Radiology, Centre hospitalier de l’Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Jeanne-Marie Giard
- Department of Medicine, Division of Hepatology and Liver Transplantation, Université de Montréal, Montréal, Québec, Canada
| | - Giada Sebastiani
- Department of Medicine, Division of Gastroenterology and Hepatology, McGill University Health Centre (MUHC), Montréal, Québec, Canada
| | - Bich N. Nguyen
- Department of Pathology, Centre hospitalier de l’Université de Montréal (CHUM), Montréal, Québec, Canada
- Department of Pathology and Cellular Biology, Université de Montréal, Montréal, Québec, Canada
| | - Guy Cloutier
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center (CRCHUM), Montréal, Québec, Canada
- Department of Radiology, Radiation oncology and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada
- Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada
- * E-mail: (GC); (AT)
| | - An Tang
- Department of Radiology, Radiation oncology and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada
- Department of Radiology, Centre hospitalier de l’Université de Montréal (CHUM), Montréal, Québec, Canada
- Laboratory of Medical Image Analysis, Centre de recherche du Centre hospitalier de l’Université de Montréal (CRCHUM), Montréal, Québec, Canada
- * E-mail: (GC); (AT)
| |
Collapse
|
|
29
|
Ghazal TM, Rehman AU, Saleem M, Ahmad M, Ahmad S, Mehmood F. Intelligent Model to Predict Early Liver Disease using Machine Learning Technique. 2022 INTERNATIONAL CONFERENCE ON BUSINESS ANALYTICS FOR TECHNOLOGY AND SECURITY (ICBATS) 2022:1-5. [DOI: 10.1109/icbats54253.2022.9758929] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Affiliation(s)
- Taher M. Ghazal
- Faculty of Information Science and Technology UKM,Network and Communication Technology Lab, Center for Cyber Security,Malaysia
| | | | | | - Munir Ahmad
- School of Computer Science, NCBA&E,Lahore,Pakistan
| | - Shabir Ahmad
- Gachon University,Department of Computer Engineering,Korea
| | - Faisal Mehmood
- Gachon University,Department of IT Convergence Engineering,Korea
| |
Collapse
|
|
30
|
Bazarbashi AN, Al-Obaid L, Ryou M. Future Directions in EndoHepatology. TECHNIQUES AND INNOVATIONS IN GASTROINTESTINAL ENDOSCOPY 2022; 24:98-107. [DOI: 10.1016/j.tige.2021.09.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
|
|
31
|
Layek K, Basak B, Samanta S, Maity SP, Barui A. Stiffness prediction on elastography images and neuro-fuzzy based segmentation for thyroid cancer detection. APPLIED OPTICS 2022; 61:49-59. [PMID: 35200805 DOI: 10.1364/ao.445226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 11/29/2021] [Indexed: 06/14/2023]
Abstract
The elastography method detects metastatic changes by measuring the stiffness of tissues. Estimation of elasticities from elastography images facilitates more precise identification of the metastatic region and detection of the same. In this study, an automated segmentation algorithm is proposed that calculates pixel-wise elasticity values to detect thyroid cancer from elastography images. This intensity to elasticity conversion is achieved by constructing a fuzzy inference system using an adaptive neuro-fuzzy inference system supported by two meta-heuristic algorithms: genetic algorithm and particle swarm optimization. Pixels of the input color images (red, green, and blue) are replaced by equivalent elasticity values (in kilo Pascal) and are stored in a two-dimensional array to form an "elasticity matrix." The elasticity matrix is then segmented into three regions, namely, suspicious, near-suspicious, and non-suspicious, based on the elasticity measures, where the threshold limits are calculated using the fuzzy entropy maximization method optimized by the differential evolution algorithm. Segmentation performances are evaluated by Kappa and the dice similarity co-efficient, and average values achieved are 0.94±0.11 and 0.93±0.12, respectively. Sensitivity and specificity values achieved by the proposed method are 86.35±0.34% and 97.67±0.40%, respectively, showing an overall accuracy of 93.50±0.42%. Results justify the importance of pixel stiffness for segmentation of thyroid nodules in elastography images.
Collapse
|
|
32
|
Padmakala S, Subasini CA, Karuppiah SP, Sheeba A. ESVM-SWRF: Ensemble SVM-based sample weighted random forests for liver disease classification. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3525. [PMID: 34431606 DOI: 10.1002/cnm.3525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Recently, a significant way to diagnose the disease is using the model of medical data mining. The most challenging task in the healthcare field is to face a large amount of data during disease analyzes and prediction. Once the data are transformed into valuable data by means of data mining models then the actual prediction and decision making is easier. The existing studies met few shortcomings because of higher execution time, more computational complexities, less scalability, slow convergence, and lack of providing the solution. In this article, we have proposed an ensemble SVM-based sample weighted random forests (eSVM-swRF) with novel improved colliding body optimization (NICBO) algorithm to predict liver diseases. The extraction, loading, transformation, and analysis (ELTA) are used to pre-process the patient data. The significant feature with a suitable model is generated depending upon the filter-based method. Based on eSVM-swRF, the parameter values such as penalty parameter (P), threshold (T), and mTry are optimized via a novel improved colliding boding optimization (NICBO) algorithm. The UCI dataset provides liver disease data for this study. The implementation platform of RapidMiner Studio version 7.6 with different evaluation measures is used to validate the performance of eSVM-swRF with the NICBO method. Anyway, the proposed method yields outstanding performance than other existing methods such as Particle Swarm Optimization-based Support Vector Machine (PSO-SVM), fuzzy adaptive, and neighbor weighted k-NN (FuzzyANWKNN), Naïve Bayes-based Support Vector Machine (NB-SVM), and Neural network.
Collapse
Affiliation(s)
- S Padmakala
- Department of CSE, St. Joseph's Institute of Technology, Chennai, Tamil Nadu, India
| | - C A Subasini
- Department of CSE, St. Joseph's Institute of Technology, Chennai, Tamil Nadu, India
| | - S P Karuppiah
- Departmentof MBA, St. Joseph's College of Engineering, Chennai, India
| | - Adlin Sheeba
- Department of CSE, St. Joseph's Institute of Technology, Chennai, Tamil Nadu, India
| |
Collapse
|
|
33
|
Laroia ST, Vellore Srinivasan S, Yadav K, Rastogi A, Kumar S, Kumar G, Kumar M. Performance of shear wave elastography: A single centre pilot study of mixed etiology liver disease patients with normal BMI. Australas J Ultrasound Med 2021; 24:120-136. [PMID: 34765422 DOI: 10.1002/ajum.12244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/21/2021] [Accepted: 03/28/2021] [Indexed: 02/06/2023] Open
Abstract
Purpose To assess the performance of shear wave ultrasound elastography (SWE) for non-invasive grading of fibrosis in normal BMI patients with varied aetiology chronic liver disease. Method Prospective SWE liver and spleen stiffness (LS, SS respectively) of 124 patients (94 men, mean age 45.4 ± 12.4 years, mean BMI 19.66 ± 1.49) with CLD of mixed aetiology, who underwent liver biopsy, between January 2019-20 was analysed using receiver operating curve (ROC) and classification analysis regression tree (CART) to determine fibrosis cut-off values and nominal logistical regression to quantify fibrosis. Results Of 124 patients, 50 (40%) had non-alcoholic steatohepatitis (NASH), 31 (25%) chronic hepatitis B (CHB) and 43 (35%) alcoholic liver disease (ALD) on biopsy. Overall mean LS and SS of the study population was 11.81 ± 5.9 and 16.88 ± 10.8 kPa, respectively. LS cut-off value <8 kPa was consistent with F0, 9-14 kPa for F1-F2 and >14.9 kPa for F3-F4 fibrosis on biopsy. On application of CART, LS value < 5.3 kPa was discriminative for NASH, 5.32 to <12.64 kPa for CHB, >12.64 kPa for ALD, SS <15.3 kPa was discriminative for NASH, 15.3-30 kPa for CHB and >30 kPa for ALD in our study population. Conclusion SWE is a viable non-invasive tool for assessment of liver fibrosis grading in a population of mixed aetiology CLD. LS values in conjunction with SS are promising predictors of F2-F3 fibrosis with potential to discriminate select categories like CHB and NASH in such a population.
Collapse
Affiliation(s)
- Shalini Thapar Laroia
- Department of Radiology Institute of Liver and Biliary Sciences Sector D-1 Vasant Kunj New Delhi 110070 India
| | - Shyam Vellore Srinivasan
- Department of Radiology Institute of Liver and Biliary Sciences Sector D-1 Vasant Kunj New Delhi 110070 India
| | - Komal Yadav
- Department of Radiology Institute of Liver and Biliary Sciences Sector D-1 Vasant Kunj New Delhi 110070 India
| | - Archana Rastogi
- Department of Clinical and Hepato-Pathology Institute of Liver and Biliary Sciences Sector D-1 Vasant Kunj New Delhi 110070 India
| | - Senthil Kumar
- Department of HPB Surgery and Liver Transplantation Institute of Liver & Biliary Sciences Sector D-1 Vasant Kunj New Delhi 110070 India
| | - Guresh Kumar
- Department of Research Institute of Liver & Biliary Sciences Sector D-1 Vasant Kunj New Delhi 110070 India
| | - Manoj Kumar
- Department of Hepatology Institute of Liver & Biliary Sciences Sector D-1 Vasant Kunj New Delhi 110070 India
| |
Collapse
|
|
34
|
Zhang Y, Zhang Y, Zhang Y, Wang D, Peng F, Cui S, Yang Z. Ultrasonic image fibrosis staging based on machine learning for chronic liver disease. 2021 IEEE INTERNATIONAL CONFERENCE ON MEDICAL IMAGING PHYSICS AND ENGINEERING (ICMIPE) 2021:1-5. [DOI: 10.1109/icmipe53131.2021.9698912] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Affiliation(s)
- Yumeng Zhang
- School of Biomedical Engineering, Capital Medical University,Beijing,China
| | - Yao Zhang
- Capital Medical University,Beijing Ditan Hospital,Department of Ultrasound,Beijing,China
| | - Yunxian Zhang
- School of Biomedical Engineering, Capital Medical University,Beijing,China
| | - Dan Wang
- School of Biomedical Engineering, Capital Medical University,Beijing,China
| | - Fan Peng
- School of Biomedical Engineering, Capital Medical University,Beijing,China
| | - Shangqi Cui
- School of Biomedical Engineering, Capital Medical University,Beijing,China
| | - Zhi Yang
- School of Biomedical Engineering, Capital Medical University,Beijing,China
| |
Collapse
|
|
35
|
Vaz K, Goodwin T, Kemp W, Roberts S, Majeed A. Artificial Intelligence in Hepatology: A Narrative Review. Semin Liver Dis 2021; 41:551-556. [PMID: 34327698 DOI: 10.1055/s-0041-1731706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
There has been a tremendous growth in data collection in hepatology over the last decade. This wealth of "big data" lends itself to the application of artificial intelligence in the development of predictive and diagnostic models with potentially greater accuracy than standard biostatistics. As processing power of computing systems has improved and data are made more accessible through the large databases and electronic health record, these more contemporary techniques for analyzing and interpreting data have garnered much interest in the field of medicine. This review highlights the current evidence base for the use of artificial intelligence in hepatology, focusing particularly on the areas of diagnosis and prognosis of advanced chronic liver disease and hepatic neoplasia.
Collapse
Affiliation(s)
- Karl Vaz
- Department of Gastroenterology and Hepatology, Austin Health, Melbourne, Australia
| | - Thomas Goodwin
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia
| | - William Kemp
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia.,Central Clinical School, Monash University, Melbourne, Australia
| | - Stuart Roberts
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia.,Central Clinical School, Monash University, Melbourne, Australia
| | - Ammar Majeed
- Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia.,Central Clinical School, Monash University, Melbourne, Australia
| |
Collapse
|
|
36
|
Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
Collapse
Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
| |
Collapse
|
|
37
|
Cheng MQ, Xian MF, Tian WS, Li MD, Hu HT, Li W, Zhang JC, Huang Y, Xie XY, Lu MD, Kuang M, Wang W, Ruan SM, Chen LD. RGB Three-Channel SWE-Based Ultrasomics Model: Improving the Efficiency in Differentiating Focal Liver Lesions. Front Oncol 2021; 11:704218. [PMID: 34646763 PMCID: PMC8504873 DOI: 10.3389/fonc.2021.704218] [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: 05/02/2021] [Accepted: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
Objective To explore a new method for color image analysis of ultrasomics and investigate the efficiency in differentiating focal liver lesions (FLLs) by Red, Green, and Blue (RGB) three-channel SWE-based ultrasomics model. Methods One hundred thirty FLLs were randomly divided into training set (n = 65) and validation set (n = 65). The RGB three-channel and direct conversion methods were applied to the same color SWE images. Ultrasomics features were extracted from the preprocessing images establishing two feature data sets. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. Two models, named RGB model (based on RGB three-channel conversion) and direct model (based on direct conversion), were used to differentiate FLLs. The diagnosis performance of the two models was evaluated by area under the curve (AUC), calibration curves, decision curves, and net reclassification index (NRI). Results In the validation cohort, the AUC of the direct model and RGB model in characterization on FLLs were 0.813 and 0.926, respectively (p = 0.038). Calibration curves and decision curves indicated that the RGB model had better calibration efficiency and provided greater clinical benefits. NRI revealed that the RGB model correctly reclassified 7% of malignant cases and 25% of benign cases compared to the direct model (p = 0.01). Conclusion The RGB model generated by RGB three-channel method yielded better diagnostic efficiency than the direct model established by direct conversion method. The RGB three-channel method may be promising on ultrasomics analysis of color images in clinical application.
Collapse
Affiliation(s)
- Mei-Qing Cheng
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Meng-Fei Xian
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wen-Shuo Tian
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming-De Li
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hang-Tong Hu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wei Li
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Jian-Chao Zhang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yang Huang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiao-Yan Xie
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming-De Lu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.,Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming Kuang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.,Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wei Wang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Si-Min Ruan
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Li-Da Chen
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| |
Collapse
|
|
38
|
Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
Collapse
Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| |
Collapse
|
|
39
|
Dinani AM, Kowdley KV, Noureddin M. Application of Artificial Intelligence for Diagnosis and Risk Stratification in NAFLD and NASH: The State of the Art. Hepatology 2021; 74:2233-2240. [PMID: 33928671 DOI: 10.1002/hep.31869] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/24/2021] [Accepted: 04/23/2021] [Indexed: 12/17/2022]
Abstract
The diagnosis of nonalcoholic fatty liver disease and associated fibrosis is challenging given the lack of signs, symptoms and nonexistent diagnostic test. Furthermore, follow up and treatment decisions become complicated with a lack of a simple reproducible method to follow these patients longitudinally. Liver biopsy is the current standard to detect, risk stratify and monitor individuals with nonalcoholic fatty liver disease. However, this method is an unrealistic option in a population that affects about one in three to four individuals worldwide. There is an urgency to develop innovative methods to facilitate management at key points in an individual's journey with nonalcoholic fatty liver disease fibrosis. Artificial intelligence is an exciting field that has the potential to achieve this. In this review, we highlight applications of artificial intelligence by leveraging our current knowledge of nonalcoholic fatty liver disease to diagnose and risk stratify NASH phenotypes.
Collapse
Affiliation(s)
- Amreen M Dinani
- Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kris V Kowdley
- Liver Institute Northwest, Seattle, WA; Elson S. Floyd College of Medicine, Washington State University, WA
| | - Mazen Noureddin
- Division of Digestive and Liver Diseases, Cedar Sinai Medical Center, Los Angeles, CA
| |
Collapse
|
|
40
|
Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
Collapse
Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
| | | | | |
Collapse
|
|
41
|
Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology 2021; 73:2546-2563. [PMID: 33098140 DOI: 10.1002/hep.31603] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 09/15/2020] [Accepted: 09/29/2020] [Indexed: 12/11/2022]
Abstract
Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.
Collapse
Affiliation(s)
- Joseph C Ahn
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
| | | | | | | | - Vijay H Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
| |
Collapse
|
|
42
|
Kim YH. Artificial intelligence in medical ultrasonography: driving on an unpaved road. Ultrasonography 2021; 40:313-317. [PMID: 34053212 PMCID: PMC8217795 DOI: 10.14366/usg.21031] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022] Open
Affiliation(s)
- Young H Kim
- Department of Radiology, University of Massachusetts Medical School, Worcester, MA, USA
| |
Collapse
|
|
43
|
Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27:1664-1690. [PMID: 33967550 PMCID: PMC8072192 DOI: 10.3748/wjg.v27.i16.1664] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/11/2021] [Accepted: 03/17/2021] [Indexed: 02/06/2023] Open
Abstract
Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.
Collapse
Affiliation(s)
- Jia-Sheng Cao
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Zi-Yi Lu
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Ming-Yu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Bin Zhang
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Sarun Juengpanich
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Jia-Hao Hu
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Shi-Jie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Win Topatana
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xue-Yin Zhou
- School of Medicine, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
| | - Xu Feng
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Ji-Liang Shen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Yu Liu
- College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xiu-Jun Cai
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| |
Collapse
|
|
44
|
Kumar R, Khan FU, Sharma A, Aziz IB, Poddar NK. Recent Applications of Artificial Intelligence in detection of Gastrointestinal, Hepatic and Pancreatic Diseases. Curr Med Chem 2021; 29:66-85. [PMID: 33820515 DOI: 10.2174/0929867328666210405114938] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/25/2021] [Accepted: 03/06/2021] [Indexed: 11/22/2022]
Abstract
There is substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remotely health monitoring using sensors and smartphones. A variety of AI-based prediction models available for the gastrointestinal inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, electronic medical records for hepatitis-associated fibrosis, pancreatic carcinoma using endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patient's treatment using multiple factors. Although enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitation of AI techniques in such disease prognosis, risk assessment, and decision support are discussed.
Collapse
Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Uttar Pradesh. India
| | - Farhat Ullah Khan
- Computer and Information Sciences Department, Universiti Teknologi Petronas, 32610, Seri Iskander, Perak. Malaysia
| | - Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology, Allahabad, Uttar Pradesh. India
| | - Izzatdin Ba Aziz
- Computer and Information Sciences Department, Universiti Teknologi Petronas, 32610, Seri Iskander, Perak. Malaysia
| | | |
Collapse
|
|
45
|
Borro P, Ziola S, Pasta A, Trombini M, Labanca S, Marenco S, Solarna D, Pisciotta L, Baldissarro I, Picciotto A, Dellepiane S. Hepatic Elastometry and Glissonian Line in the Assessment of Liver Fibrosis. ULTRASOUND IN MEDICINE & BIOLOGY 2021; 47:947-959. [PMID: 33451815 DOI: 10.1016/j.ultrasmedbio.2020.12.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 06/12/2023]
Abstract
The aim of this study was to identify a method for staging hepatic fibrosis using a non-invasive, rapid and inexpensive technique based on ultrasound morphologic hepatic features. A total of 215 patients with different liver diseases underwent B-mode (2-D brightness mode) ultrasonography, vibration-controlled transient elastography, 2-D shear wave elastography and measurement of the controlled attenuation parameter with transient elastography. B-Mode images of the anterior margin of the left lobe were obtained and processed with automatic Genoa Line Quantification (GLQ) software based on a neural network for staging liver fibrosis. The accuracy of GLQ was 90.6% during model training and 78.9% in 38 different patients with concordant elastometric measures. Receiver operating characteristic curve analysis of GLQ performance using vibration-controlled transient elastography as a reference yielded areas under the curves of 0.851 for F ≥ F1, 0.793 for F ≥ F2, 0.784 for F ≥ F3 and 0.789 for F ≥ F4. GLQ has the potential to be a rapid, easy-to-perform and tolerable method in the staging of liver fibrosis.
Collapse
Affiliation(s)
- Paolo Borro
- Gastroenterology Unit, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy.
| | - Sebastiano Ziola
- Gastroenterology Unit, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy
| | - Andrea Pasta
- Department of Internal Medicine, University of Genoa, Genoa, Italy
| | - Marco Trombini
- Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy
| | - Sara Labanca
- Gastroenterology Unit, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy
| | - Simona Marenco
- Gastroenterology Unit, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy
| | - David Solarna
- Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy
| | - Livia Pisciotta
- Department of Internal Medicine, University of Genoa, Genoa, Italy; Dietetics and Clinical Nutrition Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Antonino Picciotto
- Gastroenterology Unit, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy
| | - Silvana Dellepiane
- Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy
| |
Collapse
|
|
46
|
Maruyama H, Yamaguchi T, Nagamatsu H, Shiina S. AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound. Diagnostics (Basel) 2021; 11:diagnostics11020292. [PMID: 33673229 PMCID: PMC7918339 DOI: 10.3390/diagnostics11020292] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/03/2021] [Accepted: 02/10/2021] [Indexed: 02/07/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a common cancer worldwide. Recent international guidelines request an identification of the stage and patient background/condition for an appropriate decision for the management direction. Radiomics is a technology based on the quantitative extraction of image characteristics from radiological imaging modalities. Artificial intelligence (AI) algorithms are the principal axis of the radiomics procedure and may provide various results from large data sets beyond conventional techniques. This review article focused on the application of the radiomics-related diagnosis of HCC using radiological imaging (computed tomography, magnetic resonance imaging, and ultrasound (B-mode, contrast-enhanced ultrasound, and elastography)), and discussed the current role, limitation and future of ultrasound. Although the evidence has shown the positive effect of AI-based ultrasound in the prediction of tumor characteristics and malignant potential, posttreatment response and prognosis, there are still a number of issues in the practical management of patients with HCC. It is highly expected that the wide range of applications of AI for ultrasound will support the further improvement of the diagnostic ability of HCC and provide a great benefit to the patients.
Collapse
Affiliation(s)
- Hitoshi Maruyama
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
- Correspondence: ; Tel.: +81-3-38133111; Fax: +81-3-56845960
| | - Tadashi Yamaguchi
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoicho, Inage, Chiba 263-8522, Japan;
| | - Hiroaki Nagamatsu
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
| | - Shuichiro Shiina
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
| |
Collapse
|
|
47
|
Mohapatra S, Swarnkar T, Mishra M, Al-Dabass D, Mascella R. Deep learning in gastroenterology. HANDBOOK OF COMPUTATIONAL INTELLIGENCE IN BIOMEDICAL ENGINEERING AND HEALTHCARE 2021:121-149. [DOI: 10.1016/b978-0-12-822260-7.00001-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
|
|
48
|
Praveen AD, Vital TP, Jayaram D, Satyanarayana LV. Intelligent Liver Disease Prediction (ILDP) System Using Machine Learning Models. LECTURE NOTES IN ELECTRICAL ENGINEERING 2021:609-625. [DOI: 10.1007/978-981-15-8439-8_50] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
|
|
49
|
Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Koda H, Miyano A, Fumihara D. Current status of artificial intelligence analysis for endoscopic ultrasonography. Dig Endosc 2021; 33:298-305. [PMID: 33098123 DOI: 10.1111/den.13880] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 10/11/2020] [Accepted: 10/18/2020] [Indexed: 12/12/2022]
Abstract
Endoscopic ultrasonography (EUS) is an essential diagnostic tool for various types of pancreatic diseases such as pancreatic tumors and chronic pancreatitis; however, EUS imaging has low specificity for the diagnosis of pancreatic diseases. Artificial intelligence (AI) is a mathematical prediction technique that automates learning and recognizes patterns in data. This review describes the details and principles of AI and deep learning algorithms. The term AI does not have any definite definition; almost all AI systems fall under narrow AI, which can handle single or limited tasks. Deep learning is based on neural networks, which is a machine learning technique that is widely used in the medical field. Deep learning involves three phases: data collection and annotation, building the deep learning architecture, and training and ability validation. For medical image diagnosis, image classification, object detection, and semantic segmentation are performed. In EUS, AI is used for detecting anatomical features, differential pancreatic tumors, and cysts. For this, conventional machine learning architectures are used, and deep learning architecture has been used in only two reports. Although the diagnostic abilities in these reports were about 85-95%, these were exploratory research and very few reports have included substantial evidence. AI is increasingly being used for medical image diagnosis due to its high performance and will soon become an essential technique for medical diagnosis.
Collapse
Affiliation(s)
- Takamichi Kuwahara
- Department of Gastroenterology, Aichi Cancer Center Hospital, Aichi, Japan
| | - Kazuo Hara
- Department of Gastroenterology, Aichi Cancer Center Hospital, Aichi, Japan
| | - Nobumasa Mizuno
- Department of Gastroenterology, Aichi Cancer Center Hospital, Aichi, Japan
| | - Shin Haba
- Department of Gastroenterology, Aichi Cancer Center Hospital, Aichi, Japan
| | - Nozomi Okuno
- Department of Gastroenterology, Aichi Cancer Center Hospital, Aichi, Japan
| | - Hiroki Koda
- Department of Gastroenterology, Aichi Cancer Center Hospital, Aichi, Japan
| | - Akira Miyano
- Department of Gastroenterology, Aichi Cancer Center Hospital, Aichi, Japan
| | - Daiki Fumihara
- Department of Gastroenterology, Aichi Cancer Center Hospital, Aichi, Japan
| |
Collapse
|
|
50
|
Nishida N, Kudo M. Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor. Front Oncol 2020; 10:594580. [PMID: 33409151 PMCID: PMC7779763 DOI: 10.3389/fonc.2020.594580] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/16/2020] [Indexed: 12/15/2022] Open
Abstract
Recent advancement in artificial intelligence (AI) facilitate the development of AI-powered medical imaging including ultrasonography (US). However, overlooking or misdiagnosis of malignant lesions may result in serious consequences; the introduction of AI to the imaging modalities may be an ideal solution to prevent human error. For the development of AI for medical imaging, it is necessary to understand the characteristics of modalities on the context of task setting, required data sets, suitable AI algorism, and expected performance with clinical impact. Regarding the AI-aided US diagnosis, several attempts have been made to construct an image database and develop an AI-aided diagnosis system in the field of oncology. Regarding the diagnosis of liver tumors using US images, 4- or 5-class classifications, including the discrimination of hepatocellular carcinoma (HCC), metastatic tumors, hemangiomas, liver cysts, and focal nodular hyperplasia, have been reported using AI. Combination of radiomic approach with AI is also becoming a powerful tool for predicting the outcome in patients with HCC after treatment, indicating the potential of AI for applying personalized medical care. However, US images show high heterogeneity because of differences in conditions during the examination, and a variety of imaging parameters may affect the quality of images; such conditions may hamper the development of US-based AI. In this review, we summarized the development of AI in medical images with challenges to task setting, data curation, and focus on the application of AI for the managements of liver tumor, especially for US diagnosis.
Collapse
Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
| |
Collapse
|