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Botnari A, Kadar M, Patrascu JM. A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2024; 14:1090. [PMID: 38893617 PMCID: PMC11172202 DOI: 10.3390/diagnostics14111090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
OBJECTIVES This study delves into the cutting-edge field of deep learning techniques, particularly deep convolutional neural networks (DCNNs), which have demonstrated unprecedented potential in assisting radiologists and orthopedic surgeons in precisely identifying meniscal tears. This research aims to evaluate the effectiveness of deep learning models in recognizing, localizing, describing, and categorizing meniscal tears in magnetic resonance images (MRIs). MATERIALS AND METHODS This systematic review was rigorously conducted, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Extensive searches were conducted on MEDLINE (PubMed), Web of Science, Cochrane Library, and Google Scholar. All identified articles underwent a comprehensive risk of bias analysis. Predictive performance values were either extracted or calculated for quantitative analysis, including sensitivity and specificity. The meta-analysis was performed for all prediction models that identified the presence and location of meniscus tears. RESULTS This study's findings underscore that a range of deep learning models exhibit robust performance in detecting and classifying meniscal tears, in one case surpassing the expertise of musculoskeletal radiologists. Most studies in this review concentrated on identifying tears in the medial or lateral meniscus and even precisely locating tears-whether in the anterior or posterior horn-with exceptional accuracy, as demonstrated by AUC values ranging from 0.83 to 0.94. CONCLUSIONS Based on these findings, deep learning models have showcased significant potential in analyzing knee MR images by learning intricate details within images. They offer precise outcomes across diverse tasks, including segmenting specific anatomical structures and identifying pathological regions. Contributions: This study focused exclusively on DL models for identifying and localizing meniscus tears. It presents a meta-analysis that includes eight studies for detecting the presence of a torn meniscus and a meta-analysis of three studies with low heterogeneity that localize and classify the menisci. Another novelty is the analysis of arthroscopic surgery as ground truth. The quality of the studies was assessed against the CLAIM checklist, and the risk of bias was determined using the QUADAS-2 tool.
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Affiliation(s)
- Alexei Botnari
- Department of Orthopedics, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Manuella Kadar
- Department of Computer Science, Faculty of Informatics and Engineering, “1 Decembrie 1918” University of Alba Iulia, 510009 Alba Iulia, Romania
| | - Jenel Marian Patrascu
- Department of Orthopedics-Traumatology, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania;
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Desai V. The Future of Artificial Intelligence in Sports Medicine and Return to Play. Semin Musculoskelet Radiol 2024; 28:203-212. [PMID: 38484772 DOI: 10.1055/s-0043-1778019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Artificial intelligence (AI) has shown tremendous growth over the last decade, with the more recent development of clinical applications in health care. The ability of AI to synthesize large amounts of complex data automatically allows health care providers to access previously unavailable metrics and thus enhance and personalize patient care. These innovations include AI-assisted diagnostic tools, prediction models for each treatment pathway, and various tools for workflow optimization. The extension of AI into sports medicine is still early, but numerous AI-driven algorithms, devices, and research initiatives have delved into predicting and preventing athlete injury, aiding in injury assessment, optimizing recovery plans, monitoring rehabilitation progress, and predicting return to play.
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Affiliation(s)
- Vishal Desai
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania
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Wang MX, Kim JK, Choi JW, Park D, Chang MC. Deep learning algorithm for automatically measuring Cobb angle in patients with idiopathic scoliosis. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2024:10.1007/s00586-023-08024-5. [PMID: 38367024 DOI: 10.1007/s00586-023-08024-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/30/2023] [Accepted: 10/25/2023] [Indexed: 02/19/2024]
Abstract
PURPOSE The Cobb angle is a standard measurement to qualify and track the progression of scoliosis. However, the Cobb angle has high inter- and intra-observer variability. Consequently, its measurement varies with vertebrae and may even differ when the same vertebra is measured. Therefore, it is not constant and differs with measurements. This study aimed to develop a deep learning model that automatically measures the Cobb angle. The deep learning model for identifying vertebrae on spine radiographs was developed. METHODS The dataset consisted of 297 images that were divided into two subsets for training and validation. Two hundred and twenty-seven images (76.4%) were used to train the model, while 70 images (23.6%) were used as the validation dataset. Absolut error between the measurements by the observer and developed deep learning model and intraclass correlation coefficient (ICC). RESULTS The average absolute error between the measurements was 1.97° with a standard deviation of 1.57°. In addition, 95.9% of the angles had an absolute error of less than 5°. The ICC was calculated to assess the model's reliability further. The ICC was 0.981, indicating excellent reliability. CONCLUSIONS The authors believe the model will be useful in clinical practice by relieving clinicians of the burden of having to manually compute the Cobb angle. Further studies are needed to enhance the accuracy and versatility of this deep learning model.
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Affiliation(s)
- Ming Xing Wang
- Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-Si, Republic of Korea
| | - Jeoung Kun Kim
- Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-Si, Republic of Korea
| | - Jin-Woo Choi
- Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea
| | - Donghwi Park
- Department of Rehabilitation Medicine, Daegu Fatima Hospital, Ayangro 99, Dong Gu, Daegu, 41199, Republic of Korea.
| | - Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, 317-1, Daemyungdong, Namku, Daegu, 705-717, Republic of Korea.
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Oeding JF, Krych AJ, Pearle AD, Kelly BT, Kunze KN. Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation. Arthroscopy 2024:S0749-8063(24)00099-9. [PMID: 38325497 DOI: 10.1016/j.arthro.2024.01.043] [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] [Received: 01/17/2024] [Revised: 01/21/2024] [Accepted: 01/29/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledge and translational gaps in the literature to provide insight into specific areas for improvement as adoption of this technology continues. METHODS A comprehensive search of the literature was performed in December 2023 for articles regarding the use of DL in sports medicine. For each study, information regarding the joint of focus, specific anatomic structure/pathology to which DL was applied, imaging modality utilized, source of images used for model training and testing, data set size, model performance, and whether the DL model was externally validated was recorded. A numerical scale was used to rate each DL model's clinical impact, with 1 corresponding to proof-of-concept studies with little to no direct clinical impact and 5 corresponding to practice-changing clinical impact and ready for clinical deployment. RESULTS Fifty-five studies were identified, all of which were published within the past 5 years, while 82% were published within the past 3 years. Of the DL models identified, 84% were developed for classification tasks, 9% for automated measurements, and 7% for segmentation. A total of 62% of studies utilized magnetic resonance imaging as the imaging modality, 25% radiographs, and 7% ultrasound, while 1 study each used computed tomography, arthroscopic images, or arthroscopic video. Sixty-five percent of studies focused on the detection of tears (anterior cruciate ligament [ACL], rotator cuff [RC], and meniscus). The diagnostic performance of ACL tears, as determined by the area under the receiver operator curve (AUROC), ranged from 0.81 to 0.99 for ACL tears (excellent to near perfect), 0.83 to 0.94 for RC tears (excellent), and from 0.75 to 0.96 for meniscus tears (acceptable to excellent). In addition, 3 studies focused on detection of cartilage lesions had AUROC ranging from 0.90 to 0.92 (excellent performance). However, only 4 (7%) studies externally validated their models, suggesting that they may not be generalizable or may not perform well when applied to populations other than that used to develop the model. Finally, the mean clinical impact score was 2 (range, 1-3) on scale of 1 to 5, corresponding to limited clinical applicability. CONCLUSIONS DL models in orthopaedic sports medicine show generally excellent performance (high internal validity) but require external validation to facilitate clinical deployment. In addition, current models have low clinical applicability and fail to advance the field due to a focus on routine tasks and a narrow conceptual framework. LEVEL OF EVIDENCE Level IV, scoping review of Level I to IV studies.
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Affiliation(s)
- Jacob F Oeding
- Mayo Clinic Alix School of Medicine, Rochester, Minnesota, U.S.A
| | - Aaron J Krych
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, Minnesota, U.S.A
| | - Andrew D Pearle
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, U.S.A
| | - Bryan T Kelly
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, U.S.A
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, U.S.A..
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Kasuya S, Inaoka T, Wada A, Nakatsuka T, Nakagawa K, Terada H. Feasibility of the fat-suppression image-subtraction method using deep learning for abnormality detection on knee MRI. Pol J Radiol 2023; 88:e562-e573. [PMID: 38362017 PMCID: PMC10867951 DOI: 10.5114/pjr.2023.133660] [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: 04/25/2023] [Accepted: 09/04/2023] [Indexed: 02/17/2024] Open
Abstract
Purpose To evaluate the feasibility of using a deep learning (DL) model to generate fat-suppression images and detect abnormalities on knee magnetic resonance imaging (MRI) through the fat-suppression image-subtraction method. Material and methods A total of 45 knee MRI studies in patients with knee disorders and 12 knee MRI studies in healthy volunteers were enrolled. The DL model was developed using 2-dimensional convolutional neural networks for generating fat-suppression images and subtracting generated fat-suppression images without any abnormal findings from those with normal/abnormal findings and detecting/classifying abnormalities on knee MRI. The image qualities of the generated fat-suppression images and subtraction-images were assessed. The accuracy, average precision, average recall, F-measure, sensitivity, and area under the receiver operator characteristic curve (AUROC) of DL for each abnormality were calculated. Results A total of 2472 image datasets, each consisting of one slice of original T1WI, original intermediate-weighted images, generated fat-suppression (FS)-intermediate-weighted images without any abnormal findings, generated FS-intermediate-weighted images with normal/abnormal findings, and subtraction images between the generated FS-intermediate-weighted images at the same cross-section, were created. The generated fat-suppression images were of adequate image quality. Of the 2472 subtraction-images, 2203 (89.1%) were judged to be of adequate image quality. The accuracies for overall abnormalities, anterior cruciate ligament, bone marrow, cartilage, meniscus, and others were 89.5-95.1%. The average precision, average recall, and F-measure were 73.4-90.6%, 77.5-89.4%, and 78.4-89.4%, respectively. The sensitivity was 57.4-90.5%. The AUROCs were 0.910-0.979. Conclusions The DL model was able to generate fat-suppression images of sufficient quality to detect abnormalities on knee MRI through the fat-suppression image-subtraction method.
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Affiliation(s)
- Shusuke Kasuya
- Department of Radiology, Toho University Sakura Medical Center, Sakura, Japan
| | - Tsutomu Inaoka
- Department of Radiology, Toho University Sakura Medical Center, Sakura, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Tomoya Nakatsuka
- Department of Radiology, Toho University Sakura Medical Center, Sakura, Japan
| | - Koichi Nakagawa
- Department of Orthopaedic Surgery, Toho University Sakura Medical Center, Sakura, Japan
| | - Hitoshi Terada
- Department of Radiology, Toho University Sakura Medical Center, Sakura, Japan
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Ma Y, Qin Y, Liang C, Li X, Li M, Wang R, Yu J, Xu X, Lv S, Luo H, Jiang Y. Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury. Diagnostics (Basel) 2023; 13:2049. [PMID: 37370944 DOI: 10.3390/diagnostics13122049] [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: 05/03/2023] [Revised: 05/25/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
OBJECTIVE The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. METHODS We presented a cascaded-progressive convolutional neural network (C-PCNN) method for diagnosing meniscus injuries using magnetic resonance imaging (MRI). A total of 1396 images collected in the hospital were used for training and testing. The method used for training and testing was 5-fold cross validation. Using intraoperative arthroscopic diagnosis and MRI diagnosis as criteria, the C-PCNN was evaluated based on accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and evaluation performance. At the same time, the diagnostic accuracy of doctors with the assistance of cascade- progressive convolutional neural networks was evaluated. The diagnostic accuracy of a C-PCNN assistant with an attending doctor and chief doctor was compared to evaluate the clinical significance. RESULTS C-PCNN showed 85.6% accuracy in diagnosing and identifying anterior horn injury, and 92% accuracy in diagnosing and identifying posterior horn injury. The average accuracy of C-PCNN was 89.8%, AUC = 0.86. The diagnosis accuracy of the attending physician with the aid of the C-PCNN was comparable to that of the chief physician. CONCLUSION The C-PCNN-based MRI technique for diagnosing knee meniscus injuries has significant practical value in clinical practice. With a high rate of accuracy, clinical auxiliary physicians can increase the speed and accuracy of diagnosis and decrease the number of incorrect diagnoses.
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Affiliation(s)
- Yingkai Ma
- Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, China
| | - Yong Qin
- Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, China
| | - Chen Liang
- Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, China
| | - Xiang Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Haerbin 150001, China
| | - Minglei Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Haerbin 150001, China
| | - Ren Wang
- Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, China
| | - Jinping Yu
- Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, China
| | - Xiangning Xu
- Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, China
| | - Songcen Lv
- Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Haerbin 150001, China
| | - Hao Luo
- Department of Control Science and Engineering, Harbin Institute of Technology, Haerbin 150001, China
| | - Yuchen Jiang
- Department of Control Science and Engineering, Harbin Institute of Technology, Haerbin 150001, China
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Iqbal S, N. Qureshi A, Li J, Mahmood T. On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3173-3233. [PMID: 37260910 PMCID: PMC10071480 DOI: 10.1007/s11831-023-09899-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/19/2023] [Indexed: 06/02/2023]
Abstract
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.
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Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
- Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Tariq Mahmood
- Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer & Information Sciences (CCIS), Prince Sultan University, Riyadh, 11586 Kingdom of Saudi Arabia
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Chang MC, Kim JK, Park D. The Application of Artificial Intelligence in the Field of Rehabilitation. Am J Phys Med Rehabil 2023; 102:e58-e59. [PMID: 36228233 DOI: 10.1097/phm.0000000000002121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Xu SM, Dong D, Li W, Bai T, Zhu MZ, Gu GS. Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements. World J Clin Cases 2023; 11:1477-1487. [PMID: 36926411 PMCID: PMC10011995 DOI: 10.12998/wjcc.v11.i7.1477] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/27/2023] [Accepted: 02/13/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND Femoral trochlear dysplasia (FTD) is an important risk factor for patellar instability. Dejour classification is widely used at present and relies on standard lateral X-rays, which are not common in clinical work. Therefore, magnetic resonance imaging (MRI) has become the first choice for the diagnosis of FTD. However, manually measuring is tedious, time-consuming, and easily produces great variability.
AIM To use artificial intelligence (AI) to assist diagnosing FTD on MRI images and to evaluate its reliability.
METHODS We searched 464 knee MRI cases between January 2019 and December 2020, including FTD (n = 202) and normal trochlea (n = 252). This paper adopts the heatmap regression method to detect the key points network. For the final evaluation, several metrics (accuracy, sensitivity, specificity, etc.) were calculated.
RESULTS The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the AI model ranged from 0.74-0.96. All values were superior to junior doctors and intermediate doctors, similar to senior doctors. However, diagnostic time was much lower than that of junior doctors and intermediate doctors.
CONCLUSION The diagnosis of FTD on knee MRI can be aided by AI and can be achieved with a high level of accuracy.
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Affiliation(s)
- Sheng-Ming Xu
- Department of Orthopedic Surgery, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
| | - Dong Dong
- Department of Radiology, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
| | - Wei Li
- Department of Orthopedic Surgery, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
| | - Tian Bai
- College of Computer Science and Technology, Jilin University, Changchun 130000, Jilin Province, China
| | - Ming-Zhu Zhu
- College of Computer Science and Technology, Jilin University, Changchun 130000, Jilin Province, China
| | - Gui-Shan Gu
- Department of Orthopedic Surgery, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
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Choo YJ, Chang MC. Use of Machine Learning in Stroke Rehabilitation: A Narrative Review. BRAIN & NEUROREHABILITATION 2022; 15:e26. [PMID: 36742082 PMCID: PMC9833483 DOI: 10.12786/bn.2022.15.e26] [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: 07/01/2022] [Revised: 09/09/2022] [Accepted: 10/07/2022] [Indexed: 12/05/2022] Open
Abstract
A narrative review was conducted of machine learning applications and research in the field of stroke rehabilitation. The machine learning models commonly used in medical research include random forest, logistic regression, and deep neural networks. Convolutional neural networks (CNNs), a type of deep neural network, are typically used for image analysis. Machine learning has been used in stroke rehabilitation to predict recovery of motor function using a large amount of clinical data as input. Recent studies on predicting motor function have trained CNN models using magnetic resonance images as input data together with clinical data to increase the accuracy of motor function prediction models. Additionally, a model interpreting videofluoroscopic swallowing studies was developed and investigated. In the future, we anticipate that machine learning will be actively used to treat stroke patients, such as predicting the occurrence of depression and the recovery of language, cognitive, and sensory function, as well as prescribing appropriate rehabilitation treatments.
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Affiliation(s)
- Yoo Jin Choo
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, Korea
| | - Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, Korea
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