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Systematic review of artificial intelligence development and evaluation for MRI diagnosis of knee ligament or meniscus tears

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Skeletal Radiology Aims and scope Submit manuscript

Abstract

Objective

The purpose of this systematic review was to summarize the results of original research studies evaluating the characteristics and performance of deep learning models for detection of knee ligament and meniscus tears on MRI.

Materials and Methods

We searched PubMed for studies published as of February 2, 2022 for original studies evaluating development and evaluation of deep learning models for MRI diagnosis of knee ligament or meniscus tears. We summarized study details according to multiple criteria including baseline article details, model creation, deep learning details, and model evaluation.

Results

19 studies were included with radiology departments leading the publications in deep learning development and implementation for detecting knee injuries via MRI. Among the studies, there was a lack of standard reporting and inconsistently described development details. However, all included studies reported consistently high model performance that significantly supplemented human reader performance.

Conclusion

From our review, we found radiology departments have been leading deep learning development for injury detection on knee MRIs. Although studies inconsistently described DL model development details, all reported high model performance, indicating great promise for DL in knee MRI analysis.

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References

  1. Benjaminse A, Gokeler A, van der Schans CP. Clinical diagnosis of an anterior cruciate ligament rupture: a meta-analysis. J Orthop Sports Phys Ther. 2006;36:267–88.

    Article  PubMed  Google Scholar 

  2. Mulligan EP, Harwell JL, Robertson WJ. Reliability and diagnostic accuracy of the Lachman test performed in a prone position. J Orthop Sports Phys Ther. 2011;41:749–57.

    Article  PubMed  Google Scholar 

  3. Rosenkrantz AB, Hughes DR, Duszak R. The U.S. Radiologist Workforce: An Analysis of Temporal and Geographic Variation by Using Large National Datasets. Radiology. 2016;279:175–84.

    Article  PubMed  Google Scholar 

  4. Mollura DJ, Culp MP, Pollack E, Battino G, Scheel JR, Mango VL, et al. Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology. Radiology. 2020;297:513–20.

    Article  PubMed  Google Scholar 

  5. Kunze KN, Rossi DM, White GM, Karhade AV, Deng J, Williams BT, et al. Diagnostic Performance of Artificial Intelligence for Detection of Anterior Cruciate Ligament and Meniscus Tears: A Systematic Review. Arthroscopy: J Arthrosc Relat Surg. 2021;37:771–81.

    Article  Google Scholar 

  6. Langerhuizen DWG, Janssen SJ, Mallee WH, van den Bekerom MPJ, Ring D, Kerkhoffs GMMJ, et al. What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review. Clin Orthop Relat Res. 2019;477:2482–91.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Guermazi A, Tannoury C, Kompel AJ, Murakami AM, Ducarouge A, Gillibert A, et al. Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence. Radiology. 2022;302:627–36.

    Article  PubMed  Google Scholar 

  8. Shin Y, Kim S, Lee YH. AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency? Skeletal Radiol. 2022;51:293–304.

    Article  PubMed  Google Scholar 

  9. Gorelik N, Gyftopoulos S. Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report. Can Assoc Radiol J. 2021;72:45–59.

    Article  PubMed  Google Scholar 

  10. Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med. 2018;15:e1002699.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Liu F, Guan B, Zhou Z, Samsonov A, Rosas H, Lian K, et al. Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning. Radiol: Artif Intell. 2019;1:180091.

    PubMed  Google Scholar 

  12. Li Z, Ren S, Zhou R, Jiang X, You T, Li C, et al. Deep Learning-Based Magnetic Resonance Imaging Image Features for Diagnosis of Anterior Cruciate Ligament Injury. J Healthc Eng. 2021;2021:e4076175.

    Google Scholar 

  13. Ren M, Yi PH. Artificial intelligence in orthopedic implant model classification: a systematic review. Skeletal Radiol. 2022;51:407–16.

    Article  PubMed  Google Scholar 

  14. Pedoia V, Norman B, Mehany SN, Bucknor MD, Link TM, Majumdar S. 3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects. J Magn Reson Imaging. 2019;49:400–10.

    Article  PubMed  Google Scholar 

  15. Chang PD, Wong TT, Rasiej MJ. Deep Learning for Detection of Complete Anterior Cruciate Ligament Tear. J Digit Imaging. 2019;32:980–6.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Roblot V, Giret Y, Bou Antoun M, Morillot C, Chassin X, Cotten A, et al. Artificial intelligence to diagnose meniscus tears on MRI. Diagn Interv Imaging. 2019;100:243–9.

    Article  PubMed  CAS  Google Scholar 

  17. Germann C, Marbach G, Civardi F, Fucentese SF, Fritz J, Sutter R, et al. Deep Convolutional Neural Network-Based Diagnosis of Anterior Cruciate Ligament Tears: Performance Comparison of Homogenous Versus Heterogeneous Knee MRI Cohorts With Different Pulse Sequence Protocols and 1.5-T and 3-T Magnetic Field Strengths. Invest Radiol. 2020;55:499–506.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Fritz B, Marbach G, Civardi F, Fucentese SF, Pfirrmann CWA. Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference. Skeletal Radiol. 2020;49:1207–17.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhang L, Li M, Zhou Y, Lu G, Zhou Q. Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard. J Magn Reson Imaging. 2020;52:1745–52.

    Article  PubMed  Google Scholar 

  20. Namiri NK, Flament I, Astuto B, Shah R, Tibrewala R, Caliva F, et al. Deep Learning for Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries from MRI. Radiol: Artif Intell. 2020;2:e190207.

    PubMed  Google Scholar 

  21. Rizk B, Brat H, Zille P, Guillin R, Pouchy C, Adam C, et al. Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation. Phys Med. 2021;83:64–71.

    Article  PubMed  CAS  Google Scholar 

  22. Astuto B, Flament I, Namiri KN, Shah R, Bharadwaj U, Link MT, et al. Automatic Deep Learning-assisted Detection and Grading of Abnormalities in Knee MRI Studies. Radiol Artif Intell. 2021;3:e200165.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zarandi MHF, Khadangi A, Karimi F, Turksen IB. A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear. J Digit Imaging. 2016;29:677–95.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Couteaux V, Si-Mohamed S, Nempont O, Lefevre T, Popoff A, Pizaine G, et al. Automatic knee meniscus tear detection and orientation classification with Mask-RCNN. Diagn Interv Imaging. 2019;100:235–42.

    Article  PubMed  CAS  Google Scholar 

  25. Awan MJ, Rahim MSM, Salim N, Mohammed MA, Garcia-Zapirain B, Abdulkareem KH. Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach. Diagnostics. 2021;11:105.

    Article  PubMed  Google Scholar 

  26. Jeon Y, Yoshino K, Hagiwara S, Watanabe A, Quek ST, Yoshioka H, et al. Interpretable and Lightweight 3-D Deep Learning Model for Automated ACL Diagnosis. IEEE J Biomed Health Inform. 2021;25:2388–97.

    Article  PubMed  Google Scholar 

  27. Awan MJ, Rahim MSM, Salim N, Rehman A, Nobanee H, Shabir H. Improved Deep Convolutional Neural Network to Classify Osteoarthritis from Anterior Cruciate Ligament Tear Using Magnetic Resonance Imaging. J Personalized Med. 2021;11:1163.

    Article  Google Scholar 

  28. Tack A, Shestakov A, Lüdke D, Zachow S. A Multi-Task Deep Learning Method for Detection of Meniscal Tears in MRI Data from the Osteoarthritis Initiative Database. Front Bioeng Biotechnol. 2021;9:747217.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Qiu X, Liu Z, Zhuang M, Cheng D, Zhu C, Zhang X. Fusion of CNN1 and CNN2-based magnetic resonance image diagnosis of knee meniscus injury and a comparative analysis with computed tomography. Comput Methods Programs Biomed. 2021;211:106297.

    Article  PubMed  Google Scholar 

  30. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2:35.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Santomartino SM, Siegel E, Yi PH. Academic Radiology Departments Should Lead Artificial Intelligence Initiatives. Acad Radiol. 2023;30:971–4.

    Article  PubMed  Google Scholar 

  32. Venkatesh K, Santomartino SM, Sulam J, Yi PH. Code and Data Sharing Practices in the Radiology Artificial Intelligence Literature: A Meta-Research Study. Radiol Artif Intell. 2022;4:e220081.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Saba L, Biswas M, Kuppili V, Cuadrado Godia E, Suri HS, Edla DR, et al. The present and future of deep learning in radiology. Eur J Radiol. 2019;114:14–24.

    Article  PubMed  Google Scholar 

  34. Kiseleva A, Kotzinos D, De Hert P. Transparency of AI in Healthcare as a Multilayered System of Accountabilities: Between Legal Requirements and Technical Limitations. Front Artif Intell. 2022;5:879603.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Collins GS, de Groot JA, Dutton S, Omar O, Shanyinde M, Tajar A, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol. 2014;14:40.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Bluemke DA, Moy L, Bredella MA, Ertl-Wagner BB, Fowler KJ, Goh VJ, et al. Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board. Radiology. 2020;294:487–9.

    Article  PubMed  Google Scholar 

  37. Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell. 2020;2:e200029.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Senders JT, Arnaout O, Karhade AV, Dasenbrock HH, Gormley WB, Broekman ML, et al. Natural and Artificial Intelligence in Neurosurgery: A Systematic Review. Neurosurgery. 2018;83:181–92.

    Article  PubMed  Google Scholar 

  39. Santomartino SM, Yi PH. Systematic Review of Radiologist and Medical Student Attitudes on the Role and Impact of AI in Radiology. Acad Radiol. 2022;29:1748–56.

    Article  PubMed  Google Scholar 

  40. Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med. 2000;19:453–73.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

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Authors and Affiliations

  1. Drexel University College of Medicine, Philadelphia, PA, USA

    Samantha M. Santomartino

  2. University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, University of Maryland School of Medicine, Baltimore, MD, USA

    Samantha M. Santomartino & Paul H. Yi

  3. Department of Orthopaedic Surgery, University of South Carolina, Columbia, SC, USA

    Justin Kung

  4. Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore Street First Floor Rm. 1172, Baltimore, MD, 21201, USA

    Paul H. Yi

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  1. Samantha M. Santomartino
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All authors have made substantial contributions to all four categories established by the International Committee of Medical Journal Editors.

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Correspondence to Paul H. Yi.

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Santomartino, S.M., Kung, J. & Yi, P.H. Systematic review of artificial intelligence development and evaluation for MRI diagnosis of knee ligament or meniscus tears. Skeletal Radiol 53, 445–454 (2024). https://doi.org/10.1007/s00256-023-04416-2

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  • DOI: https://doi.org/10.1007/s00256-023-04416-2

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