Machine Learning to Recognise ACL Tears: A Systematic Review

Applied Sciences, vol. 15, pp. 4636

Abstract

Machine learning-based tools are becoming increasingly popular in clinical practice. They offer new possibilities but are also limited in their reliability and accuracy. The present systematic review updates and discusses the existing literature regarding machine learning algorithm-based identification of cruciate ligament injury on radiographic images. PubMed was searched for articles containing machine learning algorithms related to cruciate ligament injury recognition. No additional filters or time constraints were used. All eligible studies were accessed by hand. From the 115 articles initially retrieved, 29 articles were finally included. Only one study included the posterior cruciate ligament (PCL). Deep learning algorithms in the form of convolutional neural networks (CNNs) were most frequently used. Many studies presented CNNs that identified binary decision classes of regular and torn anterior cruciate ligaments (ACLs) with a best sensitivity of 0.98, a specificity of 0.99, and an AUC ROC of 1.0. Other studies expanded the decision classes to partially torn ACLs or reconstructed ACLs, usually at the cost of sensitivity and specificity. Deep learning algorithms are excellent for identifying ACL injuries, tears, or postoperative status after reconstruction on MRI images. They are much faster but only sometimes better than the human reviewer. While the technology seems ready, barriers to ethical and legal issues and clinicians’ refusals must be overcome to some extent. It can be firmly assumed that artificial intelligence will have a future contribution in the diagnosis of cruciate ligament injuries.

Authors 6

1. Julius Michael Wolfgart Aachen Department of Orthopaedic, Trauma, and Reconstructive Surgery Division of Arthroplasty and Tumour Surgery

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Orthopaedic, Trauma, and Reconstructive Surgery, Division of Arthroplasty and Tumour Surgery, RWTH University Hospital, 52074 Aachen, Germany

   Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Hospital, 52074 Aachen, Germany

2. Ulf Krister Hofmann Aachen Department of Orthopaedic, Trauma, and Reconstructive Surgery Division of Arthroplasty and Tumour Surgery

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Orthopaedic, Trauma, and Reconstructive Surgery, Division of Arthroplasty and Tumour Surgery, RWTH University Hospital, 52074 Aachen, Germany

3. Maximilian Praster Aachen Department of Orthopaedic, Trauma, and Reconstructive Surgery Division of Arthroplasty and Tumour Surgery Teaching and Research Area Experimental Orthopaedics and Trauma Surgery

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Orthopaedic, Trauma, and Reconstructive Surgery, Division of Arthroplasty and Tumour Surgery, RWTH University Hospital, 52074 Aachen, Germany

   Teaching and Research Area Experimental Orthopaedics and Trauma Surgery, RWTH University Hospital, 52074 Aachen, Germany

4. Marina Danalache

   Universitätsklinikum Tübingen · University of Tübingen

   Affiliation as printed

   Department of Orthopaedic Surgery, University Hospital Tübingen, 72076 Tübingen, Germany

5. Filippo Migliorini corresponding

   Paracelsus Medical University · Ospedale di Bolzano · Azienda Sanitaria dell'Alto Adige

   Affiliation as printed

   Department of Orthopaedic and Trauma Surgery, Academic Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of the Paracelsus Medical University, 39100 Bolzano, Italy

6. Martina Feierabend

   University of Cologne

   Affiliation as printed

   Metabolic Reconstruction and Flux Modelling, Institute for Plant Sciences, University of Cologne, 50923 Köln, Germany