Title:A Systematic Review on Deep Learning Model in Computer-aided Diagnosis for
Anterior Cruciate Ligament Injury
Volume: 20
Author(s): Herman Herman*, Yogan Jaya Kumar, Sek Yong Wee and Vinod Kumar Perhakaran
Affiliation:
- Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
- Faculty of Computer Science, Universitas Internasional Batam, Batam, Indonesia
Keywords:
Deep learning, Interpretable, Anterior cruciate ligament, Arthroscopy diagnosis, Computer-aided diagnosis, Magnetic resonance imaging.
Abstract:
Introduction:
In developing Computer-Aided Diagnosis (CAD), a Convolutional Neural Network (CNN) has been commonly used as a Deep Learning (DL)
model. Although it is still early, DL has excellent potential in implementing computers in medical diagnosis.
Methods:
This study reviews the use of DL for Anterior Cruciate Ligament (ACL) tear diagnosis. A comprehensive search was performed in PubMed,
Embase, and Web of Science databases from 2018 to 2024. The included study criteria used MRI images to evaluate ACL tears, and the diagnosis
of ACL tears was performed using the DL model. We summarized the paper by reporting their model accuracy, model comparison with
arthroscopy, and explainable.
Results:
AI implementation in tabular format; we conclude that many medical professionals believe that arthroscopic diagnosis is the most reliable method
for diagnosing ACL tears. However, due to its intrusive treatment, CAD is projected to be able to produce similar outcomes from MRI scan results.
To gain the trust of physicians and meet the demand for reliable knee injury detection systems, an algorithm for CAD should also meet several
criteria, such as being transparent, interpretable, explainable, and easy to use. Therefore, future works should consider creating an Explainable DL
model for ACL tear diagnosis. It is also essential to evaluate the performance of this Explainable DL model compared to the gold standard of
arthroscopy diagnosis.
Conclusion:
There are issues regarding the need for Explainable DL in CAD to increase confidence in its result while also highlighting the importance of the
involvement of medical practitioners in system design. There is no funding for this work.