January 2021
Investigation performed at McGill University Health Centre, Montreal, Quebec, Canada
Journal PURPOSE:
To investigate the current and potential uses of AI in the management of anterior cruciate ligament (ACL) injury.
To present how ML, with its ability to assess complex nonlinear relationships, has been used to address and improve the detection, treatment, and rehabilitation of individuals with ACL injuries.
In medicine, AI can be applied either virtually using computers or physically using robots. Over the past decade, the development of AI applications in orthopaedics has focused primarily on diagnostic, mostly image interpretations.
Convolutional Neural Network
A type of ML algorithm, outperformed general orthopaedic surgeons at detecting and classifying proximal humeral fractures. In another investigation, a CNN was developed to interpret hand, wrist and ankle radiographs with an accuracy matching that of senior orthopaedic staff.
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In this form of pathology, close to half of all injuries involve the knee. Of these injuries, ACL tears are frequent, with non-contact ACL injuries making up to 78% of all sports-related knee pathology (a branch of medical science that is focused on the study and diagnosis of disease). The diagnosis of clinically significant ACL injuries can be challenging for clinicians. ML (machine learning) may facilitate this by addressing the variability of certain tests.
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