Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries

Abstract

Background: Technological innovation is a key component of orthopaedic surgery. With the integration of powerful technologies in surgery and clinical practice, artificial intelligence (AI) may become an important tool for orthopaedic surgeons in the future. Through adaptive learning and problem solving that serve to constantly increase accuracy, machine learning algorithms show great promise in orthopaedics. Purpose: To investigate the current and potential uses of AI in the management of anterior cruciate ligament (ACL) injury. Study Design: Systematic review; Level of evidence, 3. Methods: A systematic review of the PubMed, MEDLINE, Embase, Web of Science, and SPORTDiscus databases between their start and August 12, 2020, was performed by 2 independent reviewers. Inclusion criteria included application of AI anywhere along the spectrum of predicting, diagnosing, and managing ACL injuries. Exclusion criteria included non-English publications, conference abstracts, review articles, and meta-analyses. Statistical analysis could not be performed because of data heterogeneity; therefore, a descriptive analysis was undertaken. Results: A total of 19 publications were included after screening. Applications were divided based on the different stages of the clinical course in ACL injury: prediction (n = 2), diagnosis (n = 12), intraoperative application (n = 1), and postoperative care and rehabilitation (n = 4). AI-based technologies were used in a wide variety of applications, including image interpretation, automated chart review, assistance in the physical examination via optical tracking using infrared cameras or electromagnetic sensors, generation of predictive models, and optimization of postoperative care and rehabilitation. Conclusion: There is an increasing interest in AI among orthopaedic surgeons, as reflected by the applications for ACL injury presented in this review. Although some studies showed similar or better outcomes using AI compared with traditional techniques, many challenges need to be addressed before this technology is ready for widespread use.

Artificial intelligence in orthopaedic surgery

“…Accuracy of up to 98% has been reported, as well as the potential of CNNs to outperform or perform non-inferiorly to humans. 26 , 32 - 36 It has also been reported that DL models could recognize laterality, exam view, and body part in wrist, hand, and ankle radiographs. They have also shown promise in achieving more notoriously difficult diagnoses, such as scaphoid fractures, as effectively as human specialists.…”

“…The past decade has seen imaging analysis become a considerable focal point among AI research. 32 Numerous authors have studied the ability of CNNs to identify various upper and lower limb fractures on radiographs, such as hip, calcaneus, and radial fractures. Accuracy of up to 98% has been reported, as well as the potential of CNNs to outperform or perform non-inferiorly to humans.…”

“…Examples in orthopaedic surgery include AI models predicting suitable patients for nerve blocks following anterior cruciate ligament (ACL) reconstruction. 32 Kim et al 55 developed a DL algorithm to predict the mortality and morbidity risk following spinal fusion, and found this to be more accurate compared to the traditionally used scoring system by the American Society of Anesthesiologists. 56 Another interesting application of AI is showcased by Kumar et al, 57 who developed a ML algorithm predicting patient outcomes in shoulder arthroplasty.…”

“… 68 , 69 Interestingly, it has also been proposed that ML algorithms could prove a useful augment in rehabilitation following ACL surgery, by using biomechanical data to assess for asymmetries in gait analysis. 32 , 70 …”

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

“…Timeline outlining the major historical steps that have led to the development of artificial intelligence (AI) up to the advancement of deep learning, with significant impact in the history of medicine and orthopedics. AI, ML, and DL offer a variety of application models in the orthopedic field, including the random forest model, support vector machine, multilayer perceptron, convolutional neural network (CNN), alternating decision trees, and recurrent neural network [4,15].…”