AKIRA: Deep learning tool for image standardization, implant detection and arthritis grading to establish a radiographic registry in patients with anterior cruciate ligament injuries.

Abstract

Purpose: Developing large-scale, standardized radiographic registries for anterior cruciate ligament (ACL) injuries with artificial intelligence (AI) tools can enhance personalized orthopaedics. We propose deploying Artificial Intelligence for Knee Imaging Registration and Analysis (AKIRA), a trio of deep learning (DL) algorithms, to automatically classify and annotate radiographs. We hypothesize that algorithms can efficiently organize radiographs based on laterality, projection, identify implants and classify osteoarthritis (OA) grade.

Methods: A collection of 20,836 knee radiographs from all time points of treatment (mean orthopaedic follow-up 70.7 months; interquartile range [IQR]: 6.8-172 months) were aggregated from 1628 ACL-injured patients (median age 26 years [IQR: 19-42], 57% male). Three DL algorithms (EfficientNet, YOLO [You Only Look Once] and Residual Network) were employed. Radiograph laterality and projection (anterior-posterior [AP], lateral, sunrise, posterior-anterior, hip-knee-ankle and Camp-Coventry intercondylar [notch]) were labelled by a DL model. Manually provided labels of metal fixation implants were used to develop a DL object detection algorithm. The degree of OA, both as measured by specific Kellgren-Lawrence (KL) grades, as well as based on a binarized label of OA (defined as KL Grade ≥2), on standing AP radiographs were classified using a DL algorithm. Individual model performances were evaluated on a subset of images prior to the deployment of AKIRA to registry construction using all ACL radiographs.

Results: The classification algorithms showed excellent performance in classifying radiographic laterality (F1 score: 0.962-0.975) and projection (F1 score: 0.941-1.0). The object detection algorithm achieved high precision-recall (area under the precision-recall curve: 0.695-0.992) for identifying various metal fixations. The KL classifier reached concordances of 0.39-0.40, improving to 0.81-0.82 for binary OA labels. Sequential deployment of AKIRA following internal validation processed and labelled all 20,836 images with the appropriate views, implants, and the presence of OA within 88 min.

Conclusion: AKIRA effectively automated the classification and object detection in a large radiograph cohort of ACL injuries, creating an AI-enabled radiographic registry with comprehensive details on laterality, projection, implants and OA.

Study design: Cross-sectional study.

Level of evidence: Level IV.