Artificial Intelligence Radiographic Analysis Tool for Total Knee Arthroplasty

Background: The postoperative follow-up of a patient after total knee arthroplasty (TKA) requires regular evaluation of the condition of the knee through interpretation of X-rays. This rigorous analysis requires expertize, time, and methodical standardization. Our work evaluated the use of an artificial intelligence tool, X-TKA, to assist surgeons in their interpretation. Methods: A series of 12 convolutional neural networks were trained on a large database containing 39,751 X-ray images. These algorithms are able to determine examination quality, identify image characteristics, assess prosthesis sizing and positioning, measure knee-prosthesis alignment angles, and detect anomalies in the bone-cement-implant complex. The individual interpretations of a pool of senior surgeons with and without the assistance of X-TKA were evaluated on a reference dataset built in consensus by senior surgeons. Results: The algorithms obtained a mean area under the curve value of 0.98 on the quality assurance and the image characteristics tasks. They reached a mean difference for the predicted angles of 1.71 degrees (standard deviation, 1.53 degrees), similar to the surgeon average difference of 1.69 degrees (standard deviation, 1.52 degrees). The comparative analysis showed that the assistance of X-TKA allowed surgeons to gain 5% in accuracy and 12% in sensitivity in the detection of interface anomalies. Moreover, this study demonstrated a gain in repeatability for each single surgeon (Light's kappa +0.17), as well as a gain in the reproducibility between surgeons (Light's kappa +0.1). Conclusion: This study highlights the benefit of using an intelligent artificial tool for a standardized interpretation of postoperative knee X-rays and indicates the potential for its use in clinical practice. (c) 2023 Elsevier Inc. All rights reserved.