Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm

Abstract

Background and Objectives: The number of patients who undergo multiple operations on a knee is increasing. The objective of this study was to develop a deep learning algorithm that could detect 17 different surgical implants on plain knee radiographs. Materials and Methods: An internal dataset consisted of 5206 plain knee antero-posterior X-rays from a single, tertiary institute for model development. An external set contained 238 X-rays from another tertiary institute. A total of 17 different types of implants including total knee arthroplasty, unicompartmental knee arthroplasty, plate, and screw were labeled. The internal dataset was approximately split into a train set, a validation set, and an internal test set at a ratio of 7:1:2. You Only look Once (YOLO) was selected as the detection network. Model performances with the validation set, internal test set, and external test set were compared. Results: Total accuracy, total sensitivity, total specificity value of the validation set, internal test set, and external test set were (0.978, 0.768, 0.999), (0.953, 0.810, 0.990), and (0.956, 0.493, 0.975), respectively. Means ± standard deviations (SDs) of diagonal components of confusion matrix for these three subsets were 0.858 ± 0.242, 0.852 ± 0.182, and 0.576 ± 0.312, respectively. True positive rate of total knee arthroplasty, the most dominant class of the dataset, was higher than 0.99 with internal subsets and 0.96 with an external test set. Conclusion: Implant identification on plain knee radiographs could be automated using a deep learning technique. The detection algorithm dealt with overlapping cases while maintaining high accuracy on total knee arthroplasty. This could be applied in future research that analyzes X-ray images with deep learning, which would help prompt decision-making in clinics.

Advanced Imaging of Total Knee Arthroplasty

“…Several current studies use deep learning (DL) approaches when evaluating TKA (e.g., by identifying different types of knee prostheses on radiographs), [61][62][63][64] whereas another radiography study developed and validated DL models for the identification of both THA and TKA designs. 65 In addition, a DL algorithm was proven to allow for fully automatic measurements of the hip knee ankle on full-limb radiographs after TKA.…”

Identification of Knee Prostheses from Lateral Radiographs Using Deep Learning Techniques

“…However, it takes a lot of time and effort to determine the implant brand from a primary knee arthroplasty [1]. The frequency of patients having repeated knee surgeries is rising along with the number of knee operations being performed [2]. Total knee arthroplasty (TKA), a surgical surgery that may be used to treat severe osteoarthritis of the knee, lowers joint damage and enhances overall joint function [3].…”