Accepted for/Published in: JMIR Formative Research
Date Submitted: Sep 19, 2022
Date Accepted: Aug 4, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Prediction of Total Hip Replacement by Using Deep Learning Algorithm on Plain Pelvic Radiographs
ABSTRACT
Background:
Total hip replacement (THR) is considered the gold standard of treatment for refractory degenerative hip disorders. Delayed THR resulted in a poorer outcome. Deep learning (DL) in medical image applications has recently obtained significant breakthroughs. DL in practical wayfinding such as short-termed THR prediction is still lacking.
Objective:
We propose a DL-based assistant system on the pelvic radiograph (PXR) to predict the need for THR within three months.
Methods:
We developed a convolutional neural networks-based DL algorithm to analyze PXRs and to predict the hip region of interest (ROI) whether to have THR or not. The dataset was collected from August 2008 to December 2017. The images included 3013 surgical hip ROIs which underwent THR and 1630 non-surgical hip ROIs without following hip surgery. The images were split by using split-sample validation into training (80%, n=3903), validation (10%, n=476) and testing sets (10%, n=475) to evaluate the algorithm performance.
Results:
The algorithm called SurgHipNet yielded an area under the receiver operating characteristic curve (AUROC) of 0.994 (95% CI, 0.990–0.998). The accuracy, sensitivity, specificity, and F1 score of the model were 0.977, 0.920, 0932, and 0.944, respectively.
Conclusions:
The proposed approach has demonstrated that SurgHipNet showed its ability and possibility to provide efficient support in clinical decision-making and assist physicians in judging the timing to performTHR promptly
Citation
