AI may boost arthroplasty imaging research

In this study, the group hypothesized that a deep learning AI model could efficiently and accurately classify shoulder x-rays according to important featuresThe researchers included 2,303 x-rays from 1,724 shoulder arthroplasty patients, with two observers manually labeling all x-rays according to three features:The laterality of the shoulder (left or right)The x-ray view (anterior-posterior, axillary, or scapular Y-view)Whether the image had no implant (preoperative), an anatomic total shoulder arthroplasty (aTSA), or a reverse shoulder arthroplasty (RSA)All of the labeled x-rays were randomly split into development and testing sets at the patient level and based on stratification, with the trained algorithm then evaluated on the testing set using quantitative metrics and visual evaluation techniques.According to the findings, the algorithm perfectly classified the laterality (F1 scores of 100% on the testing set). When classifying the imaging projection, the algorithm achieved F1 scores of 99.2% on anterior-posterior views, 100% on axillary views, and 100% on lateral views. In addition, when classifying the implant type, the model achieved F1 scores of 100% on preoperative images, 95.2% on aTSA, and 100% on RSA x-rays.Importantly, it took the algorithm 20.3 seconds to analyze 502 images, the authors added.“The ability to complete this task automatically, quickly, and with outstanding precision translates into incredible potential time and cost-savings for research endeav...

Source: AuntMinnie.com Headlines - October 27, 2023 Category: Radiology Authors: Will Morton Tags: Imaging Informatics Artificial Intelligence Source Type: news