AI model shows promise for interpreting follow-up chest x ‑rays

A group in Seoul, Korea, has developed an AI model that could help reduce radiology workflows by identifying “no changes” in follow-up chest x-rays of patients in critical care, according to a study published October 24 in Radiology.In a test set of 533 pairs of patient x-rays (baseline and follow-up), the algorithm was highly accurate when interpreting those that showed no changes, wrote first authors Jihye Yun, PhD, Yura Ahn, MD, of the University of Ulsan, and colleagues.“Radiologists routinely compare the current and previous chest radiographs during interpretation to enhance the sensitivity for change detection and provide information for differential diagnosis. However, this results in a large workload that could delay the timely reporting of significant findings,” the group wrote.Moreover, most AI models developed to interpret chest x-rays so far are restricted to an image at a single time point. Thus, in this study, the group aimed to validate a deep-learning algorithm using thoracic cage registration and subtraction to triage pairs of chest radiographs showing no change.Example of triage of no change in a pair of chest radiographs in the emergency department. (A) Baseline posteroanterior chest radiograph in a 63-year-old female patient shows a small amount of left (L) pleural effusion and partial atelectasis of the right middle lobe. (B) Follow-up posteroanterior chest radiograph obtained in the same patient 1 day later shows no significant change. (C) Gradie...

Source: AuntMinnie.com Headlines - October 24, 2023 Category: Radiology Authors: Will Morton Tags: Artificial Intelligence Imaging Informatics Digital X-Ray Source Type: news