Artificial intelligence for automated detection and measurements of carpal instability signs on conventional radiographs.

ABSTRACT:

Objectives

To develop and validate an artificial intelligence (AI) system for measuring and detecting signs of carpal instability on conventional radiographs.

Materials and methods

Two case-control datasets of hand and wrist radiographs were retrospectively acquired at three hospitals (hospitals A, B, and C). Dataset 1 (2178 radiographs from 1993 patients, hospitals A and B, 2018-2019) was used for developing an AI system for measuring scapholunate (SL) joint distances, SL and capitolunate (CL) angles, and carpal arc interruptions. Dataset 2 (481 radiographs from 217 patients, hospital C, 2017-2021) was used for testing, and with a subsample (174 radiographs from 87 patients), an observer study was conducted to compare its performance to five clinicians. Evaluation metrics i

SUBMITTER: Hendrix N 

PROVIDER: S-EPMC11399222 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature