Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study.

ABSTRACT:

Objectives

The aim of this study was to evaluate the diagnostic performance of nonspecialist readers with and without the use of an artificial intelligence (AI) support tool to detect traumatic fractures on radiographs of the appendicular skeleton.

Methods

The design was a retrospective, fully crossed multi-reader, multi-case study on a balanced dataset of patients (≥2 years of age) with an AI tool as a diagnostic intervention. Fifteen readers assessed 340 radiographic exams, with and without the AI tool in 2 different sessions and the time spent was automatically recorded. Reference standard was established by 3 consultant radiologists. Sensitivity, specificity, and false positives per patient were calculated.

Results

Patient-wise sensitivity increased from 72% to 80

SUBMITTER: Bachmann R 

PROVIDER: S-EPMC11096271 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature