Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy.

ABSTRACT:

Objectives

Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists.

Methods

A deep learning model was trained on 212,484 NCCTB scans drawn from a private radiology group in Australia. Scans from inpatient, outpatient, and emergency settings were included. Scan inclusion criteria were age ≥ 18 years and series slice thickness ≤ 1.5 mm. Thirty-two radiologists reviewed 2848 scans with and with

SUBMITTER: Buchlak QD 

PROVIDER: S-EPMC10853361 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature