A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm.

ABSTRACT:

Purpose

This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software.

Materials and methods

In this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, r

SUBMITTER: Joo B 

PROVIDER: S-EPMC8542476 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature