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Deep learning based automatic detection algorithm for acute intracranial haemorrhage: a pivotal randomized clinical trial

Medicine and Health

Deep learning based automatic detection algorithm for acute intracranial haemorrhage: a pivotal randomized clinical trial

T. J. Yun, J. W. Choi, et al.

This innovative study developed and validated an AI algorithm that enhances the diagnosis of acute intracranial hemorrhage through brain CT imaging. The research reveals that AI-assisted interpretation significantly boosts diagnostic accuracy, especially among non-radiologist physicians. This groundbreaking work was conducted by a team of experts including Tae Jin Yun, Jin Wook Choi, Miran Han, Woo Sang Jung, Seung Hong Choi, Roh-Eul Yoo, and In Pyeong Hwang.

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Playback language: English
Abstract
This study developed and validated an AI algorithm for diagnosing acute intracranial hemorrhage (AIH) using brain CT images. A retrospective, multi-reader, pivotal, crossover, randomized study showed that AI-assisted brain CT interpretation resulted in significantly higher diagnostic accuracy (0.9703 vs. 0.9471, p < 0.0001) than AI-unassisted interpretation. Non-radiologist physicians showed the greatest improvement in accuracy with AI assistance.
Publisher
npj Digital Medicine
Published On
Nov 16, 2023
Authors
Tae Jin Yun, Jin Wook Choi, Miran Han, Woo Sang Jung, Seung Hong Choi, Roh-Eul Yoo, In Pyeong Hwang
Tags
AI algorithm
acute intracranial hemorrhage
brain CT images
diagnostic accuracy
AI-assisted interpretation
medical imaging
radiology

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