A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Abstract:

:Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.

journal_name

Nat Commun

journal_title

Nature communications

authors

Shi Z,Miao C,Schoepf UJ,Savage RH,Dargis DM,Pan C,Chai X,Li XL,Xia S,Zhang X,Gu Y,Zhang Y,Hu B,Xu W,Zhou C,Luo S,Wang H,Mao L,Liang K,Wen L,Zhou L,Yu Y,Lu GM,Zhang LJ

doi

10.1038/s41467-020-19527-w

subject

Has Abstract

pub_date

2020-11-30 00:00:00

pages

6090

issue

1

issn

2041-1723

pii

10.1038/s41467-020-19527-w

journal_volume

11

pub_type

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