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 Communjournal_title
Nature communicationsauthors
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 LJdoi
10.1038/s41467-020-19527-wsubject
Has Abstractpub_date
2020-11-30 00:00:00pages
6090issue
1issn
2041-1723pii
10.1038/s41467-020-19527-wjournal_volume
11pub_type
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