PeerJ · 2025
Deep-learning tool for early identification of non-traumatic intracranial hemorrhage etiology and application in clinical diagnostics based on computed tomography (CT) scans.
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Zhao Meng, Li Wenjie, Hu Yifan, Jiang Ruixuan, Zhao Yuanli, Zhang Dong, Zhang Yan, Wang Rong, Cao Yong, Zhang Qian, Ma Yonggang, Li Jiaxi, Yu Shaochen, Zhang Ran, Zheng Yefeng, Wang Shuo, Zhao Jizong
Аннотация
BACKGROUND: To develop an artificial intelligence system that can accurately identify acute non-traumatic intracranial hemorrhage (ICH) etiology (aneurysms, hypertensive hemorrhage, arteriovenous malformation (AVM), Moyamoya disease (MMD), cavernous malformation (CM), or other causes) based on non-contrast computed tomography (NCCT) scans and investigate whether clinicians can benefit from it in a diagnostic setting.
METHODS: The deep learning model was developed with 1,868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. We tested the model on two independent datasets (TT200 and SD 98) collected after April 2018. The model's diagnostic performance was compared with clinicians' performance. We further designed a simulated study to compare the clinicians' performance with and without the deep learning system complements.
RESULTS: The proposed deep learning system achieved area under the receiver operating curve of 0.986 (95% CI [0.967-1.000]) on aneurysms, 0.952 (0.917-0.987) on hypertensive hemorrhage, 0.950 (0.860-1.000) on arteriovenous malformation (AVM), 0.749 (0.586-0.912) on Moyamoya disease (MMD), 0.837 (0.704-0.969) on cavernous malformation (CM), and 0.839 (0.722-0.959) on other causes in TT200 dataset. Given a 90% specificity level, the sensitivities of our model were 97.1% and 90.9% for aneurysm and AVM diagnosis, respectively. On the test dataset SD98, the model achieved AUCs on aneurysms and hypertensive hemorrhage of 0.945 (95% CI [0.882-1.000]) and 0.883 (95% CI [0.818-0.948]), respectively. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system complements.
CONCLUSIONS: The proposed deep learning tool can be an accuracy tool for early identification of hemorrhage etiologies based on NCCT scans. It may also provide more information for clinicians for triage and further imaging examination selection.
METHODS: The deep learning model was developed with 1,868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. We tested the model on two independent datasets (TT200 and SD 98) collected after April 2018. The model's diagnostic performance was compared with clinicians' performance. We further designed a simulated study to compare the clinicians' performance with and without the deep learning system complements.
RESULTS: The proposed deep learning system achieved area under the receiver operating curve of 0.986 (95% CI [0.967-1.000]) on aneurysms, 0.952 (0.917-0.987) on hypertensive hemorrhage, 0.950 (0.860-1.000) on arteriovenous malformation (AVM), 0.749 (0.586-0.912) on Moyamoya disease (MMD), 0.837 (0.704-0.969) on cavernous malformation (CM), and 0.839 (0.722-0.959) on other causes in TT200 dataset. Given a 90% specificity level, the sensitivities of our model were 97.1% and 90.9% for aneurysm and AVM diagnosis, respectively. On the test dataset SD98, the model achieved AUCs on aneurysms and hypertensive hemorrhage of 0.945 (95% CI [0.882-1.000]) and 0.883 (95% CI [0.818-0.948]), respectively. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system complements.
CONCLUSIONS: The proposed deep learning tool can be an accuracy tool for early identification of hemorrhage etiologies based on NCCT scans. It may also provide more information for clinicians for triage and further imaging examination selection.