Background Approximately 1/3 of spontaneous intracerebral hemorrhage (sICH) patients did not know the onset time and were excluded from studies about time-dependent treatments for hyperacute sICH. Aims To help clinicians explore the benefit of time-dependent treatments for unclear-onset patients, we presented artificial intelligence models to identify onset time using non-contrast computed tomography (NCCT) based on weakly supervised multitask learning (WS-MTL) structure. Methods The patients with reliable symptom onset time (strong label) or repeat CT (weak label) were included and split into training set and test set (internal and external). The WS-MTL structure utilized strong and weak labels simultaneously to improve performance. The models included three binary classification models for classifying whether NCCT acquired within 6, 8 or 12 hours for different treatments measured by area under curve (AUC), and a regression model for determining the exact onset time measured by mean absolute error (MAE). The generalizability of models was also explored in comprehensive analysis. Results 4 004 patients with 10 780 NCCT scans were included. The performance of WS-MTL classification model showed high accuracy, and that of regression model was satisfactory in ⤠6 hours subgroup. In comprehensive analysis, the WS-MTL showed better performance for larger hematomas and thinner scans. And the performance improved effectively as training amounts increasing and could be improved steadily through retraining. Conclusions The WS-MTL models showed good performance and generalizability. Considering the large number of unclear-onset sICH patients, it may be worth to integrate the WS-MTL model into clinical practice to identify the onset time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.