WSO EXPRESS: Weakly Supervised Multitask Learning Models to Identify Symptom Onset Time of Intracerebral Hemorrhage with Unclear Onset.

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Abstract

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.