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Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan

2024; Nature Portfolio; Volume: 7; Issue: 1 Linguagem: Inglês

10.1038/s41746-024-01007-w

2398-6352

Tran Anh Tuan, Tal Zeevi, Stefan P. Haider, Gaby Abou Karam, Elisa R. Berson, Hishan Tharmaseelan, Adnan I. Qureshi, Pina C. Sanelli, David J. Werring, Ajay Malhotra, Nils Petersen, Adam de Havenon, Guido J. Falcone, Kevin N. Sheth, Seyedmehdi Payabvash,

Neurosurgical Procedures and Complications

Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE