Development of a Clinically Applicable Deep Learning System Based on Sparse Training Data to Accurately Detect Acute Intracranial Hemorrhage from Non-enhanced Head Computed Tomography.

Non-enhanced head computed tomography is widely used for patients presenting with head trauma or stroke, given acute intracranial hemorrhage significantly influences clinical decision-making. This study aimed to develop a deep learning algorithm, referred to as DeepCT, to detect acute intracranial hemorrhage on non-enhanced head computed tomography images and evaluate its clinical applicability. We retrospectively collected 1,815 computed tomography image sets from a single center for model training. Additional computed tomography sets from 3 centers were used to construct an independent validation dataset (VAL) and 2 test datasets (GPS-C and DICH). A third test dataset (US-TW) comprised 150 cases, each from 1 hospital in Taiwan and 1 hospital in the United States of America. Our deep learning model, based on U-Net and ResNet architectures, was implemented using PyTorch. The deep learning algorithm exhibited high accuracy across the validation and test datasets, with overall accuracy ranging from 0.9343 to 0.9820. Our findings show that the deep learning algorithm effectively identifies acute intracranial hemorrhage in non-enhanced head computed tomography studies. Clinically, this algorithm can be used for hyperacute triage, reducing reporting times, and enhancing the accuracy of radiologist interpretations. The evaluation of the algorithm on both United States and Taiwan datasets further supports its universal reliability for detecting acute intracranial hemorrhage.