High sensitivity in spontaneous intracranial hemorrhage detection from emergency head CT scans using ensemble-learning approach

Spontaneous intracranial hemorrhages have a high disease burden. Due to increasing medical imaging, new technological solutions for assisting in image interpretation are warranted. We developed a deep learning (DL) solution for spontaneous intracranial hemorrhage detection from head CT scans. The DL solution included four base convolutional neural networks (CNNs), which were trained using 300 head CT scans. A metamodel was trained on top of the four base CNNs, and simple post processing steps were applied to improve the solution's accuracy. The solution performance was evaluated using a retrospective dataset of consecutive emergency head CTs imaged in ten different emergency rooms. 7797 head CT scans were included in the validation dataset and 118 CT scans presented with spontaneous intracranial hemorrhage. The trained metamodel together with a simple rule-based post-processing step showed 89.8% sensitivity and 89.5% specificity for hemorrhage detection at the case-level. The solution detected all 78 spontaneous hemorrhage cases imaged presumably or confirmedly within 12 h from the symptom onset and identified five hemorrhages missed in the initial on-call reports. Although the success of DL algorithms depends on multiple factors, including training data versatility and quality of annotations, using the proposed ensemble-learning approach and rule-based post-processing may help clinicians to develop highly accurate DL solutions for clinical imaging diagnostics.