Abstract WP68: Interpretable Deep Learning-based Characterization of Intracranial Hemorrhage on Head CT

Abstract

Background: Machine learning algorithms have proven accurate in the detection of intracranial hemorrhage (ICH) on head CT. Most reported algorithms, however, are limited to binary detection and global lesion volume estimation. We developed a pipeline to additionally perform subtype classification for clinical risk stratification, while also providing visual displays to improve interpretability of algorithm output (“black box problem”) and enable clinical systems integration. Methods: We developed a convolutional neural network (CNN) for ICH detection and subtype classification. The algorithm was trained on 2,209 head CT exams (hemorrhage-negative controls and the 5 major ICH subtypes) and outputs class-activation heatmaps to aid interpretability. We quantify intraparenchymal hemorrhage (IPH) volume using a modified U-Net CNN trained on a cohort of 110 manually-segmented hematomas to precisely delineate lesion boundaries. Algorithm output was stored as DICOM segmentation objects which can be overlaid on native images within our existing viewer applications, enabling the user to dynamically adjust characteristics such as color and transparency. Results: Diagnostic performance for ICH detection was high with ROC area-under-curve of 0.98. Fig 1A shows a heatmap highlighting different ICH foci. IPH subtype classification performance is summarized in Fig 1B with area-under-curve of 0.95. Fig 1C shows IPH segmentation and volume estimation overlays with automatically generated text for the medical record. Dice similarity coefficient for segmentation was 0.87. Conclusion: We describe a machine-learning approach for ICH detection, subtype classification, and IPH-specific volume estimation. Utilizing easily interpretable heatmaps, dynamically-adjustable overlays, and automated result text generation, our pipeline is capable of providing accurate information optimized for clinical integration.