The purpose of this study was to use a deep learning algorithm to detect and localize subacute and chronic ischemic infarcts on head CT scans for use in automated volumetric progression tracking.
We sampled 308 head CT scans (11840 slices) which were reported with chronic or subacute infarct. The infarcted regions in 11840 infarct-positive slices were marked. We trained segmentation algorithm to predict a heatmap of infarct lesion. The heatmap was used to derive scan level features representative of lesion density and volume to train a random forest to predict scan-level probabilities of chronic infarct. Area under receiver operating characteristics curves (AUC) were used to evaluate scan level predictions.
The algorithm was validated on an independent dataset of 1610 head CT scans containing 78 chronic & 9 subacute infarct, 45 chronic ICH, 6 glioblastomas. The distribution of infarct affected territories was – 52.9% MCA, 33.3 % PCA, 9.3% ACA and 4.7% vertebrobasilar territories. The algorithm yielded AUC of 0.8474 (95% CI 0.7964 – 0.8984) for scan level predictions. It identified 8 of 9 subacute infarcts (88.89% recall) and 70 out of 78 chronic infarcts (89.74% recall). The eight missed chronic infarcts constituted of 3 lacunar and 2 hemorrhagic. The volumes of predicted infarct lesions ranged from 1 mL – 526 mL with mean prediction volume as 55.60mL.
The study demonstrates the capability of deep learning algorithms to accurately differentiate infarcts from infarct mimics.