Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients

Abstract

Abstract Objectives Artif icial intelligence (AI)–based image analysis is increasingly applied in the acute stroke field. Its implementation for the detection and quantification of hemorrhage suspect hyperdensities in non-contrast-enhanced head CT (NCCT) scans may facilitate clinical decision-making and accelerate stroke management. Methods NCCTs of 160 patients with suspected acute stroke were analyzed regarding the presence or absence of acute intracranial hemorrhages (ICH) using a novel AI-based algorithm. Read was performed by two blinded neuroradiology residents (R1 and R2). Ground truth was established by an expert neuroradiologist. Specificity, sensitivity, and area under the curve were calculated for ICH and intraparenchymal hemorrhage (IPH) detection. IPH-volumes were segmented and quantified automatically by the algorithm and semi-automatically. Intraclass correlation coefficient (ICC) and Dice coefficient (DC) were calculated. Results In total, 79 of 160 patients showed acute ICH, while 47 had IPH. Sensitivity and specificity for ICH detection were 0.91 and 0.89 for the algorithm; 0.99 and 0.98 for R1; and 1.00 and 0.98 for R2. Sensitivity and specificity for IPH detection were 0.98 and 0.89 for the algorithm; 0.83 and 0.99 for R1; and 0.91 and 0.99 for R2. Interreader reliability for ICH and IPH detection showed strong agreements for the algorithm (0.80 and 0.84), R1 (0.96 and 0.84), and R2 (0.98 and 0.92), respectively. ICC indicated an excellent (0.98) agreement between the algorithm and the reference standard of the IPH-volumes. The mean DC was 0.82. Conclusion The AI-based algorithm reliably assessed the presence or absence of acute ICHs in this dataset and quantified IPH volumes precisely. Key Points • Artificial intelligence (AI) is able to detect hyperdense volumes on brain CTs reliably. • Sensitivity and specificity are highest for the detection of intraparenchymal hemorrhages. • Interreader reliability for hemorrhage detection shows strong agreement for AI and human readers.