Quantitative Histological Insights Into Sudden Arrhythmic Death Syndrome: Findings From a Forensic Autopsy Cohort

Abstract

Sudden arrhythmic death syndrome (SADS) is a major cause of sudden cardiac death in young individuals, characterized by structurally normal hearts and negative toxicology. Although guidelines recommend family screening, phenotyping remains challenging. This study applied quantitative histology and deep-learning-based cell segmentation to investigate morphological features in SADS compared to controls. We conducted a retrospective autopsy study of 77 SADS cases and 41 age- and sex-matched controls (aged 1-49 years) who died from trauma or suicide. Cardiac tissue was analyzed using QuPath and deep learning-based image processing (Quan10). Random Forest classification and recursive feature elimination were used to identify discriminating features. Quantitative analysis found subtle but significant morphological differences. SADS cases had reduced residual myocardium in overall tissue (53% vs. 56%, p = 0.02) and endocardial regions (49% vs. 54%, p < 0.001). Endocardial and epicardial adipocyte density were key discriminators in the model. Genetic analysis identified pathogenic variants in six cases and three controls. AI-driven histology detected differences in hearts previously considered normal, suggesting subgroups within SADS. These findings support the use of quantitative tools in postmortem phenotyping, with potential to refine diagnosis, guide family screening, and improve understanding of arrhythmic mechanisms.

Keywords: artificial intelligence (AI); cardiac morphology; postmortem; sudden arrhythmic death syndrome (SADS); sudden cardiac death (SCD).