What question did this study set out to answer?

This study aims to investigate morphological features in sudden arrhythmic death syndrome (SADS) using quantitative histology and AI.

March 1, 2026Open Access

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

Key Result

SADS hearts showed significantly reduced residual myocardium (53% vs 56%, p=0.02) and distinct adipocyte patterns aiding classification versus controls in autopsy tissue.

Key Points

This study aims to investigate morphological features in sudden arrhythmic death syndrome (SADS) using quantitative histology and AI.
Conducted a retrospective autopsy study of 77 SADS cases and 41 controls.
Employed quantitative histology and deep-learning-based cell segmentation techniques.
Used Random Forest classification and recursive feature elimination for identifying discriminating features.
Analyzed cardiac tissue morphology with QuPath and deep learning-based image processing (Quan10).
SADS cases exhibited 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.
Identified pathogenic genetic variants in six SADS cases and three controls.
AI techniques detected differences in previously normal-looking hearts, indicating possible subgroups within SADS.

Structured PICO

Does AI-driven quantitative histology identify morphological differences in the structurally normal hearts of SADS victims compared to controls?

Population

118 deceased individuals aged 1-49 years, comprising 77 Sudden Arrhythmic Death Syndrome (SADS) cases and 41 age- and sex-matched controls who died from trauma or suicide.

Intervention

Quantitative histology and deep-learning-based cell segmentation (QuPath and Quan10) of cardiac tissue

Comparator

Standard evaluation (comparison between SADS cases and controls)

Outcome

Morphological differences in cardiac tissue (including residual myocardium and adipocyte density)surrogate

AI-driven quantitative histology identifies subtle morphological differences, such as reduced residual myocardium and altered adipocyte density, in SADS hearts previously considered structurally normal.

Main Result

Absolute Event Rate: 0% vs 0%

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.