The CLARISA deep learning framework classified Connexin-43 lateralization in rat ventricular myocardium with a ROC-AUC of 0.904 (95% CI 0.828-0.960).
CLARISA is a novel, segmentation-free deep learning framework that accurately automates the assessment of Connexin-43 lateralization in cardiac tissue.
Effect estimate: ROC-AUC 0.904 (95% CI 0.828-0.960)
Connexin-43 (CX43) lateralization in ventricular myocardium has been associated with abnormal impulse propagation and increased arrhythmia susceptibility. Its quantitative assessment in histological sections remains challenging because previous methods require segmentation of individual cardiomyocytes and rely on geometric rules applied to segmented cell profiles. Here, we present CLARISA, a segmentation-free, ROI-based deep learning framework that classifies CX43-positive regions as terminal or lateralized directly from fluorescence images. An expert-annotated dataset was generated from left-ventricular cryosections of Wistar rat hearts, in which CX43-positive regions were labeled according to their distribution pattern. A dual-stream EfficientNetV2-S classifier was trained to capture both local and contextual ROI morphology. We also developed a semi-automated whole-section inference module to generate spatial lateralization probability maps and global percent lateralization estimates. On the held-out test set, CLARISA achieved a ROC-AUC of 0.904 (95% bootstrap CI: 0.828–0.960) and a PR-AUC of 0.808 (95% bootstrap CI: 0.682–0.913), supporting the feasibility of automated ROI classification for CX43 lateralization assessment. When deployed on whole tissue sections, including an independently analyzed section not used during model development, CLARISA generated spatial maps that captured heterogeneous CX43 organization and produced a global percent lateralization estimate closely aligned with expert annotation, differing by only 1.30 percentage points over the same detected CX43-positive area. Comparison with a previously published segmentation-based method further indicated that ROI-based and cell-segmentation-based approaches provide related but non-equivalent readouts of CX43 lateralization. The ROI-based design additionally reduces annotation burden—requiring classification of discrete CX43-positive signal rather than complex cardiomyocyte delineation—and ensures that all detected CX43-positive signal contributes to the lateralization estimate regardless of cell boundaries. These results establish CLARISA as a proof-of-principle framework for scalable, segmentation-free CX43 lateralization assessment in cardiac tissue. Further validation across larger, independent, and more heterogeneous datasets will be required to assess robustness, portability across imaging conditions, and translational applicability. The complete codebase, pretrained model, image data, and expert annotation tool are publicly available.
Gattari et al. (Tue,) conducted a other in Connexin-43 lateralization in ventricular myocardium. CLARISA (Connexin-43 Lateralization Automated ROI-Based Image Signal Analyzer) vs. Expert annotation and segmentation-based methods was evaluated on Classification of CX43-positive regions as terminal or lateralized (ROC-AUC) (ROC-AUC 0.904, 95% CI 0.828-0.960). The CLARISA deep learning framework classified Connexin-43 lateralization in rat ventricular myocardium with a ROC-AUC of 0.904 (95% CI 0.828-0.960).