A deep learning-based approach for efficient detection and classification of local Ca²⁺ release events in Full-Frame confocal imaging.

The release of Ca ions from intracellular stores plays a crucial role in many cellular processes, acting as a secondary messenger in various cell types, including cardiomyocytes, smooth muscle cells, hepatocytes, and many others. Detecting and classifying associated local Ca release events is particularly important, as these events provide insight into the mechanisms, interplay, and interdependencies of local Carelease events underlying global intracellular Casignaling. However, time-consuming and labor-intensive procedures often complicate analysis, especially with low signal-to-noise ratio imaging data. Here, we present an innovative deep learning-based approach for automatically detecting and classifying local Ca release events. This approach is exemplified with rapid full-frame confocal imaging data recorded in isolated cardiomyocytes. To demonstrate the robustness and accuracy of our method, we first use conventional evaluation methods by comparing the intersection between manual annotations and the segmentation of Ca release events provided by the deep learning method, as well as the annotated and recognized instances of individual events. In addition to these methods, we compare the performance of the proposed model with the annotation of six experts in the field. Our model can recognize more than 75 % of the annotated Ca release events and correctly classify more than 75 %. A key result was that there were no significant differences between the annotations produced by human experts and the result of the proposed deep learning model. We conclude that the proposed approach is a robust and time-saving alternative to conventional full-frame confocal imaging analysis of local intracellular Ca events.