A Generative AI Framework to Predict Cardiomyocyte Contraction Function from Single Static Images.

Area of Science:

• Cardiovascular Biology
• Artificial Intelligence in Medicine
• Cellular Imaging

Background:

• Understanding cardiomyocyte structure-function relationships is crucial for cardiac biology and disease modeling.
• Current methods for assessing cardiomyocyte contractility require time-resolved imaging and intensive computational analysis, limiting throughput.

Purpose of the Study:

• To develop a generative artificial intelligence (AI) framework for predicting cardiomyocyte contractile behavior directly from single static images.
• To establish a scalable and interpretable method for inferring cardiomyocyte function from morphology, reducing reliance on time-lapse imaging.

Main Methods:

• A U-Net-based generator integrated with a patch-based Generative Adversarial Network (GAN) discriminator was employed to translate morphological and sarcomere structural features into contraction heatmaps.
• Synthetic cell-function pairs generated using a StyleGAN2 framework were incorporated to enhance model performance and generalizability.
• Analysis of AI predictions focused on structure-function relationships, contractile output, prediction fidelity, and reconstruction error as an interpretable metric.

Main Results:

• The U-Net-GAN model achieved high predictive accuracy, with Structural Similarity Index (SSIM) values reaching 0.84 when using combined morphological and structural inputs.
• Incorporation of synthetic data improved prediction accuracy and perceptual similarity, enhancing the model's generalizability.
• AI predictions successfully captured biologically meaningful structure-function relationships, demonstrating that sarcomere organization correlates with contractile output and prediction fidelity.

Conclusions:

• The developed generative AI framework offers a scalable and interpretable strategy for inferring cardiomyocyte function from static morphology, eliminating the need for time-lapse imaging.
• This approach positions generative AI as a powerful tool for bridging cellular structure and function, enabling high-throughput functional phenotyping in cardiac research.
• The framework advances in vitro cardiac modeling by providing an efficient method for assessing cardiomyocyte behavior.