Development of an Automated System for Cardiomyocyte Activity Using Computer Vision

Computer vision, a pivotal field within computer science, empowers machines to interpret and analyse visual information such as images and videos. Its growing application in healthcare, particularly in the diagnosis and treatment of cardiac conditions, underscores its transformative potential. Traditional methods for detecting cardiac beat rates are largely manual, making them time-consuming and labour-intensive, thereby limiting their scalability in clinical contexts. To address this gap, there is a critical need for an automated system capable of identifying cells in video data and extracting key parameters such as beat rate, cell area during systole and diastole, and beat duration. This study introduces a novel computer vision-based framework that automates the detection of heart cell contractions from video recordings. By employing motion segmentation, masking techniques, and machine learning algorithms, the system efficiently identifies active cardiomyocytes, calculates beats per minute (BPM), and measures the time taken for a complete contraction-relaxation cycle. This approach not only improves diagnostic accuracy but also contributes to more efficient and scalable cardiac assessments, representing a significant advancement in computational healthcare.