Leveraging NVIDIA Clara for Real-Time Cardiac Image Segmentation and Diagnosis

Medical imaging is crucial for heart diagnosis, but outdated algorithms and hardware result in delayed processing and low accuracy. Using NVIDIA Clara, a GPU-accelerated platform, the study proposes real-time cardiac picture segmentation and diagnosis. The proposed system uses advanced DL algorithms built for cardiac imaging to enhance processing speed and accuracy. Employing rigorous data preprocessing and deep learning model able to perform precise partitioning of cardiac structures and positioning defects that lead to quick heart arrhythmia diagnose in a timely manner. It also substantially reduces processing times compared to older systems while improving the contour quality and diagnosability. The system achieves the following DSC values: 0.93 for the left ventricle; 0.92 for right; 0.90 for left atrium; 0.89 for right ventricle and 0.91 for the major blood vessels. With diagnostic accuracy scores higher than the current system Sensitivity 94.2%, Specificity 92.8%, and Accuracy 93.5%. The method has potential to enhance cardiac care by facilitating earlier interventions leading better patient outcomes.