Enhancing Diagnostic Accuracy in Echocardiography with Deep Learning Techniques

Echocardiography is a critical tool in the diagnosis and monitoring of cardiovascular diseases. However, accurate interpretation of echocardiographic videos can be challenging and often relies on the expertise of the interpreting physician. In this study, propose a novel approach leveraging state-of-the-art deep learning techniques, specifically the BERT and GPT-2 algorithms, to enhance diagnostic accuracy in echocardiography. The BERT (Bidirectional Encoder Representations from Transformers) algorithm, originally designed for natural language processing tasks, has demonstrated remarkable capabilities in capturing contextual information from sequential data. In study, adapt BERT to process sequences of echocardiographic frames, effectively extracting intricate patterns and relationships that may elude conventional analysis methods. Additionally, employ the GPT-2 (Generative Pre-trained Transformer 2) algorithm, renowned for its proficiency in generating coherent and contextually appropriate text. In application, GPT-2 is employed to generate detailed textual descriptions of echocardiographic images. This text-based output serves as an additional source of information for the diagnostic process. To evaluate the efficacy of proposed approach, conducted extensive experiments on a diverse dataset of echocardiographic images. The diagnostic accuracy of deep learning-based system was measured against a benchmark of expert cardiologists’ interpretations. The results demonstrate a significant improvement in diagnostic accuracy, with the BERT algorithm achieving an accuracy of 75.36 percent and the GPT-2 algorithm achieving an accuracy of 83.36 percent. The promising results obtained suggest the potential of these techniques to revolutionize the field of cardiovascular videos, ultimately leading to more accurate and timely diagnoses for patients with cardiovascular diseases.