HSD report: Heart Sound Diagnosis with Echocardiography Reports (2024)

Abstract: Heart sound auscultation holds significant importance in the diagnosis of congenital heart disease. However, existing methods for Heart Sound Diagnosis (HSD) tasks are predominantly limited to a few fixed categories, framing the HSD task as a rigid classification problem that does not fully align with medical practice and offers only limited information to physicians. Besides, such methods do not utilize echocardiography reports, the gold standard in the diagnosis of related diseases. To tackle this challenge, we introduce HSDreport, a new benchmark for HSD, which mandates the direct utilization of heart sounds obtained from auscultation to predict echocardiography reports. This benchmark aims to merge the convenience of auscultation with the comprehensive nature of echocardiography reports. First, we collect a new dataset for this benchmark, comprising 2,275 heart sound samples along with their corresponding reports. Subsequently, we develop a knowledge-aware query-based transformer to handle this task. The intent is to leverage the capabilities of medically pre-trained models and the internal knowledge of large language models (LLMs) to address the task’s inherent complexity and variability, thereby enhancing the robustness and scientific validity of the method. Furthermore, our experimental results indicate that our method significantly outperforms traditional HSD approaches and existing multimodal LLMs in detecting key abnormalities in heart sounds.

## introduction heart sound auscultation is an essential part of clinical medicine and is extensively utilized to screen for congenital heart disease (chd) due to its cost-effectiveness. chd is the most common congenital abnormality, with 13.3 million patients in previous works, heart sound diagnosis (hsd) was treated as a multi-class problem with two to five categories. our new benchmark, starting from echocardiography reports, treats hsd as a twelve-category multi-label task. furthermore, we have developed a knowledge-aware, query-based transformer approach that utilizes medical descriptions to address this novel benchmark. ## conclusion this study introduces hsdreport, a new benchmark and method designed to revolutionize hsd by integrating the diagnostic gold standard of echocardiography with the accessibility of auscultation. by col-lecting a novel dataset of 2,275 heart sound samples paired with echocardiography, we set the stage for a more detailed approach to diagnosing heart conditions. the development of our knowledge-aware query-based transformer model marks a significant advancement in the field, effectively harnessing the potential of medically pre-trained models and the nuanced understanding of llm.