EchoVQA: Enabling Conversational Assistance for Point-of-Care Cardiac Ultrasound
This work addresses the expertise gap in point-of-care cardiac ultrasound by enabling interactive clinical assistance through VQA, particularly for novice operators.
EchoVQA introduces the first large-scale VQA dataset for echocardiography with 14,299 images and 74,819 QA pairs, including point-of-care acquisitions and acquisition guidance questions. Their parameter-efficient method achieves state-of-the-art performance on most benchmarks with significantly fewer trainable parameters.
Point-of-care transthoracic echocardiography (TTE) enables cardiac assessment in virtually any clinical setting, yet its diagnostic utility remains constrained by the expertise required for image acquisition and interpretation. Visual question answering (VQA) offers a promising paradigm for bridging this expertise gap through interactive clinical assistance, but existing echocardiography VQA datasets are limited in scale, restricted to high-quality images, and only cover a few views. We introduce EchoVQA, the first large-scale VQA dataset for echocardiography, comprising 14,299 images and 74,819 question-answer pairs. The dataset integrates public sources (EchoNet-Dynamic, CAMUS) with our own point-of-care acquisitions from two handheld probes (Lumify, Clarius), spanning diverse views and including both high-quality and suboptimal images. Uniquely, EchoVQA includes acquisition guidance questions to help users optimize transducer positioning toward a diagnostic apical 4-chamber view for left ventricular ejection fraction estimation -- a challenging task for novice operators in point-of-care settings. We further develop a parameter-efficient method based on multimodal learnable prompts achieving state-of-the-art performance on most benchmarks, including EchoVQA, with significantly less trainable parameters than existing state-of-the-art approaches.