Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks

Abstract

Transoesophageal echocardiography (TEE) is a valuable diagnostic and monitoring imaging modality. Proper image acquisition is essential for diagnosis, yet current assessment techniques are solely based on manual expert review. This paper presents a supervised deep learning framework for automatically evaluating and grading the quality of TEE images. To obtain the necessary dataset, 38 participants of varied experience performed TEE exams with a high-fidelity virtual reality (VR) platform. Two Convolutional Neural Network (CNN) architectures, AlexNet and VGG, structured to perform regression, were finetuned and validated on manually graded images from three evaluators. Two different scoring strategies, a criteria-based percentage and an overall general impression, were used. The developed CNN models estimate the average score with a root mean square accuracy ranging between 84%-93%, indicating the ability to replicate expert valuation. Proposed strategies for automated TEE assessment can have a significant impact on the training process of new TEE operators, providing direct feedback and facilitating the development of the necessary dexterous skills.

Advances in TEE-Centric Intraprocedural Multimodal Image Guidance for Congenital and Structural Heart Disease

“…AI-guided visualization and automated skill assessments built into our system may accelerate clinicians’ transition from novice to expert. Success stories in TEE training will be exponential if appropriate 3D visualization-based training on an integrated platform can be established [ 111 , 112 ].…”

“…This AI-driven approach also holds promise in aiding novice TEE trainees. [ 112 ] 2018 3D-printing, 3D-VR headset, and desktop delivery This study assessed the efficiency of three distinct 3D techniques for preoperative planning and management. The results revealed that clinicians expressed a willingness to incorporate 3D virtual reality.…”