Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks

TL;DR

This paper introduces a deep learning framework using CNNs to automatically evaluate and grade TEE image quality, aiming to improve training and diagnostic accuracy.

Contribution

It presents a novel supervised deep learning approach with CNNs for automated TEE image quality assessment, validated on a diverse dataset.

Findings

CNN models achieved 84%-93% accuracy in scoring

Automated assessment can replicate expert evaluations

Potential to enhance TEE training and diagnostics

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 learn ing 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.