Skills Classification in Cardiac Ultrasound with Temporal Convolution and Domain Knowledge Using a Low-Cost Probe Tracker

Abstract

As point-of-care ultrasound (POCUS) becomes more integrated into clinical practice, it is essential to address all aspects of ultrasound operator proficiency. Ultrasound proficiency requires the ability to acquire, interpret and integrate bedside ultrasound images. The difference in image acquisition psychomotor skills between novice (trainee) and expert (instructor) ultrasonographer has not been described. We created an inexpensive system, called Probe Watch, to record probe motion and assess image acquisition in cardiac POCUS using an inertial measurement device and software for data recording based on open-source components. We designed a temporal convolutional network for skills classification from probe motion that integrates clinical domain knowledge. We further designed data augmentation methods to improve its generalization. Subsequently, we validated the setup and assessment method on a set of novice and expert sonographers performing cardiac ultrasound in a simulation-based training environment. The proposed methods classified participants as novice or expert with areas under the receiver operating characteristic curve of 0.931 and 0.761 for snippets and trials, respectively. Integrating domain knowledge into the neural network had added value. Furthermore, we identified the most discriminative features for assessment. Probe Watch quantifies motion during cardiac ultrasound and provides insight into probe motion behavior. It may be deployed during cardiac ultrasound training to monitor learning curves objectively and automatically.