JMIR Publications

ABSTRACT

Background:

Point-of-care ultrasound (US) training is increasingly integrated into undergraduate medical education, leading to huge demand for trained faculty members to provide training and feedback.

Objective:

This study aimed to develop an adjunct tool, a deep learning-based feedback model to facilitate student learning.

Methods:

Renal US images (n=2807) were utilized to train a cascaded deep learning-based feedback model, which classified images into 3 labels: optimal, sub-optimal and wrong. Sub-optimal images were further sub-labelled to images with artifact, incorrect gain and/or incorrect position. The model was deployed among year 5 students receiving bedside US training, who were invited to upload renal US images to an online platform for the model to grade the image quality and provide feedback. Mixed-method analysis was used. Students were invited to fill in an online questionnaire to evaluate their learning experience with respect to the effectiveness of the model in enhancing ultrasound training and its usability based on a 5-point scale. Focused group interviews were organized to gain more insights to the successes and challenges of implementation.

Results:

The cohort consisted of 231 year 5 medical students and 98 (42.4%) of them filled in the questionnaire. Among them, 32-48% found the model effective in assisting US training (score 4-5) and 49-75% were satisfied with the usability and their interactions with the model. Focused group interviews identified the model encouraged those who were able to regulate their own learning to be more engaged, but also recognised discordant curricular design and hardware limitations impeded the use of the model.

Conclusions:

A cascaded deep learning-based feedback model was developed and deployed with positive responses from year 5 medical students receiving bedside US training to support self-regulated learning.