Telemedicine utilization was accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, the quality of photographs sent by patients remains a major limitation. To address this issue, we developed TrueImage 2.0, an artificial intelligence (AI) model for assessing patient photo quality for telemedicine and providing real-time feedback to patients for photo quality improvement. TrueImage 2.0 was trained on 1700 telemedicine images annotated by clinicians for photo quality. On a retrospective dataset of 357 telemedicine images, TrueImage 2.0 effectively identified poor quality images (Receiver operator curve area under the curve (ROC-AUC) =0.78) and the reason for poor quality (Blurry ROC-AUC=0.84, Lighting issues ROC-AUC=0.70). The performance is consistent across age, gender, and skin tone. Next, we assessed whether patient-TrueImage 2.0 interaction led to an improvement in submitted photo quality through a prospective clinical pilot study with 98 patients. TrueImage 2.0 reduced the number of patients with a poor-quality image by 68.0%.
@article{arxiv.2209.09105,
title = {Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality},
author = {Kailas Vodrahalli and Justin Ko and Albert S. Chiou and Roberto Novoa and Abubakar Abid and Michelle Phung and Kiana Yekrang and Paige Petrone and James Zou and Roxana Daneshjou},
journal= {arXiv preprint arXiv:2209.09105},
year = {2022}
}