Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality

TL;DR

This paper presents TrueImage 2.0, an AI tool that assesses and improves telemedicine patient photo quality in real-time, significantly reducing poor-quality images in clinical settings.

Contribution

We developed and validated TrueImage 2.0, an AI model that evaluates and enhances telemedicine photo quality, with proven effectiveness in clinical pilot testing.

Findings

Effective identification of poor-quality images (ROC-AUC=0.78)

Reduction of poor-quality images by 68% in clinical pilot

Consistent performance across demographics

Abstract

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%.