Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality
This addresses a major limitation in telemedicine for patients and clinicians by improving diagnostic reliability, though it is incremental as it builds on existing AI methods for image quality assessment.
The researchers tackled the problem of poor photo quality in telemedicine for skin conditions by developing TrueImage 2.0, an AI model that assesses photo quality and provides real-time feedback, resulting in a 68.0% reduction in patients with poor-quality images in a clinical pilot.
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%.