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%.

在 COVID-19 疫情期间，远程医疗使用得到加速，并且皮肤病是常见的使用案例。为了解决患者拍摄的照片质量差的问题，我们开发了 TrueImage 2.0，一种人工智能（AI）模型，用于评估患者的远程医疗照片质量，并提供实时反馈以改进照片质量。