  Telehealth is an increasingly critical component of the health care
ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of
telehealth has exposed limitations in the existing infrastructure. In this
paper, we study and highlight photo quality as a major challenge in the
telehealth workflow. We focus on teledermatology, where photo quality is
particularly important; the framework proposed here can be generalized to other
health domains. For telemedicine, dermatologists request that patients submit
images of their lesions for assessment. However, these images are often of
insufficient quality to make a clinical diagnosis since patients do not have
experience taking clinical photos. A clinician has to manually triage poor
quality images and request new images to be submitted, leading to wasted time
for both the clinician and the patient. We propose an automated image
assessment machine learning pipeline, TrueImage, to detect poor quality
dermatology photos and to guide patients in taking better photos. Our
experiments indicate that TrueImage can reject 50% of the sub-par quality
images, while retaining 80% of good quality images patients send in, despite
heterogeneity and limitations in the training data. These promising results
suggest that our solution is feasible and can improve the quality of
teledermatology care.
