Kailas Vodrahalli1,*,†, Roxana Daneshjou2,3,*,†, Roberto A Novoa2,4, Albert Chiou2, Justin M Ko2, James Zou1,3,*
1Department of Electrical Engineering, Stanford University
2Department of Dermatology, Stanford University School of Medicine
3Department of Biomedical Data Science, Stanford University School of Medicine
4Department of Pathology, Stanford University School of Medicine
†Authors contributed equally to this work
*Corresponding author
Email: kailasv@stanford.edu, roxanad@stanford.edu, jamesz@stanford.edu
Pacific Symposium on Biocomputing 26:220-231(2021)
© 2021 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
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.