TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos

• URL: http://arxiv.org/abs/2010.02086v1
• Date: Thu, 1 Oct 2020 17:47:57 GMT
• Title: TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos
• Authors: Kailas Vodrahalli, Roxana Daneshjou, Roberto A Novoa, Albert Chiou, Justin M Ko, and James Zou
• Abstract summary: We focus on teledermatology, where photo quality is particularly important. For telemedicine, dermatologists request that patients submit images of their lesions for assessment. These images are often of insufficient quality to make a clinical diagnosis since patients do not have experience taking clinical photos. We propose an automated image assessment machine learning pipeline, TrueImage, to detect poor quality dermatology photos and to guide patients in taking better photos.
• Score: 8.27648210293057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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