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Image quality assessment for closed-loop computer-assisted lung ultrasound
We describe a novel, two-stage computer assistance system for lung anoma...
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A Machine Learning System for Retaining Patients in HIV Care
Retaining persons living with HIV (PLWH) in medical care is paramount to...
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CNN-based Repetitive self-revised learning for photos' aesthetics imbalanced classification
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Hi Sigma, do I have the Coronavirus?: Call for a New Artificial Intelligence Approach to Support Health Care Professionals Dealing With The COVID-19 Pandemic
Just like your phone can detect what song is playing in crowded spaces, ...
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Automatic Assessment of Artistic Quality of Photos
This paper proposes a technique to assess the aesthetic quality of photo...
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Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?
Antibiotic resistance constitutes a major health threat. Predicting bact...
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Improving Mechanical Ventilator Clinical Decision Support Systems with A Machine Learning Classifier for Determining Ventilator Mode
Clinical decision support systems (CDSS) will play an in-creasing role i...
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TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos
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 images, while retaining 80 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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