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Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning

Published version
Peer-reviewed

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Authors

Kong, Yanguo 
He, Cheng 
Liu, Changsong 
Wang, Liting 

Abstract

Abstract: Due to acromegaly’s insidious onset and slow progression, its diagnosis is usually delayed, thus causing severe complications and treatment difficulty. A convenient screening method is imperative. Based on our previous work, we herein developed a new automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning on the data of 2148 photographs at different severity levels. Each photograph was given a score reflecting its severity (range 1~3). Our developed model achieved a prediction accuracy of 90.7% on the internal test dataset and outperformed the performance of ten junior internal medicine physicians (89.0%). The prospect of applying this model to real clinical practices is promising due to its potential health economic benefits.

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Keywords

Letter to the Editor, Severity-classification model, Acromegaly, Facial photographs, Deep learning

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Publisher

BioMed Central
Sponsorship
Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019XK320041)