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BACKGROUND: Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. METHODS: In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. RESULTS: The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. CONCLUSIONS: Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity.

Original publication

DOI

10.1016/j.ebiom.2017.12.015

Type

Journal article

Journal

Ebiomedicine

Publication Date

01/2018

Volume

27

Pages

94 - 102

Keywords

Artificial intelligence, Automatic acromegaly diagnosis, Convolutional neural network, Face recognition, Machine learning