Back to Search
Start Over
Automatic detection of acromegaly from facial photographs using machine learning methods
- Source :
- EBioMedicine, Vol 27, Iss C, Pp 94-102 (2018), EBioMedicine
- Publication Year :
- 2017
-
Abstract
- 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.<br />Highlights • An automatic and handy diagnosis system for acromegaly was developed. • 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 algorithm allows medical practitioners and patients to proactively track face changes and detect acromegaly earlier and automatically, thus to facilitate cures and increase the likelihood of preventing irreversible complications of excessive secretion of growth hormone. We developed an automatic and handy diagnosis system for acromegaly, which can allow medical practitioners and patients to proactively track face changes and detect acromegaly earlier and automatically, thus to facilitate cures and increase the likelihood of preventing irreversible complications of excessive secretion of growth hormone.
- Subjects :
- Male
Artificial intelligence
Computer science
lcsh:Medicine
Convolutional neural network
030209 endocrinology & metabolism
Machine learning
computer.software_genre
Facial recognition system
General Biochemistry, Genetics and Molecular Biology
Automation
03 medical and health sciences
0302 clinical medicine
Acromegaly
Photography
medicine
Humans
Face recognition
Minimum bounding rectangle
lcsh:R5-920
Pixel
business.industry
lcsh:R
General Medicine
Middle Aged
Automatic acromegaly diagnosis
medicine.disease
Support vector machine
Face
030220 oncology & carcinogenesis
Face (geometry)
Female
lcsh:Medicine (General)
business
Growth hormone suppression test
computer
Algorithms
Research Paper
Subjects
Details
- Language :
- English
- ISSN :
- 23523964
- Volume :
- 27
- Database :
- OpenAIRE
- Journal :
- EBioMedicine
- Accession number :
- edsair.doi.dedup.....701b42c68faee240c660201d26dddf0e