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Title: Real-time detection of acromegaly from facial images with artificial intelligence
Authors: Kızılgül, M.
Karakış, R.
Dogan, N.
Bostan, H.
Yapıcı, M.M.
Gül, U.
Uçan, B.
Keywords: acromegaly
artificial intelligence
deep learning
artificial intelligence
cross-sectional study
diagnostic imaging
machine learning
Artificial Intelligence
Cross-Sectional Studies
Machine Learning
Neural Networks, Computer
Issue Date: 2023
Publisher: NLM (Medline)
Abstract: OBJECTIVE: Despite improvements in diagnostic methods, acromegaly is still a late-diagnosed disease. In this study, it was aimed to automatically recognize acromegaly disease from facial images by using deep learning methods and to facilitate the detection of the disease. DESIGN: Cross-sectional, single-centre study. METHODS: The study included 77 acromegaly (52.56 ± 11.74, 34 males/43 females) patients and 71 healthy controls (48.47 ± 8.91, 39 males/32 females), considering gender and age compatibility. At the time of the photography, 56/77 (73%) of the acromegaly patients were in remission. Normalized images were obtained by scaling, aligning, and cropping video frames. Three architectures named ResNet50, DenseNet121, and InceptionV3 were used for the transfer learning-based convolutional neural network (CNN) model developed to classify face images as "Healthy" or "Acromegaly". Additionally, we trained and integrated these CNN machine learning methods to create an Ensemble Method (EM) for facial detection of acromegaly. RESULTS: The positive predictive values obtained for acromegaly with the ResNet50, DenseNet121, InceptionV3, and EM were calculated as 0.958, 0.965, 0.962, and 0.997, respectively. The average sensitivity, specificity, precision, and correlation coefficient values calculated for each of the ResNet50, DenseNet121, and InceptionV3 models are quite close. On the other hand, EM outperformed these three CNN architectures and provided the best overall performance in terms of sensitivity, specificity, accuracy, and precision as 0.997, 0.997, 0.997, and 0.998, respectively. CONCLUSIONS: The present study provided evidence that the proposed AcroEnsemble Model might detect acromegaly from facial images with high performance. This highlights that artificial intelligence programs are promising methods for detecting acromegaly in the future. © The Author(s) 2023. Published by Oxford University Press on behalf of (ESE) European Society of Endocrinology. All rights reserved. For permissions, please e-mail:
ISSN: 1479-683X
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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