Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Real-time detection of acromegaly from facial images with artificial intelligence
European Journal of Endocrinology · Ocak 2023
YÖKSİS Kayıtları
Real-time detection of acromegaly from facial images with artificial intelligence
European Journal of Endocrinology · 2023 SCI-Expanded
PROFESÖR NURETTİN DOĞAN →
Makale Bilgileri
DergiEuropean Journal of Endocrinology
Yayın TarihiOcak 2023
Cilt / Sayfa188 · 158-165
Scopus ID2-s2.0-85147460775
Erişim🔓 Açık Erişim
Özet
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.
Yazarlar (11)
1
Muhammed Kizilgul
2
Rukiye Karakis
3
Nurettin Doğan
ORCID: 0000-0002-8267-8469
4
Hayri Bostan
ORCID: 0000-0002-4957-9856
5
Muhammed Mutlu Yapici
6
Umran Gul
7
Bekir Uçan
ORCID: 0000-0002-0810-5224
8
Elvan Duman
9
Hakan Duger
10
Erman Çakal
ORCID: 0000-0003-4455-7276
11
Ömer Akın
ORCID: 0000-0002-6359-1640
Anahtar Kelimeler
acromegaly
artificial intelligence
deep learning
detection
Kurumlar
Ankara Üniversitesi
Ankara Turkey
Burdur Mehmet Akif Ersoy Üniversitesi
Burdur Turkey
Cumhuriyet Üniversitesi
Sivas Turkey
Selçuk Üniversitesi
Selçuklu Turkey
TOBB University of Economics and Technology
Ankara Turkey
University of Health Sciences
Istanbul Turkey
Metrikler
3
Atıf
11
Yazar
4
Anahtar Kelime