Acromegaly facial changes analysis using last generation artificial intelligence methodology : the AcroFace system
Rashwan, Hatem A. (Universitat Rovira i Virgili)
Marques-Pamies, Montserrat (Hospital General de Granollers)
Ruiz, Sabina (Universitat Autònoma de Barcelona. Departament de Medicina)
Gil, Joan (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Asensio-Wandosell, Diego (Universitat Autònoma de Barcelona. Departament de Medicina)
Martínez Momblán, Ma. Antonia (Universitat de Barcelona)
Vázquez, Federico (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Salinas, Isabel (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Ciriza, Raquel (Asociación Española de Afectados por Acromegalia (Huesca, Espanya))
Jordà, Mireia (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Chanson, Philippe (Université Paris-Saclay)
Valassi, Elena (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Abdelnasser, Mohamed (Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i Matemàtiques)
Puig, Domènec (Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i Matemàtiques)
Puig Domingo, Manuel (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)

Data:	2025
Resum:	Purpose: To describe the development of the AcroFace system, an AI-based system for early detection of acromegaly, based on facial photographs analysis. Methods: Two types of features were explored: (1) the visual/texture of a set of 2D facial images, and (2) geometric information obtained from a reconstructed 3D model from a single image. We optimized acromegaly detection by integrating SVM for geometric features and CNNs for visual features, each chosen for their strength in processing distinct data types effectively. This combination enhances overall accuracy by leveraging SVM's capability to manage structured, quantitative data and CNNs' proficiency in interpreting complex image textures, thus providing a comprehensive analysis of both geometric alignment and textural anomalies. ResNet-50, VGG-16, MobileNet, Inception V3, DensNet121 and Xception models were trained with an expert endocrinologist-based score as a ground truth. Results: ResNet-50 model as a feature extractor and Support Vector Regression (SVR) with a linear kernel showed the best performance (accuracy δ1 of 75% and δ3 of 89%), followed by the VGG-16 as a feature extractor and SVR with a linear kernel. Geometric features yield less accurate results than visual ones. The validation cohort showed the following performance: precision 0. 90, accuracy 0. 93, F1-Score 0. 92, sensitivity 0. 93 and specificity 0. 93. Conclusion: AcroFace system shows a good performance to discriminate acromegaly and non-acromegaly facial traits that may serve for the detection of acromegaly at an early stage as a screening procedure at a population level.
Ajuts:	Instituto de Salud Carlos III PMP22/00021
Nota:	Altres ajuts: acords transformatius de la UAB
Nota:	Open Access Funding provided by Universitat Autonoma de Barcelona.
Nota:	This study was partially supported by a grant of the Aspire program by Pfizer international and by a grant from the Instituto de Salud Carlos III PMP22/00021 funded by the European Union-Next Generation EU to Manel Puig-Domingo.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Acromegaly ; Facial changes ; Facial analysis ; Artificial intelligence ; Acromegaly detection
Publicat a:	Pituitary, Vol. 28 Núm. 3 (june 2025) , p. 50, ISSN 1573-7403

DOI: 10.1007/s11102-025-01515-2
PMID: 40257631

6 p, 881.7 KB

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