Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system

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 re...

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Autores: Rashwan, Hatem A., Marques Pamies, Montserrat, Ruiz, Sabina, Gil, Joan, Asensio-Wandosell, Diego, Martínez Momblán, Ma. Antonia, Vázquez, Federico, Salinas, Isabel, Ciriza, Raquel, Jordà Ramos, Mireia, Chanson, Philippe, Valassi, Elena, Abdelnasser, Mohamed, Puig, Domènec, Puig-Domingo, Manel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Oviedo (UNIOVI)
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/225294
Acceso en línea:https://hdl.handle.net/2445/225294
Access Level:acceso abierto
Palabra clave:Acromegàlia
Intel·ligència artificial
Diagnòstic per la imatge
Acromegaly
Artificial intelligence
Diagnostic imaging
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spelling Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace systemRashwan, Hatem A.Marques Pamies, MontserratRuiz, SabinaGil, JoanAsensio-Wandosell, DiegoMartínez Momblán, Ma. AntoniaVázquez, FedericoSalinas, IsabelCiriza, RaquelJordà Ramos, MireiaChanson, PhilippeValassi, ElenaAbdelnasser, MohamedPuig, DomènecPuig-Domingo, ManelAcromegàliaIntel·ligència artificialDiagnòstic per la imatgeAcromegalyArtificial intelligenceDiagnostic imagingPurpose: 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.Springer Verlag2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/225294Articles publicats en revistes (Infermeria Fonamental i Clínica)reponame:Dipòsit Digital de la UBinstname:Universidad de Oviedo (UNIOVI)InglésReproducció del document publicat a: https://doi.org/10.1007/s11102-025-01515-2Pituitary, 2025, vol. 28https://doi.org/10.1007/s11102-025-01515-2cc by (c) Rashwan, Hatem A. et al., 2025https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2252942026-05-27T06:46:51Z
dc.title.none.fl_str_mv Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system
title Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system
spellingShingle Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system
Rashwan, Hatem A.
Acromegàlia
Intel·ligència artificial
Diagnòstic per la imatge
Acromegaly
Artificial intelligence
Diagnostic imaging
title_short Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system
title_full Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system
title_fullStr Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system
title_full_unstemmed Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system
title_sort Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system
dc.creator.none.fl_str_mv Rashwan, Hatem A.
Marques Pamies, Montserrat
Ruiz, Sabina
Gil, Joan
Asensio-Wandosell, Diego
Martínez Momblán, Ma. Antonia
Vázquez, Federico
Salinas, Isabel
Ciriza, Raquel
Jordà Ramos, Mireia
Chanson, Philippe
Valassi, Elena
Abdelnasser, Mohamed
Puig, Domènec
Puig-Domingo, Manel
author Rashwan, Hatem A.
author_facet Rashwan, Hatem A.
Marques Pamies, Montserrat
Ruiz, Sabina
Gil, Joan
Asensio-Wandosell, Diego
Martínez Momblán, Ma. Antonia
Vázquez, Federico
Salinas, Isabel
Ciriza, Raquel
Jordà Ramos, Mireia
Chanson, Philippe
Valassi, Elena
Abdelnasser, Mohamed
Puig, Domènec
Puig-Domingo, Manel
author_role author
author2 Marques Pamies, Montserrat
Ruiz, Sabina
Gil, Joan
Asensio-Wandosell, Diego
Martínez Momblán, Ma. Antonia
Vázquez, Federico
Salinas, Isabel
Ciriza, Raquel
Jordà Ramos, Mireia
Chanson, Philippe
Valassi, Elena
Abdelnasser, Mohamed
Puig, Domènec
Puig-Domingo, Manel
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Acromegàlia
Intel·ligència artificial
Diagnòstic per la imatge
Acromegaly
Artificial intelligence
Diagnostic imaging
topic Acromegàlia
Intel·ligència artificial
Diagnòstic per la imatge
Acromegaly
Artificial intelligence
Diagnostic imaging
description 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.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/225294
url https://hdl.handle.net/2445/225294
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1007/s11102-025-01515-2
Pituitary, 2025, vol. 28
https://doi.org/10.1007/s11102-025-01515-2
dc.rights.none.fl_str_mv cc by (c) Rashwan, Hatem A. et al., 2025
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc by (c) Rashwan, Hatem A. et al., 2025
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv Articles publicats en revistes (Infermeria Fonamental i Clínica)
reponame:Dipòsit Digital de la UB
instname:Universidad de Oviedo (UNIOVI)
instname_str Universidad de Oviedo (UNIOVI)
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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