2026-04-14T08:30:13Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/130742025-10-24T10:59:24Zcom_2072_451665com_2072_378040col_2072_451666

Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system Rashwan, Hatem A. Asensio-Wandosell, Diego Martínez Momblan, Mª Antonia Marques Pamies, Montserrat Ruiz-Janer, Sabina Gil, Joan [Rashwan HA] Department of Computer Engineering and Mathematics, University of Rovira i Virgili, Tarragona, Spain. [Marqués-Pamies M] Àrea d'endocrinologia (UDEN), Hospital General de Granollers, Granollers, Spain. [Ruiz S, Asensio-Wandosell D] Department of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain. [Gil J] Department of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain. Germans Trias Research Institute and Hospital, Service of Endocrinology and Nutrition, Badalona, Spain. CIBERER group 747, Instituto de salud Carlos III, Madrid, Spain. [Martínez-Momblán MA] Fundamental and Medical-Surgical Nursing Department, Medicine and Health Sciences Faculty, Nursing School, Universitat de Barcelona (UB), L’Hospitalet de Llobregat, Spain Hospital General de Granollers Acromegàlia Intel·ligència artificial Imatges - Anàlisi DISEASES::Musculoskeletal Diseases::Bone Diseases::Bone Diseases, Endocrine::Acromegaly PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence PSYCHIATRY AND PSYCHOLOGY::Psychological Phenomena::Mental Processes::Perception::Pattern Recognition, Physiological::Pattern Recognition, Visual::Facial Recognition ENFERMEDADES::enfermedades musculoesqueléticas::enfermedades óseas::enfermedades óseas endocrinas::acromegalia FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial PSIQUIATRÍA Y PSICOLOGÍA::fenómenos psicológicos::procesos mentales::percepción::reconocimiento fisiológico de modelos::reconocimiento visual de modelos::reconocimiento facial Acromegalia; Inteligencia artificial; Análisis facial Acromegàlia; Intel·ligència artificial; Anàlisi facial Acromegaly; Artificial intelligence; Facial analysis To describe the development of the AcroFace system, an AI-based system for early detection of acromegaly, based on facial photographs analysis. 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. 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. 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. Open Access Funding provided by Universitat Autonoma de Barcelona. 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. 2025-05-12T10:42:02Z 2025-05-12T10:42:02Z 2025-04-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Rashwan HA, Marqués-Pamies M, Ruiz S, Gil J, Asensio-Wandosell D, Martínez-Momblán MA, et al. Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system. Pituitary. 2025 Apr 21;28(3):50. 1573-7403 http://hdl.handle.net/11351/13074 10.1007/s11102-025-01515-2 40257631 http://hdl.handle.net/11351/13074 eng Pituitary;28(3) https://www.doi.org/10.1007/s11102-025-01515-2 Attribution-NonCommercial 4.0 International http://creativecommons.org/licenses/by-nc/3.0/ info:eu-repo/semantics/openAccess application/pdf Springer Scientia