Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system
Por:
Rashwan, HA, Marqués-Pamies, M, Ruiz, S, Gil, J, Asensio-Wandosell, D, Martinez-Momblán, MA, Vázquez, F, Salinas, I, Ciriza, R, Jordà, M, Chanson, P, Valassi, E, Abdelnasser, M, Puig, D and Puig-Domingo, M
Publicada:
1 jun 2025
Resumen:
PurposeTo describe the development of the AcroFace system, an AI-based system for early detection of acromegaly, based on facial photographs analysis.MethodsTwo 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.ResultsResNet-50 model as a feature extractor and Support Vector Regression (SVR) with a linear kernel showed the best performance (accuracy delta 1 of 75% and delta 3 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.ConclusionAcroFace 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.
Filiaciones:
Rashwan, HA:
Univ Rovira & Virgili, Dept Comp Engn & Math, Tarragona, Spain
Marqués-Pamies, M:
Autonomous Univ Barcelona, Germans Trias Res Inst & Hosp, Serv Endocrinol & Nutr, Barcelona, Spain
Hosp Granollers, Endocrinol Unit, Barcelona, Spain
Ruiz, S:
Autonomous Univ Barcelona, Germans Trias Res Inst & Hosp, Serv Endocrinol & Nutr, Barcelona, Spain
Gil, J:
Autonomous Univ Barcelona, Germans Trias Res Inst & Hosp, Serv Endocrinol & Nutr, Barcelona, Spain
Inst Salud Carlos III, Dept Med, Autonomous Univ CIBERER Grp 747, Madrid, Spain
Asensio-Wandosell, D:
Autonomous Univ Barcelona, Germans Trias Res Inst & Hosp, Serv Endocrinol & Nutr, Barcelona, Spain
Inst Salud Carlos III, Dept Med, Autonomous Univ CIBERER Grp 747, Madrid, Spain
Martinez-Momblán, MA:
Autonomous Univ Barcelona, Germans Trias Res Inst & Hosp, Serv Endocrinol & Nutr, Barcelona, Spain
Univ Barcelona, Med & Hlth Sci Fac, Nursing Sch, Fundamental & Med Surg Nursing Dept, Barcelona, Spain
:
Autonomous Univ Barcelona, Germans Trias Res Inst & Hosp, Serv Endocrinol & Nutr, Barcelona, Spain
:
Autonomous Univ Barcelona, Germans Trias Res Inst & Hosp, Serv Endocrinol & Nutr, Barcelona, Spain
Ciriza, R:
Spanish Assoc People Acromegaly, Huesca, Spain
:
Autonomous Univ Barcelona, Germans Trias Res Inst & Hosp, Serv Endocrinol & Nutr, Barcelona, Spain
Chanson, P:
Univ Paris Saclay, Hop Bicetre, AP HP,Physiol & Physiopathol Endocriniennes, Ctr Reference Malad Rares Hypophyse,Inserm,Serv En, F-94275 Le Kremlin Bicetre, France
:
Autonomous Univ Barcelona, Germans Trias Res Inst & Hosp, Serv Endocrinol & Nutr, Barcelona, Spain
Inst Salud Carlos III, Dept Med, Autonomous Univ CIBERER Grp 747, Madrid, Spain
Abdelnasser, M:
Univ Rovira & Virgili, Dept Comp Engn & Math, Tarragona, Spain
Puig, D:
Univ Rovira & Virgili, Dept Comp Engn & Math, Tarragona, Spain
:
Autonomous Univ Barcelona, Germans Trias Res Inst & Hosp, Serv Endocrinol & Nutr, Barcelona, Spain
Inst Salud Carlos III, Dept Med, Autonomous Univ CIBERER Grp 747, Madrid, Spain
Green Submitted, hybrid
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