Facial Analysis in Acromegaly Using Machine Learning: Toward Earlier Diagnosis
Our findings demonstrate that acromegaly-related facial features can be detected years before clinical diagnosis using machine learning. By combining high-performance deep models with interpretable approaches, this study supports the potential for artificial intelligence-based facial screening tools to aid in early detection of acromegaly.
Authors
- Lala Soltanova (ORCID: https://orcid.org/0000-0002-4420-4721)
- Zehra Kara (ORCID: https://orcid.org/0000-0002-6029-8991)
- Sabriye Sibel Taze (ORCID: https://orcid.org/0000-0002-9652-5498)
- Pınar Kadıoğlu (ORCID: https://orcid.org/0000-0002-8329-140X)
- İlkin Muradov (ORCID: https://orcid.org/0000-0002-1906-0535)
- Ayyuce Begum Bektas
- Gönenç Onay
- Serdar Şahin (ORCID: https://orcid.org/0000-0002-6233-5418)
- Hande Mefkure Özkaya (ORCID: https://orcid.org/0000-0001-6207-7941)
- Banu Betül Kocaman (ORCID: https://orcid.org/0009-0004-9281-1771)
- Mouloud Adel
- Oguzhan Recep Akkol
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- Istanbul University-Cerrahpaşa (TR)
- Institut Fresnel (FR)
- Galatasaray University (TR)
Publication Details
- Journal
- The Journal of Clinical Endocrinology & Metabolism
- Published
- 2025-08-20
- DOI
- https://doi.org/10.1210/clinem/dgaf477
- Citations
- 2
- Primary Topic
- Pituitary Gland Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 2.59