Evaluating artificial intelligence for a focal nodular hyperplasia diagnosis using magnetic resonance imaging: preliminary findings
AI is studied to provide assisted or automated interpretation of radiological images with an accurate and reproducible imaging diagnosis.
Authors
- Bülent Aydınlı (ORCID: https://orcid.org/0000-0002-2242-1012)
- Nil Tokgöz (ORCID: https://orcid.org/0000-0003-2812-1528)
- Gülsüm Özlem Elpek (ORCID: https://orcid.org/0000-0002-1237-5454)
- Mustafa Harman (ORCID: https://orcid.org/0000-0001-9234-887X)
- Önder Durmaz (ORCID: https://orcid.org/0000-0003-1157-2787)
- Ali Avanaz (ORCID: https://orcid.org/0000-0002-4559-4258)
- Sonay Aydın (ORCID: https://orcid.org/0000-0002-3812-6333)
- Merve Yazol (ORCID: https://orcid.org/0000-0003-1437-8998)
- Volkan Kızılgöz (ORCID: https://orcid.org/0000-0003-3450-711X)
- Ramazan Terzi (ORCID: https://orcid.org/0000-0003-2345-8666)
- Mecit Kantarcı (ORCID: https://orcid.org/0000-0002-1043-6719)
- Ayşegül Sağır Kahraman (ORCID: https://orcid.org/0000-0002-2147-1181)
- Adil Kılıç (ORCID: https://orcid.org/0009-0004-9358-8743)
- Halime Kabalcı (ORCID: https://orcid.org/0009-0009-2027-1653)
Institutions
- Erzincan Binali Yıldırım University (TR)
- Akdeniz University (TR)
- Ege University (TR)
- Atatürk University (TR)
- Gazi University (TR)
Publication Details
- Journal
- Diagnostic and Interventional Radiology
- Published
- 2025-03-26
- DOI
- https://doi.org/10.4274/dir.2025.243095
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72