Deep learning for determining the difficulty of endodontic treatment: a pilot study

• Egile Nagusiak: Hamed Karkehabadi (Egilea), Elham Khoshbin (Egilea), Nikoo Ghasemi (Egilea), Amal Mahavi (Egilea), Hossein Mohammad-Rahimi (Egilea), Soroush Sadr (Egilea)
• Formatua: Liburua
• Argitaratua: BMC, 2024-05-01T00:00:00Z.
• Gaiak: article
• Sarrera elektronikoa: Connect to this object online.

Abstract Background To develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs. Methods A dataset of 1,386 periapical radiographs was compiled from two clinical sites. Two dentists and two endodontists annotated the radiographs fo...