Deep Learning for Caries Detection: A Systematic Review

Hossein Mohammad-rahimi, Saeed Reza Motamedian, Mohammad Hossein Rohban, Joachim Krois, Sergio Uribe, Erfan Mahmoudi Nia, Rata Rokhshad, Mohadeseh Nadimi, Falk Schwendicke (Corresponding Author)

Department of Conservative Dentistry and Oral Health

Research output: Contribution to journal › Review article › peer-review

102 Citations (Scopus)

309 Downloads (Pure)

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Caries-AI: Diagnostic accuracy of artificial intelligence systems for radiographic caries detection and high-quality dataset of annotated radiographs
E. Uribe, S. (Project leader), Apse, I. A. (Assistant (student)), Radziņš, O. (Expert) & Maldupa, I. (Leading expert)
Riga Stradins University
26/07/21 → 25/07/22
Project: RSU grants

Deep Learning for Caries Detection: A Systematic Review

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Caries-AI: Diagnostic accuracy of artificial intelligence systems for radiographic caries detection and high-quality dataset of annotated radiographs