AI-Dentify: deep learning for proximal caries detection on bitewing x-ray - HUNT4 Oral Health Study
Abstract Background Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has th...
Saved in:
Main Authors: | Javier Pérez de Frutos (Author), Ragnhild Holden Helland (Author), Shreya Desai (Author), Line Cathrine Nymoen (Author), Thomas Langø (Author), Theodor Remman (Author), Abhijit Sen (Author) |
---|---|
Format: | Book |
Published: |
BMC,
2024-03-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evaluation of the effects of postprocessing settings in digital bitewing radiographs on proximal caries detection
by: Mehrdad Abdinian, et al.
Published: (2024) -
NEAR-INFRARED TRANSILLUMINATION COMPARED TO DIGITAL BITEWING RADIOGRAPHY FOR PROXIMAL CARIES DETECTION
by: Veselina Todorova, et al.
Published: (2023) -
The Comparison of Dentine Thickness Under Proximal Caries Between Bitewing Radiographs and Tooth Structure
by: Khosravi K, et al.
Published: (2001) -
The validity of self-reported number of teeth and edentulousness among Norwegian older adults, the HUNT Study
by: Hedda Høvik, et al.
Published: (2022) -
Estimation of Remnant Dentin Thickness under Proximal Caries Using Digital Bitewing Radiography: An In-Vitro Study
by: Masoomeh Afsa, et al.
Published: (2016)