Evaluation of a YOLOv5x-based deep learning model for interproximal caries segmentation on bitewing radiographs across primary and permanent teeth
Hatice Selin Güçlü, Akif Demirel, Kaan Orhan · 2025
Read Evaluation of a YOLOv5x-based deep learning model for interproximal caries segmentation on bitewing radiographs across primary and permanent teeth by Hatice Selin Güçlü, Akif Demirel, Kaan Orhan (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12860016.txt.
Description
Hatice Guclu and co-authors present a 2025 study on an AI model that outlines tooth decay between teeth on bitewing X-rays. Such decay hides where teeth touch, and dentists can miss early lesions. The team trained a segmentation model on images of both baby and adult teeth.
Two thousand radiographs, half from each tooth type, were marked by hand, giving over five thousand lesions. The model learned from most images and was tested on the rest. It found decay with good balance of precision and recall, scoring somewhat higher on baby teeth than on adult teeth. The authors present the scores in plain clinical terms and discuss where the model could support dentists.
Catalog record
| CREATOR | Hatice Selin Güçlü, Akif Demirel, Kaan Orhan |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 58 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |