Caries lesions diagnosis with deep convolutional neural network in intraoral QLF images by handheld device.

ABSTRACT:

Objectives

This study investigated the effectiveness of a deep convolutional neural network (CNN) in diagnosing and staging caries lesions in quantitative light-induced fluorescence (QLF) images taken by a self-manufactured handheld device.

Methods

A small toothbrush-like device consisting of a 400 nm UV light-emitting lamp with a 470 nm filter was manufactured for intraoral imaging. A total of 133 cases with 9,478 QLF images of teeth were included for caries lesion evaluation using a CNN model. The database was divided into development, validation, and testing cohorts at a 7:2:1 ratio. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated for model perfor

SUBMITTER: Tan R 

PROVIDER: S-EPMC11218122 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature