"Determining the efficacy of a machine learning model for measuring periodontal bone loss".

ABSTRACT:

Background

Considering the prevalence of Periodontitis, new tools to help improve its diagnostic workflow could be beneficial. Machine Learning (ML) models have already been used in dentistry to automate radiographic analysis.

Aims

To determine the efficacy of an ML model for automatically measuring Periodontal Bone Loss (PBL) in panoramic radiographs by comparing it to dentists.

Methods

A dataset of 2010 images with and without PBL was segmented using Label Studio. The dataset was split into n = 1970 images for building a training dataset and n = 40 images for building a testing dataset. We propose a model composed of three components. Firstly, statistical inference techniques find probability functions that best describe the segmented dataset. Secondly, Convolutiona

SUBMITTER: Cerda Mardini D 

PROVIDER: S-EPMC10792795 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature