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“Determining the efficacy of a machine learning model for measuring periodontal bone loss” by Diego Cerda Mardini; Patricio Cerda Mardini; Daniela Paz Vicuña Iturriaga; Duniel Ricardo Ortuño Borroto is a Dentistry article available to read on EtoBox.

What is “Determining the efficacy of a machine learning model for measuring periodontal bone loss” about?

## 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, Convolutional Neural Networks extract visual information from the training dataset. Thirdly, an algorithm calculates PBL as a percentage and classifies it in stages. Afterwards, a standardized test compared the model to two radiologists, two periodontists and one general dentist. The test was built using the testing dataset, 40 questions long, done in controlled conditions, with radiologists considered as ground t

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Author

Diego Cerda Mardini; Patricio Cerda Mardini; Daniela Paz Vicuña Iturriaga; Duniel Ricardo Ortuño Borroto

Publisher

BioMed Central

Published

2024

Language

EN

Field

Dentistry (Health Sciences)