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Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs

Medicine and Health

Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs

R. Abdelazim and E. M. Fouad

This innovative study by Riem Abdelazim and Eman M. Fouad introduces an AI-based system that precisely detects root fractures in periapical radiographs, automating dental diagnosis with remarkable accuracy. Using five advanced pretrained models, the results highlight VGG16’s superior performance, while DenseNet models tackle overfitting effectively. Discover how AI is redefining dental diagnostics!

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Playback language: English
Abstract
This study proposes an AI-based system for detecting root fractures in periapical radiographs, aiming to automate dental diagnosis. Using 400 images (200 fractured, 200 unfractured), five pretrained models (VGG16, VGG19, ResNet50, DenseNet121, DenseNet169) were employed with a voting mechanism. VGG16 showed the best performance with low losses and high specificity, sensitivity, and PPV. DenseNet121 and DenseNet169 effectively addressed overfitting, achieving balanced metrics and impressive PPVs. The AI-based system demonstrated high precision and sensitivity for detecting root fractures.
Publisher
BDJ Open
Published On
Oct 01, 2024
Authors
Riem Abdelazim, Eman M. Fouad
Tags
AI-based system
root fractures
periapical radiographs
dental diagnosis
pretrained models
overfitting
sensitivity

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