AIMC Topic: Periapical Diseases
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Automated classification of panoramic radiographs with inflammatory periapical lesions using a CNN-LSTM architecture.
Journal of dentistry
Mar 16, 2025
OBJECTIVES: Considering Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network approaches have shown promising image classification performance, the aim of this study was to compare the performance of novel Convolutional Neural ...
A novel AI model for detecting periapical lesion on CBCT: CBCT-SAM.
Journal of dentistry
Dec 10, 2024
OBJECTIVES: Periapical lesions are not always evident on radiographic scans. Sometimes, asymptomatic or initial periapical lesions on cone-beam computed tomography (CBCT) could be missed by inexperienced dentists, especially when the scan has a large...
Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study.
BMC oral health
Nov 1, 2024
Periapical periodontitis may manifest as a radiographic lesion radiographically. Periapical lesions are amongst the most common dental pathologies that present as periapical radiolucencies on panoramic radiographs. The objective of this research is t...
Artificial intelligence for detecting periapical radiolucencies: A systematic review and meta-analysis.
Journal of dentistry
Jun 6, 2024
OBJECTIVES: Dentists' diagnostic accuracy in detecting periapical radiolucency varies considerably. This systematic review and meta-analysis aimed to investigate the accuracy of artificial intelligence (AI) for detecting periapical radiolucency.
Artificial intelligence vs. semi-automated segmentation for assessment of dental periapical lesion volume index score: A cone-beam CT study.
Computers in biology and medicine
Apr 28, 2024
INTRODUCTION: Cone beam computed tomography periapical volume index (CBCTPAVI) is a categorisation tool to assess periapical lesion size in three-dimensions and predict treatment outcomes. This index was determined using a time-consuming semi-automat...
Multi-model deep learning approach for segmentation of teeth and periapical lesions on pantomographs.
Oral surgery, oral medicine, oral pathology and oral radiology
Nov 26, 2023
INTRODUCTION: The fields of medicine and dentistry are beginning to integrate artificial intelligence (AI) in diagnostics. This may reduce subjectivity and improve the accuracy of diagnoses and treatment planning. Current evidence on pathosis detecti...
Automatic Detection of Periapical Osteolytic Lesions on Cone-beam Computed Tomography Using Deep Convolutional Neuronal Networks.
Journal of endodontics
Aug 8, 2022
INTRODUCTION: Cone-beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiol...
Artificial intelligence system seems to be able to detect a high proportion of periapical lesions in cone-beam computed tomographic images.
Journal of the American Dental Association (1939)
Jul 14, 2020
A comparative analysis of deep learning models for assisting in the diagnosis of periapical lesions in periapical radiographs.
BMC oral health
May 26, 2025
PURPOSE: Numerous studies have investigated the use of convolutional neural network (CNN) models for detecting periapical lesions(PLs). However, limited research has focused on evaluating their potential in assisting clinicians with diagnosis. This s...
The influence of a deep learning tool on the performance of oral and maxillofacial radiologists in the detection of apical radiolucencies.
Dento maxillo facial radiology
Feb 1, 2025
OBJECTIVES: This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effec...
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