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
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
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
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
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
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
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
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...

The detection of apical radiolucencies in periapical radiographs: A comparison between an artificial intelligence platform and expert endodontists with CBCT serving as the diagnostic benchmark.

International endodontic journal
AIM: Accurate detection of periapical radiolucent lesions (PARLs) is crucial for endodontic diagnosis. While cone beam computed tomography (CBCT) is considered the radiographic gold standard for detecting PARLs in non-root filled teeth, its use is of...

A comparative analysis of deep learning models for assisting in the diagnosis of periapical lesions in periapical radiographs.

BMC oral health
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...