AIMC Topic: Periapical Diseases

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Knowledge-level comparison in pulpal and periapical diseases: dental students versus artificial intelligence models (Gemini, Microsoft Copilot, ChatGPT-3.5, ChatGPT-4o): cross-sectional study.

BMC medical education
BACKGROUND: This study explored the diagnostic accuracy of artificial intelligence (AI) chatbots and dental students when responding to questions related to pulpal and periapical diseases. Rapid advancements in AI have led to increased interest in th...

Impact of artificial intelligence assistance on diagnosing periapical radiolucencies: A randomized controlled trial.

Journal of dentistry
OBJECTIVES: This randomized controlled trial aimed to evaluate the impact of artificial intelligence (AI) assistance on dentists' diagnostic accuracy, confidence, and treatment decisions when detecting periapical radiolucencies (PRs) on panoramic rad...

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...