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Related Experiment Video

Updated: Jan 13, 2026

Detection and Quantitation of Label-Retaining Cells in Mouse Incisors using a 3D Reconstruction Approach after Tissue Clearing

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Deep Learning-Based Detection of Root Numbers in Maxillary Premolars.

Ecem Azgari¹, Cem Azgari², Hesna Sazak Öveçoğlu³

¹Department of Endodontics, Institute of Health Sciences, Marmara University, İstanbul, Turkey.

International Endodontic Journal

|January 6, 2026

Summary

Deep learning models accurately predict maxillary premolar root numbers from panoramic radiographs. An ensemble model demonstrated the most reliable performance, offering a potential supportive tool for clinical decisions.

Keywords:

artificial intelligence deep learning maxillary premolar panoramic radiography root morphology

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Published on: April 8, 2020

Area of Science:

Dentistry
Radiology
Artificial Intelligence

Background:

Determining the root number of maxillary premolars is crucial for endodontic diagnosis and treatment planning.
Panoramic radiography is a common imaging modality, but root number detection can be challenging.
Deep learning offers potential for automated analysis of radiographic images.

Purpose of the Study:

To evaluate the efficacy of deep learning models in detecting the root number of maxillary premolars using panoramic radiographs.
To compare the performance of different convolutional neural network (CNN) architectures.

Main Methods:

A retrospective study utilized 925 maxillary premolars from 350 patients, with CBCT scans as the reference standard.
Three CNN models (AlexNet, DenseNet-121, EfficientNet-B0) were trained using transfer learning on preprocessed panoramic images.
Data augmentation, five-fold cross-validation, and an independent external validation set were employed to assess model performance and generalizability.

Main Results:

The ensemble deep learning model achieved the highest accuracy (0.90) and AUC (0.94) in cross-validation.
On external validation, the ensemble model also performed best (accuracy 0.87), outperforming an expert clinician (accuracy 0.82).
The ensemble model demonstrated reduced variability and narrower confidence intervals, indicating robust performance.

Conclusions:

Deep learning models, particularly the ensemble approach, show reliable performance in identifying maxillary premolar root numbers from panoramic radiographs.
These AI systems hold promise as supportive tools to aid clinicians in decision-making.
Further research can explore integration into routine dental practice.