Artificial Intelligence for Root Canal Orifice Identification Using Dental Operating Microscope Images: A Preliminary Evaluation

To evaluate the diagnostic performance of artificial intelligence (AI) in detecting root canal orifices using images captured with a dental operating microscope (DOM). A total of 80 human maxillary first and second molars were included in the study. After preparing traditional access cavities, root canal orifices were identified under a dental operating microscope (DOM) at 21.25× magnification. Following orifice identification, video recordings were obtained using the DOM, from which a total of 1527 frames were randomly selected for analysis. The root canal orifices in these frames were manually labelled using CranioCatch labeling software (CranioCatch, Eskişehir, Turkey). In the binary classification task, the system correctly identified 502 out of 526 root canal orifices, yielding an accuracy of 91%. The YOLO-based CNN demonstrated high accuracy and sensitivity in detecting root canal orifices from DOM images.

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Publication Details

Journal
Australian Endodontic Journal
Published
2025-05-30
DOI
https://doi.org/10.1111/aej.12955
Citations
3
Primary Topic
Dental Radiography and Imaging
Type
article
Field-Weighted Citation Impact
5.37
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article

Artificial Intelligence for Root Canal Orifice Identification Using Dental Operating Microscope Images: A Preliminary Evaluation

Özer Çelik, Ertuğrul Karataş, İbrahim Şevki Bayrakdar, Olçun Ümit Ünal
3 citations
Australian Endodontic Journal
Dental Radiography and Imaging
5.37
article

Artificial Intelligence for Root Canal Orifice Identification Using Dental Operating Microscope Images: A Preliminary Evaluation

Özer Çelik, Ertuğrul Karataş, İbrahim Şevki Bayrakdar, Olçun Ümit Ünal
article en
3 citations

Abstract

To evaluate the diagnostic performance of artificial intelligence (AI) in detecting root canal orifices using images captured with a dental operating microscope (DOM). A total of 80 human maxillary first and second molars were included in the study. After preparing traditional access cavities, root canal orifices were identified under a dental operating microscope (DOM) at 21.25× magnification. Following orifice identification, video recordings were obtained using the DOM, from which a total of 1527 frames were randomly selected for analysis. The root canal orifices in these frames were manually labelled using CranioCatch labeling software (CranioCatch, Eskişehir, Turkey). In the binary classification task, the system correctly identified 502 out of 526 root canal orifices, yielding an accuracy of 91%. The YOLO-based CNN demonstrated high accuracy and sensitivity in detecting root canal orifices from DOM images.

Australian Endodontic JournalVol. 51(2)
Eskişehir Osmangazi University (TR), Atatürk University (TR)
Openalex Percentile: Top 5%
Dental Radiography and Imaging
5.37
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