Deep learning model using YOLO-v5 for detecting and numbering of teeth in dental bitewing images

Cansu Görürgöz; Akhilanand Chaurasia; Mohmed Isaqali Karobari; Ibrahim Sevki Bayrakdar; Özer Çelik; Kaan Orhan

doi:10.5114/jos.2025.148467

eISSN: 2299-551X
ISSN: 0011-4553

Journal of Stomatology

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1/2025
vol. 78

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abstract:

Original paper

Deep learning model using YOLO-v5 for detecting and numbering of teeth in dental bitewing images

Cansu Görürgöz

¹

,

Akhilanand Chaurasia

²

,

Mohmed Isaqali Karobari

³

,

Ibrahim Sevki Bayrakdar

^{4, 5}

,

Özer Çelik

^{5, 6}

,

Kaan Orhan

^{7, 8}

Department of Dentomaxillofacial Radiology, Bolu İzzet Baysal Oral Health Hospital, Turkey
Department of Oral Medicine and Radiology, King George’s Medical University, India
Dental Research Unit, Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, India
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Turkey
Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Turkey
Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Turkey
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Turkey
Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Hungary

J Stoma 2025; 78, 1: 42-51

DOI: https://doi.org/10.5114/jos.2025.148467

Online publish date: 2025/03/19

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Introduction:
Tooth detection and numbering is a fundamental issue that has been the subject of research projects applying artificial intelligence in dental radiography evaluation.

Objectives:
The objectives of the current study were to build a deep learning model for detecting and numbering teeth, and to evaluate the model’s performance.

Material and methods:
Retrospective data collection was done from 3,491 bitewing radiographs of randomly selected individuals. Confusion matrix was used to calculate sensitivity, precision, and true positive and false positive/negative values to examine the performance of the algorithm.

Results:
The sensitivity and precision were 0.9940 and 1 for the classifying task, respectively. In addition, the predicted F1 score was 0.99970, demonstrating a favorable balance between recall and precision. On the right side, the IDF1 score was 89%, with a confidence level of 0.73. The mAP for all classes was high, accurately modeling 90.9% detections with 0.5 threshold. On the left side, the IDF1 score was 89%, with a confidence level of 0.37. The mAP for all classes was high, accurately modeling 91% detections with 0.5 threshold.

Conclusions:
The most disadvantageous feature of the bitewing radiograph is that sometimes various areas of the tooth cannot be completely observed. This situation leads to a reduction in the detection ability of the model. However, this research shows that convolutional neural networks algorithms can be very accurate and effective in detecting and numbering of the teeth.

keywords:

Deep learning model using YOLO-v5 for detecting and numbering of teeth in dental bitewing images

Cansu Görürgöz 1 , Akhilanand Chaurasia 2 , Mohmed Isaqali Karobari 3 , Ibrahim Sevki Bayrakdar 4, 5 , Özer Çelik 5, 6 , Kaan Orhan 7, 8

object detection, deep learning, YOLO-v5, bitewing radiograph, teeth numbering

Cansu Görürgöz

¹

,

Akhilanand Chaurasia

²

,

Mohmed Isaqali Karobari

³

,

Ibrahim Sevki Bayrakdar

^{4, 5}

,

Özer Çelik

^{5, 6}

,

Kaan Orhan

^{7, 8}