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Fully Automated Detection of Osteoporosis Stage on Panoramic Radiographs Using YOLOv5 Deep Learning Model and Designing a Graphical User Interface

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Abstract

Purpose

Osteoporosis is a systemic disease that causes fracture risk and bone fragility due to decreased bone mineral density and deterioration of bone microarchitecture. Deep learning-based image analysis technologies have effectively been used as a decision support system in diagnosing disease. This study proposes a deep learning-based approach that automatically performs osteoporosis localization and stage estimation on panoramic radiographs with different contrasts.

Methods

Eight hundred forty-six panoramic radiographs were collected from the hospital database and pre-processed. Two radiologists annotated the images according to the Mandibular Cortical Index, considering the cortical region extending from the distal to the antegonial area of the foramen mentale. The data were trained and validated using the YOLOv5 deep learning algorithm in the Linux-based COLAB Pro cloud environment. The Weights and Bias platform was integrated into COLAB, and the training process was monitored instantly. Using the model weights obtained, the test data that the system had not seen before were analyzed. Using the non-maximum suppression technique on the test data, the bounding boxes of the regions that could be osteoporosis were automatically drawn. Finally, a graphical user interface was developed with the PyQT5 library.

Results

Two radiologists analyzed the data, and the performance criteria were calculated. The performance criteria of the test data were obtained as follows: an average precision of 0.994, a recall of 0.993, an F1-score of 0.993, and an inference time of 14.3 ms (0.0143 s).

Conclusion

The proposed method showed that deep learning could successfully perform automatic localization and staging of osteoporosis on panoramic radiographs without region-of-interest cropping and complex pre-processing methods.

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Data Availability

The data sets can be shared with researchers who wish to conduct studies upon reasonable request.

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Acknowledgements

This study was presented as an abstract at the 26th Turkish Dental Association International Dentistry Congress (TDB) in Istanbul, Turkey, from September 8 to 11, 2022.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Technology, Pamukkale University, Çamlaraltı District, University Street, Kinikli Campus, Pamukkale, 20160, Denizli, Turkey

    Muhammet Üsame ÖZİÇ

  2. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey

    Melek Tassoker

  3. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Okan University, Istanbul, Turkey

    Fatma Yuce

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  1. Muhammet Üsame ÖZİÇ
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  3. Fatma Yuce
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Contributions

All authors contributed to the study conception and design. MT and FY contributed to data collection, labeling and evaluation of results. MÜÖ contributed to algorithm design, coding, and determination of performance criteria. All authors contribute equally to article manuscript writing, literature review, and article design.

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Correspondence to Muhammet Üsame ÖZİÇ.

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Ethical Approval

This study was conducted at the Faculty of Dentistry, Necmettin Erbakan University, Department of Dentomaxillofacial Radiology, with the approval of the Ethics Committee (No. 2022/149) and was performed according to the stipulations laid out by the Declaration of Helsinki.

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ÖZİÇ, M.Ü., Tassoker, M. & Yuce, F. Fully Automated Detection of Osteoporosis Stage on Panoramic Radiographs Using YOLOv5 Deep Learning Model and Designing a Graphical User Interface. J. Med. Biol. Eng. 43, 715–731 (2023). https://doi.org/10.1007/s40846-023-00831-x

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