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Diagnosing osteoporosis using deep neural networkassisted optical image processing method

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Abstract

Osteoporosis is a disease in which bone mass and structural strength decrease, leading to increased fragility and susceptibility to fractures in the face, neck, spine, wrist, etc. It's a disease that doesn't display any symptoms until a break occurs; in other words, it's a silent illness. Sometime it also utilised frequently in dentistry application such that to detect osteoporosis during the operation or implanting on maxilla. This body of work shows that, despite major breakthroughs in medicine, there remains a unique role for the development of novel tools for the diagnosis of osteoporosis. In this work, we present an osteoporosis detection system developed using image processing and support vector machine (SVM) techniques. Compared to prior studies in this area, the data collected by the technology established in this study—which includes 50 sample photographs of the tibia—is of high quality. The proposed method achieves an impressively high level of accuracy (83.6% with 50 samples) because to the inclusion of the histogram feature and the use of tissue properties during feature extraction.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

The authors would like to thank the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia, for their support in the publication of this research.

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Author information

Author notes

  1. Mahmud Uz Zaman, Mohammad Khursheed Alam: Joint first authors.

Authors and Affiliations

  1. Department of Oral and Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Prince Sattam Bin Abdullaziz University, 11942, Al-Kharj, Saudi Arabia

    Mahmud Uz Zaman & Nasser Raqe Alqhtani

  2. Orthodontic Division, Preventive Dentistry Department, Orthodontic Division, College of Dentistry, Jouf University, 72345, Sakaka, Saudi Arabia

    Mohammad Khursheed Alam

  3. Department of Dental Research Cell, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, India

    Mohammad Khursheed Alam

  4. Department of Public Health, Faculty of Allied Health Sciences, Daffodil Lnternational University, Dhaka, Bangladesh

    Mohammad Khursheed Alam

  5. Conservative Dental Sciences Department, College of Dentistry, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia

    Ali Robaian

  6. Department of Preventive Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia

    Abdullah Saad Alqahtani

  7. Department of Surgery, Faculty of Medicine, University of Tabuk, Tabuk, Saudi Arabia

    Mana Alqahtani

  8. Department of Prosthetic Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia

    Khaled M. Alzahrani & Fawaz Alqahtani

Authors
  1. Mahmud Uz Zaman
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  2. Mohammad Khursheed Alam
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  3. Nasser Raqe Alqhtani
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  4. Ali Robaian
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  5. Abdullah Saad Alqahtani
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  7. Khaled M. Alzahrani
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  8. Fawaz Alqahtani
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Contributions

Authors’ contribution Conception and Design: MUZ, MKA, NRA, AR, ASA, MA, KMA Data and Investigation: MKA, MUZ, NRA, AR Manuscript Initial draft: MUZ, MKA, NRA, AR, ASA, MA, KMA Manuscript final draft: MUZ, MKA, NRA, AR, ASA, MA, KMA Approval: MUZ, MKA, NRA, AR, ASA, MA, KMA

Corresponding authors

Correspondence to Mahmud Uz Zaman or Mohammad Khursheed Alam.

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Zaman, M.U., Alam, M.K., Alqhtani, N.R. et al. Diagnosing osteoporosis using deep neural networkassisted optical image processing method. Opt Quant Electron 56, 441 (2024). https://doi.org/10.1007/s11082-023-06031-w

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