Deep Learning for Osteoporosis Diagnosis Using Magnetic Resonance Images of Lumbar Vertebrae

U. D. o. Health and H. Services, "Bone health and Osteoporosis: a report of the Surgeon General," Rockville, MD: US Department of Health and Human Services, Office of the Surgeon General, vol. 87, 2004.

B. Larijani, M. R. M. Tehrani, Z. Hamidi, A. Soltani, and M. Pajouhi, "Osteoporosis, prevention, diagnosis and treatment," Journal of Reproduction & Infertility, vol. 6, no. 1, 2005.

K. V. Lakshmi and M. Padmavathamma, "Modeling an expert system for diagnosis of gestational diabetes mellitus based on risk factors," J Computer Eng (IOSRJCE), vol. 8, pp. 29-32, 2013.

Google Scholar 

O. L. Svendsen, C. Hassager, V. Skødt, and C. Christiansen, "Impact of soft tissue on in vivo accuracy of bone mineral measurements in the spine, hip, and forearm: a human cadaver study," Journal of bone and mineral research, vol. 10, no. 6, pp. 868-873, 1995.

CAS  PubMed  Google Scholar 

E.-M. Lochmüller, N. Krefting, D. Bürklein, F. Eckstein, E. Lochmüller, and D. Bürklein, "Effect of fixation, soft-tissues, and scan projection on bone mineral measurements with dual energy X-ray absorptiometry (DXA)," Calcified tissue international, vol. 68, no. 3, 2001.

D. Mueller and A. Gandjour, "Cost‐effectiveness of using clinical risk factors with and without DXA for Osteoporosis screening in postmenopausal women," Value in Health, vol. 12, no. 8, pp. 1106-1117, 2009.

PubMed  Google Scholar 

M. Sim, M. Stone, A. Johansen, and W. Evans, "Cost effectiveness analysis of BMD referral for DXA using ultrasound as a selective pre-screen in a group of women with low trauma Colles’ fractures," Technology and Health Care, vol. 8, no. 5, pp. 277-284, 2000.

CAS  PubMed  Google Scholar 

V. Sapthagirivasan and M. Anburajan, "Diagnosis of Osteoporosis by extraction of trabecular features from hip radiographs using support vector machine: an investigation panorama with DXA," Computers in biology and medicine, vol. 43, no. 11, pp. 1910-1919, 2013.

CAS  PubMed  Google Scholar 

I. M. Wani and S. Arora, "Computer-aided diagnosis systems for Osteoporosis detection: A comprehensive survey," Medical & biological engineering & computing, vol. 58, no. 9, pp. 1873-1917, 2020.

Google Scholar 

CL. Benhamou, S. Poupon, E. Lespessailles,S. Loiseau and R. Jennane, “Fractal analysis of radiographic trabecular bone texture and bone mineral density: two complementary parameters related to osteoporotic fractures,” J Bone Miner Res, vol. 16, pp. 697–704, 2001.

CAS  PubMed  Google Scholar 

Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, "Deep learning for visual understanding: A review," Neurocomputing, vol. 187, pp. 27-48, 2016.

Google Scholar 

D. Liu and Y. Wang, "Monza: Image classification of vehicle make and model using convolutional neural networks and transfer learning," ed: Stanford Univ., Stanford, CA, USA, 2017.

S. Rastegar et al., "Radiomics for classification of bone mineral loss: A machine learning study," Diagnostic and interventional imaging, vol. 101, no. 9, pp. 599-610, 2020.

CAS  PubMed  Google Scholar 

M. A. Badgeley et al., "Deep learning predicts hip fracture using confounding patient and healthcare variables," NPJ digital medicine, vol. 2, no. 1, pp. 1-10, 2019.

Google Scholar 

P. J. Lisboa, E. C. Ifeachor, and P. S. Szczepaniak, Artificial neural networks in biomedicine. Springer Science & Business Media, 2000.

Q. Du, K. Nie, and Z. Wang, "Application of entropy-based attribute reduction and an artificial neural network in medicine: a case study of estimating medical care costs associated with myocardial infarction," Entropy, vol. 16, no. 9, pp. 4788-4800, 2014.

Google Scholar 

Q. K. Al-Shayea, "Artificial neural networks in medical diagnosis," International Journal of Computer Science Issues, vol. 8, no. 2, pp. 150-154, 2011.

Google Scholar 

F. Amato, A. López, E. M. Peña-Méndez, P. Vaňhara, A. Hampl, and J. Havel, "Artificial neural networks in medical diagnosis," vol. 11, ed: Elsevier, 2013, pp. 47–58.

G. González, G. R. Washko, and R. S. J. Estépar, "Deep learning for biomarker regression: application to Osteoporosis and emphysema on chest CT scans," in Medical Imaging 2018: Image Processing, 2018, vol. 10574: SPIE, pp. 372–378.

Nasab, S. T. M., & Abualigah, L. (2024). Improve Harris Hawkes optimizer algorithm via Laplace crossover. Journal of Ambient Intelligence and Humanized Computing, 15(4), 2057-2072.

Google Scholar 

Moshayedi, A. J., Nasab, S. T. M., Khan, Z. H., & Khan, A. S. (2024). Meta-heuristic Algorithms as an Optimizer: Prospects and Challenges (Part II). Engineering Applications of AI and Swarm Intelligence, 155–180.

Moshayedi, A. J., Nasab, S. T. M., Khan, Z. H., & Khan, A. S. (2024). Meta-heuristic Algorithms as an Optimizer: Prospects and Challenges (Part I). Engineering Applications of AI and Swarm Intelligence, 131–154.

Sivasakthi, B., Preetha, K., & Selvanayagi, D. (2025). Osteoporosis Disease Detection using Optimized Elman Recurrent Neural Network based on Hybrid Bacterial Colony Optimization and Tabu Search Algorithm. International Research Journal of Multidisciplinary Technovation, 7(1), 1-16.

Google Scholar 

Devikanniga, D., & Raj, R. J. S. (2021). Optimization of Extreme Learning Machine Using the Intelligence of Monarch Butterflies for Osteoporosis Diagnosis. In Intelligence in Big Data Technologies—Beyond the Hype: Proceedings of ICBDCC 2019 (pp. 607–615). Springer Singapore.

Devikanniga, D., & Joshua Samuel Raj, R. (2018). Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm. Healthcare technology letters, 5(2), 70-75.

J. H. Lee, G. Y. Kim, Y. N. Hwang, S. Y. Park, and S. M. Kim, "Classification of Osteoporosis by extracting the microarchitectural properties of trabecular bone from DXA scans based on thresholding technique," Journal of Medical Imaging and Health Informatics, vol. 5, no. 8, pp. 1782-1789, 2015.

Google Scholar 

U. Ferizi, S. Honig, and G. Chang, "Artificial intelligence, Osteoporosis and fragility fractures," Current opinion in rheumatology, vol. 31, no. 4, p. 368, 2019.

PubMed  PubMed Central  Google Scholar 

J. J. Hwang et al., "Strut analysis for Osteoporosis detection model using dental panoramic radiography," Dentomaxillofacial Radiology, vol. 46, no. 7, p. 20170006, 2017.

PubMed  PubMed Central  Google Scholar 

M. Singh, A. R. Nagrath, and P. Maini, "Changes in trabecular pattern of the upper end of the femur as an index of Osteoporosis," JBJS, vol. 52, no. 3, pp. 457-467, 1970.

CAS  Google Scholar 

B. N. Nguyen, H. Hoshino, D. Togawa, and Y. Matsuyama, "Cortical thickness index of the proximal femur: A radiographic parameter for preliminary assessment of bone mineral density and Osteoporosis status in the age 50 years and over population," Clinics in Orthopedic Surgery, vol. 10, no. 2, pp. 149-156, 2018.

PubMed  PubMed Central  Google Scholar 

A. P. Sah, T. S. Thornhill, M. S. LeBoff, and J. Glowacki, "Correlation of plain radiographic indices of the hip with quantitative bone mineral density," Osteoporosis international, vol. 18, no. 8, pp. 1119-1126, 2007.

CAS  PubMed  PubMed Central  Google Scholar 

Küçükçiloğlu, Y., Şekeroğlu, B., Adalı, T., & Şentürk, N. (2024). Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models. Diagnostic and Interventional Radiology, 30(1), 9.

PubMed  PubMed Central  Google Scholar 

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