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Semin Musculoskelet Radiol 2024; 28(05): 610-619
DOI: 10.1055/s-0044-1789218
Review Article

Artificial Intelligence Applications for Imaging Metabolic Bone Diseases

Amanda Isaac 
1   School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
,
Asli Irmak Akdogan 
2   Ataturk Training and Research Hospital, Izmir Katip Çelebi University, Izmir, Turkey
,
Danoob Dalili
3   Epsom and St. Helier Hospitals, Surrey, London, United Kingdom
,
Nuran Saber
4   Pamukkale University, Denizli, Turkey
,
David Drobny
1   School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
,
Giuseppe Guglielmi
5   Università degli studi di Foggia, Foggia, Italy
,
Marc Modat
1   School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
,
Alberto Bazzocchi
6   Dipartimento di Scienze Mediche e Chirurgiche, Bologna University, Bologna, Italy
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Abstract

Artificial intelligence (AI) has significantly impacted the field of medical imaging, particularly in diagnosing and managing metabolic bone diseases (MBDs) such as osteoporosis and osteopenia, Paget's disease, osteomalacia, and rickets, as well as rare conditions such as osteitis fibrosa cystica and osteogenesis imperfecta. This article provides an in-depth analysis of AI techniques used in imaging these conditions, recent advancements, and their clinical applications. It also explores ethical considerations and future perspectives. Through comprehensive examination and case studies, we highlight the transformative potential of AI in enhancing diagnostic accuracy, improving patient outcomes, and contributing to personalized medicine. By integrating AI with existing imaging techniques, we can significantly enhance the capabilities of medical imaging in diagnosing, monitoring, and treating MBDs. We also provide a comprehensive overview of the current state, challenges, and future prospects of AI applications in this crucial area of health care.

Keywords

artificial intelligence - deep learning - machine learning - natural language processing - computer vision


Publication History

Article published online:
15 October 2024

© 2024. Thieme. All rights reserved.

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  • References

  • 1 Bazzocchi A, Isaac A, Dalili D. et al. Imaging of metabolic bone diseases: the spine view, Part I. Semin Musculoskelet Radiol 2022; 26 (04) 478-490
    Thieme ConnectPubMedSearch in Google Scholar
  • 2 Aparisi Gómez MP, Isaac A, Dalili D. et al. Imaging of metabolic bone diseases: the spine view, Part II. Semin Musculoskelet Radiol 2022; 26 (04) 491-500
    Thieme ConnectPubMedSearch in Google Scholar
  • 3 Wáng YXJ, Diacinti D, Aparisi Gómez MP. et al. Radiological diagnosis of prevalent osteoporotic vertebral fracture on radiographs: an interim consensus from a group of experts of the ESSR osteoporosis and metabolism subcommittee. Skeletal Radiol 2024; April 25 (Epub ahead of print)
    PubMed
  • 4 Tran T, Bliuc D, van Geel T. et al. Population-wide impact of non-hip non-vertebral fractures on mortality. J Bone Miner Res 2017; 32 (09) 1802-1810
    CrossrefPubMedSearch in Google Scholar
  • 5 Hirsch JA, Zini C, Anselmetti GC. et al. Vertebral augmentation: is it time to get past the pain? A consensus statement from the Sardinia Spine and Stroke Congress. Medicina (Kaunas) 2022; 58 (10) 1431
    CrossrefPubMedSearch in Google Scholar
  • 6 Gao L, Jiao T, Feng Q, Wang W. Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis. Osteoporos Int 2021; 32 (07) 1279-1286
    CrossrefPubMedSearch in Google Scholar
  • 7 Hong N, Whittier DE, Glüer CC, Leslie WD. The potential role for artificial intelligence in fracture risk prediction. Lancet Diabetes Endocrinol 2024;S2213-8587(24)00153-0
    PubMed
  • 8 Kong SH, Shin CS. Applications of machine learning in bone and mineral research. Endocrinol Metab (Seoul) 2021; 36 (05) 928-937
    CrossrefPubMedSearch in Google Scholar
  • 9 Weber MA, Bazzocchi A, Nöbauer-Huhmann IM. Tumors of the spine: when can biopsy be avoided?. Semin Musculoskelet Radiol 2022; 26 (04) 453-468
    Thieme ConnectPubMedSearch in Google Scholar
  • 10 Fang K, Zheng X, Lin X, Dai Z. A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques. Front Endocrinol (Lausanne) 2024; 15: 1296047
    CrossrefPubMedSearch in Google Scholar
  • 11 Dalili D, Bazzocchi A, Dalili DE, Guglielmi G, Isaac A. The role of body composition assessment in obesity and eating disorders. Eur J Radiol 2020; 131: 109227
    CrossrefPubMedSearch in Google Scholar
  • 12 Murata K, Endo K, Aihara T. et al. Artificial intelligence for the detection of vertebral fractures on plain spinal radiography. Sci Rep 2020; 10 (01) 20031
    CrossrefPubMedSearch in Google Scholar
  • 13 Genant HK, Wu CY, van Kuijk C, Nevitt MC. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res 1993; 8 (09) 1137-1148
    CrossrefPubMedSearch in Google Scholar
  • 14 Nguyen TV. Individualized fracture risk assessment: state-of-the-art and room for improvement. Osteoporos Sarcopenia 2018; 4 (01) 2-10
    CrossrefPubMedSearch in Google Scholar
  • 15 Leslie WD, Binkley N, McCloskey EV. et al. FRAX adjustment by Trabecular Bone Score with or without bone mineral density: the Manitoba BMD Registry. J Clin Densitom 2023; 26 (03) 101378
    CrossrefPubMedSearch in Google Scholar
  • 16 Dalili D, Isaac A, Garnon J, Cazzato RL, Gangi A. Towards personalized musculoskeletal interventional oncology: enhanced image-guided biopsies and interventions. Semin Roentgenol 2022; 57 (03) 201-211
    CrossrefPubMedSearch in Google Scholar
  • 17 Dalili D, Isaac A, Rashidi A, Åström G, Fritz J. Image-guided sports medicine and musculoskeletal tumor interventions: a patient-centered model. Semin Musculoskelet Radiol 2020; 24 (03) 290-309
    Thieme ConnectPubMedSearch in Google Scholar
  • 18 Gazzotti S, Aparisi Gómez MP, Schileo E. et al. High-resolution peripheral quantitative computed tomography: research or clinical practice?. Br J Radiol 2023; 96 (1150) 20221016
    CrossrefPubMedSearch in Google Scholar
  • 19 Üstün F, Ustabaşıoğlu FE, Tokuç B, Bülbül BY, Çelik M, Aytürk S. Paget's disease of the bone found incidentally on F-18 FDG PET/CT: clinical significance and differential diagnostic criteria. Acta Endocrinol (Bucur) 2023; 19 (03) 292-300
    CrossrefPubMedSearch in Google Scholar
  • 20 Cheung H, Yechoor A, Behnia F. et al. Common skeletal neoplasms and nonneoplastic lesions at 18F-FDG PET/CT. Radiographics 2022; 42 (01) 250-267
    CrossrefPubMedSearch in Google Scholar
  • 21 Lim DSW, Makmur A, Zhu L. et al. Improved productivity using deep learning-assisted reporting for lumbar spine MRI. Radiology 2022; 305 (01) 160-166
    CrossrefPubMedSearch in Google Scholar
  • 22 Shayganfar A, Khodayi M, Ebrahimian S, Tabrizi Z. Quantitative diagnosis of osteoporosis using lumbar spine signal intensity in magnetic resonance imaging. Br J Radiol 2019; 92 (1097) 20180774
    CrossrefPubMedSearch in Google Scholar
  • 23 Bandirali M, Di Leo G, Papini GD. et al. A new diagnostic score to detect osteoporosis in patients undergoing lumbar spine MRI. Eur Radiol 2015; 25 (10) 2951-2959
    CrossrefPubMedSearch in Google Scholar
  • 24 Tabor E, Pluskiewicz W, Tabor K. Clinical conformity between heel ultrasound and densitometry in postmenopausal women: a systematic review. J Ultrasound Med 2018; 37 (02) 363-369
    CrossrefPubMedSearch in Google Scholar
  • 25 de Vries BCS, Hegeman JH, Nijmeijer W, Geerdink J, Seifert C, Groothuis-Oudshoorn CGM. Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis. Osteoporos Int 2021; 32 (03) 437-449
    CrossrefPubMedSearch in Google Scholar
  • 26 Khan O, Badhiwala JH, Grasso G, Fehlings MG. Use of machine learning and artificial intelligence to drive personalized medicine approaches for spine care. World Neurosurg 2020; 140: 512-518
    CrossrefPubMedSearch in Google Scholar
  • 27 Aggarwal R, Sounderajah V, Martin G. et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med 2021; 4 (01) 65
    CrossrefPubMedSearch in Google Scholar
  • 28 Litjens G, Kooi T, Bejnordi BE. et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88
    CrossrefPubMedSearch in Google Scholar
  • 29 Ong JCL, Chang SY, William W. et al. Ethical and regulatory challenges of large language models in medicine. Lancet Digit Health 2024; 6 (06) e428-e432
    CrossrefPubMedSearch in Google Scholar
  • 30 Schonfeld E, de Lotbiniere-Bassett M, Jansen T, Anthony D, Veeravagu A. Vertebrae segmentation in reduced radiation CT imaging for augmented reality applications. Int J CARS 2022; 17 (04) 775-783
    CrossrefPubMedSearch in Google Scholar
  • 31 Greffier J, Frandon J, Durand Q. et al. Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study. Diagn Interv Imaging 2023; 104 (02) 76-83
    CrossrefPubMedSearch in Google Scholar
  • 32 Sekuboyina A, Husseini ME, Bayat A. et al. VerSe: A vertebrae labelling and segmentation benchmark for multi-detector CT images. Med Image Anal 2021; 73: 102166
    CrossrefPubMedSearch in Google Scholar
  • 33 Tsai DJ, Lin C, Lin CS, Lee CC, Wang CH, Fang WH. Artificial intelligence-enabled chest X-ray classifies osteoporosis and identifies mortality risk. J Med Syst 2024; 48 (01) 12
    CrossrefPubMedSearch in Google Scholar
  • 34 Levi R, Garoli F, Battaglia M. et al. CT-based radiomics can identify physiological modifications of bone structure related to subjects' age and sex. Radiol Med (Torino) 2023; 128 (06) 744-754
    CrossrefPubMedSearch in Google Scholar
  • 35 Lambin P, Leijenaar RTH, Deist TM. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14 (12) 749-762
    CrossrefPubMedSearch in Google Scholar
  • 36 Haarburger C, Müller-Franzes G, Weninger L, Kuhl C, Truhn D, Merhof D. Radiomics feature reproducibility under inter-rater variability in segmentations of CT images. Sci Rep 2020; 10 (01) 12688
    CrossrefPubMedSearch in Google Scholar
  • 37 Cobo M, Menéndez Fernández-Miranda P, Bastarrika G, Lloret Iglesias L. Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows. Sci Data 2023; 10 (01) 732
    CrossrefPubMedSearch in Google Scholar
  • 38 Chianca V, Cuocolo R, Gitto S. et al. Radiomic machine learning classifiers in spine bone tumors: a multi-software, multi-scanner study. Eur J Radiol 2021; 137: 109586
    CrossrefPubMedSearch in Google Scholar
  • 39 Xue Z, Huo J, Sun X. et al. Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density. BMC Musculoskelet Disord 2022; 23 (01) 336
    CrossrefPubMedSearch in Google Scholar
  • 40 Anderson PG, Baum GL, Keathley N. et al. Deep learning assistance closes the accuracy gap in fracture detection across clinician types. Clin Orthop Relat Res 2023; 481 (03) 580-588
    CrossrefPubMedSearch in Google Scholar
  • 41 Fu T, Viswanathan V, Attia A. et al. Assessing the potential of a deep learning tool to improve fracture detection by radiologists and emergency physicians on extremity radiographs. Acad Radiol 2024; 31 (05) 1989-1999
    CrossrefPubMedSearch in Google Scholar
  • 42 Dallora AL, Anderberg P, Kvist O, Mendes E, Diaz Ruiz S, Sanmartin Berglund J. Bone age assessment with various machine learning techniques: a systematic literature review and meta-analysis. PLoS One 2019; 14 (07) e0220242
    CrossrefPubMedSearch in Google Scholar
  • 43 Martin DD, Calder AD, Ranke MB, Binder G, Thodberg HH. Accuracy and self-validation of automated bone age determination. Sci Rep 2022; 12 (01) 6388
    CrossrefPubMedSearch in Google Scholar
  • 44 Lee BD, Lee MS. Automated bone age assessment using artificial intelligence: the future of bone age assessment. Korean J Radiol 2021; 22 (05) 792-800
    CrossrefPubMedSearch in Google Scholar
  • 45 Booz C, Yel I, Wichmann JL. et al. Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method. Eur Radiol Exp 2020; 4 (01) 6
    CrossrefPubMedSearch in Google Scholar
  • 46 van Rijn RR, Thodberg HH. Bone age assessment: automated techniques coming of age?. Acta Radiol 2013; 54 (09) 1024-1029
    CrossrefPubMedSearch in Google Scholar
  • 47 Thodberg HH, Sävendahl L. Validation and reference values of automated bone age determination for four ethnicities. Acad Radiol 2010; 17 (11) 1425-1432
    CrossrefPubMedSearch in Google Scholar
  • 48 Thodberg HH. Clinical review: an automated method for determination of bone age. J Clin Endocrinol Metab 2009; 94 (07) 2239-2244
    CrossrefPubMedSearch in Google Scholar
  • 49 Thodberg HH, Kreiborg S, Juul A, Pedersen KD. The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 2009; 28 (01) 52-66
    CrossrefPubMedSearch in Google Scholar
  • 50 Wang F, Gu X, Chen S. et al. Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development. PeerJ 2020; 8: e8854
    CrossrefPubMedSearch in Google Scholar
  • 51 Satoh M. Bone age: assessment methods and clinical applications. Clin Pediatr Endocrinol 2015; 24 (04) 143-152
    CrossrefPubMedSearch in Google Scholar
  • 52 Kim PH, Yoon HM, Kim JR. et al. Bone age assessment using artificial intelligence in Korean pediatric population: a comparison of deep-learning models trained with healthy chronological and Greulich-Pyle ages as labels. Korean J Radiol 2023; 24 (11) 1151-1163
    CrossrefPubMedSearch in Google Scholar
  • 53 Kim JR, Shim WH, Yoon HM. et al. Computerized bone age estimation using deep learning based program: evaluation of the accuracy and efficiency. AJR Am J Roentgenol 2017; 209 (06) 1374-1380
    CrossrefPubMedSearch in Google Scholar
  • 54 Wang F, Cidan W, Gu X. et al. Performance of an artificial intelligence system for bone age assessment in Tibet. Br J Radiol 2021; 94 (1120) 20201119
    CrossrefPubMedSearch in Google Scholar
  • 55 Han K, Song K, Choi BW. How to develop, validate, and compare clinical prediction models involving radiological parameters: study design and statistical methods. Korean J Radiol 2016; 17 (03) 339-350
    CrossrefPubMedSearch in Google Scholar
  • 56 Cortese G. How to use statistical models and methods for clinical prediction. Ann Transl Med 2020; 8 (04) 76
    CrossrefPubMedSearch in Google Scholar
  • 57 Su TL, Jaki T, Hickey GL, Buchan I, Sperrin M. A review of statistical updating methods for clinical prediction models. Stat Methods Med Res 2018; 27 (01) 185-197
    CrossrefPubMedSearch in Google Scholar
  • 58 Riley RD, Collins GS. Stability of clinical prediction models developed using statistical or machine learning methods. Biom J 2023; 65 (08) e2200302
    CrossrefPubMedSearch in Google Scholar
  • 59 Staffa SJ, Zurakowski D. Statistical development and validation of clinical prediction models. Anesthesiology 2021; 135 (03) 396-405
    CrossrefPubMedSearch in Google Scholar
  • 60 Collins GS, Moons KGM, Dhiman P. et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024; 385: e078378
    CrossrefPubMedSearch in Google Scholar
  • 61 Zabihiyeganeh M, Mirzaei A, Tabrizian P. et al. Prediction of subsequent fragility fractures: application of machine learning. BMC Musculoskelet Disord 2024; 25 (01) 438
    CrossrefPubMedSearch in Google Scholar
  • 62 Gui C, Chen X, Sheikh K. et al. Radiomic modeling to predict risk of vertebral compression fracture after stereotactic body radiation therapy for spinal metastases. J Neurosurg Spine 2021; 36 (02) 294-302
    CrossrefPubMedSearch in Google Scholar
  • 63 Hung TNK, Le NQK, Le NH. et al. An AI-based prediction model for drug-drug interactions in osteoporosis and Paget's diseases from SMILES. Mol Inform 2022; 41 (06) e2100264
    CrossrefPubMedSearch in Google Scholar
  • 64 Sosa BR, Cung M, Suhardi VJ. et al. Capacity for large language model chatbots to aid in orthopedic management, research, and patient queries. J Orthop Res 2024; 42 (06) 1276-1282
    CrossrefPubMedSearch in Google Scholar
  • 65 Ahmed W, Saturno M, Rajjoub R. et al. ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis. Eur Spine J 2024; March 15 (Epub ahead of print)
    PubMed
  • 66 Chen Y, Esmaeilzadeh P. Generative AI in medical practice: in-depth exploration of privacy and security challenges. J Med Internet Res 2024; 26: e53008
    CrossrefPubMedSearch in Google Scholar
  • 67 Boyanov MA. Whole body and regional bone mineral content and density in women aged 20–75 years. Acta Endocrinol (Bucur) 2016; 12 (02) 191-196
    CrossrefPubMedSearch in Google Scholar
  • 68 Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks?. ArXiv 2014 ; abstract available at: https://arxiv.org/abs/1411.1792 . Accessed August 5, 2024
    PubMedSearch in Google Scholar
  • 69 Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng 2010; 22: 1345-1359
    CrossrefPubMedSearch in Google Scholar
  • 70 Compte R, Granville Smith I, Isaac A. et al. Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis. Eur Spine J 2023; 32 (11) 3764-3787
    CrossrefPubMedSearch in Google Scholar
  • 71 Vasiljeva K, van Duren BH, Pandit H. Changing device regulations in the European Union: impact on research, innovation and clinical practice. Indian J Orthop 2020; 54 (02) 123-129
    CrossrefPubMedSearch in Google Scholar
  • 72 Niemiec E. Will the EU medical device regulation help to improve the safety and performance of medical AI devices?. Digit Health 2022 ;8:20552076221089079
    PubMedSearch in Google Scholar
  • 73 Fraser AG, Biasin E, Bijnens B. et al. Artificial intelligence in medical device software and high-risk medical devices—a review of definitions, expert recommendations and regulatory initiatives. Expert Rev Med Devices 2023; 20 (06) 467-491
    CrossrefPubMedSearch in Google Scholar
  • 74 Thomas L, Hyde C, Mullarkey D, Greenhalgh J, Kalsi D, Ko J. Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance. Front Med (Lausanne) 2023; 10: 1264846
    CrossrefPubMedSearch in Google Scholar
  • 75 Zinchenko VV, Arzamasov KM, Chetverikov SF. et al. Methodology for conducting post-marketing surveillance of software as a medical device based on artificial intelligence technologies. Sovrem Tekhnologii Med 2022; 14 (05) 15-23
    CrossrefPubMedSearch in Google Scholar
  • 76 Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol 2019; 20 (03) 405-410
    CrossrefPubMedSearch in Google Scholar
  • 77 Steffens D, Hancock MJ, Pereira LS, Kent PM, Latimer J, Maher CG. Do MRI findings identify patients with low back pain or sciatica who respond better to particular interventions? A systematic review. Eur Spine J 2016; 25 (04) 1170-1187
    CrossrefPubMedSearch in Google Scholar
  • 78 Liu X, Faes L, Kale AU. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health 2019; 1 (06) e271-e297
    CrossrefPubMedSearch in Google Scholar
  • 79 Alukaev D, Kiselev S, Mustafaev T, Ainur A, Ibragimov B, Vrtovec T. A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation. Eur Spine J 2022; 31 (08) 2115-2124
    CrossrefPubMedSearch in Google Scholar
  • 80 Cabrera A, Bouterse A, Nelson M. et al. Accounting for age in prediction of discharge destination following elective lumbar fusion: a supervised machine learning approach. Spine J 2023; 23 (07) 997-1006
    CrossrefPubMedSearch in Google Scholar
  • 81 Walston SL, Seki H, Takita H. et al. Data set terminology of deep learning in medicine: a historical review and recommendation. . Jpn J Radiol 2024
    PubMed