Endocrinol Metab.  2021 Oct;36(5):928-937. 10.3803/EnM.2021.1111.

Applications of Machine Learning in Bone and Mineral Research

• Kong SH
• Shin CS

• KMID: 2521941
• DOI: http://doi.org/10.3803/EnM.2021.1111

Abstract

In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualized approach and a better understanding of the disease in the field of osteoporosis research, similar to that in other research fields. This review aimed to provide an overview of the latest studies using machine learning to address issues, mainly focusing on osteoporosis and fractures. Machine learning models for diagnosing and classifying osteoporosis and detecting fractures from images have shown promising performance. Fracture risk prediction is another promising field of research, and studies are being conducted using various data sources. However, these approaches may be biased due to the nature of the techniques or the quality of the data. Therefore, more studies based on the proposed guidelines are needed to improve the technical feasibility and generalizability of artificial intelligence algorithms.

Keyword

Osteoporosis; Data science; Medical informatics

Figure

• 

  Fig. 1 The trend in the number and categories of machine learning-related publications per year in the field of bone and mineral research. The included publications were from PubMed until the search date (May 30th, 2021). Search strategies were (“Osteoporo-sis”[Mesh] OR “Osteoporotic Fractures”[Mesh] OR “Hip Frac-tures”[Mesh] OR “Spinal Fractures”[Mesh] OR “Humeral Frac-tures”[Mesh] OR “Bone Density”[Mesh] OR Osteoporos*[tiab] OR “fragility fractur*”[tiab] OR (Fractur*[tiab] AND (spin*[tiab] OR vertebra*[tiab] OR hip[tiab] OR humer*[tiab])) OR “bone mineral densit*”[tiab]) AND (“Artificial Intelligence”[Mesh:noexp] OR “machine learning”[Mesh] OR “Neural Networks, Computer” [Mesh] OR “artificial Intelligence”[tiab] OR “machine learning” [tiab] OR “deep learning”[tiab] OR “neural network*”[tiab]) AND English[la]).

• 

  Fig. 2 Cross table of the relationship between the results of the algorithm and reference standard. AI, artificial intelligence; TP, true positive; FP, false positive; FN, false negative; TN, true negative.

Reference

1. National Osteoporosis Foundation. National Bone Health Policy. New report on burden of osteoporosis highlights huge and growing economic and human toll of the disease [Internet]. Arlington: National Osteoporosis Foundation;2019. [cited 2021 Sep 23]. Available from: https://www.nof.org/news/new-report-on-burden-of-osteoporosis-highlights-huge-and-growing-economic-and-human-toll-of-the-disease .

2. Kim HY, Ha YC, Kim TY, Cho H, Lee YK, Baek JY, et al. Healthcare costs of osteoporotic fracture in Korea: information from the National Health Insurance Claims Database, 2008–2011. J Bone Metab. 2017; 24:125–33.

3. Kanis JA, Harvey NC, Johansson H, Oden A, Leslie WD, McCloskey EV. FRAX update. J Clin Densitom. 2017; 20:360–7.

4. Aspray TJ. New horizons in fracture risk assessment. Age Ageing. 2013; 42:548–54.

5. Hong N, Park H, Rhee Y. Machine learning applications in endocrinology and metabolism research: an overview. Endocrinol Metab (Seoul). 2020; 35:71–84.

6. Weber GM, Mandl KD, Kohane IS. Finding the missing link for big biomedical data. JAMA. 2014; 311:2479–80.

7. Cook S. Programming: a developer’s guide to parallel computing with GPUs (applications of GPU computing). Waltham: Morgan Kaufmann Publishers;2012.

8. Dimitriadis VK, Gavriilidis GI, Natsiavas P. Pharmacovigilance and clinical environment: utilizing OMOP-CDM and OHDSI software stack to integrate EHR data. Stud Health Technol Inform. 2021; 281:555–9.

9. Fang Y, Li W, Chen X, Chen K, Kang H, Yu P, et al. Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks. Eur Radiol. 2021; 31:1831–42.

10. Gonzalez G, Washko GR, Estepar RS. Deep learning for biomarker regression: application to osteoporosis and emphysema on chest CT scans. Proc SPIE Int Soc Opt Eng. 2018; 10574:105741H.

11. Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network. Eur Radiol. 2020; 30:3549–57.

12. Nam KH, Seo I, Kim DH, Lee JI, Choi BK, Han IH. Machine learning model to predict osteoporotic spine with Hounsfield units on lumbar computed tomography. J Korean Neurosurg Soc. 2019; 62:442–9.

13. Krishnaraj A, Barrett S, Bregman-Amitai O, Cohen-Sfady M, Bar A, Chettrit D, et al. Simulating dual-energy X-ray absorptiometry in CT using deep-learning segmentation cascade. J Am Coll Radiol. 2019; 16:1473–9.

14. Areeckal AS, Jayasheelan N, Kamath J, Zawadynski S, Kocher M, David SS. Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population. Osteoporos Int. 2018; 29:665–73.

15. Tecle N, Teitel J, Morris MR, Sani N, Mitten D, Hammert WC. Convolutional neural network for second metacarpal radiographic osteoporosis screening. J Hand Surg Am. 2020; 45:175–81.

16. Yamamoto N, Sukegawa S, Kitamura A, Goto R, Noda T, Nakano K, et al. Deep learning for osteoporosis classification using hip radiographs and patient clinical covariates. Biomolecules. 2020; 10:1534.

17. Zhang B, Yu K, Ning Z, Wang K, Dong Y, Liu X, et al. Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: a multicenter retrospective cohort study. Bone. 2020; 140:115561.

18. Liu J, Wang J, Ruan W, Lin C, Chen D. Diagnostic and gradation model of osteoporosis based on improved deep U-Net network. J Med Syst. 2019; 44:15.

19. Zhang T, Liu P, Zhang Y, Wang W, Lu Y, Xi M, et al. Combining information from multiple bone turnover markers as diagnostic indices for osteoporosis using support vector machines. Biomarkers. 2019; 24:120–6.

20. Wang J, Yan D, Zhao A, Hou X, Zheng X, Chen P, et al. Discovery of potential biomarkers for osteoporosis using LC-MS/MS metabolomic methods. Osteoporos Int. 2019; 30:1491–9.

21. Meng J, Sun N, Chen Y, Li Z, Cui X, Fan J, et al. Artificial neural network optimizes self-examination of osteoporosis risk in women. J Int Med Res. 2019; 47:3088–98.

22. Shim JG, Kim DW, Ryu KH, Cho EA, Ahn JH, Kim JI, et al. Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women. Arch Osteoporos. 2020; 15:169.

23. Sun X, Qiao Y, Li W, Sui Y, Ruan Y, Xiao J. A graphene oxide-aided triple helical aggregation-induced emission biosensor for highly specific detection of charged collagen peptides. J Mater Chem B. 2020; 8:6027–33.

24. Zheng K, Harris CE, Jennane R, Makrogiannis S. Integrative blockwise sparse analysis for tissue characterization and classification. Artif Intell Med. 2020; 107:101885.

25. Lee JS, Adhikari S, Liu L, Jeong HG, Kim H, Yoon SJ. Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofac Radiol. 2019; 48:20170344.

26. Singh A, Dutta MK, Jennane R, Lespessailles E. Classification of the trabecular bone structure of osteoporotic patients using machine vision. Comput Biol Med. 2017; 91:148–58.

27. Dwivedi R, Singh C, Yu B, Wainwright MJ. Revisiting complexity and the bias-variance tradeoff. arXiv. 2020. Jun. 17. https://arxiv.org/abs/2006.10189 .

28. Sun C, Shrivastava A, Singh S, Gupta A. Revisiting unreasonable effectiveness of data in deep learning era. In : Proceedings of the 2017 IEEE International Conference on Computer Vision; 2017 Oct 22–29; Venice, Italy. Los Alamitos: IEEE Computer Society;2017. p. 843–52.

29. Tomita N, Cheung YY, Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med. 2018; 98:8–15.

30. Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A. 2018; 115:11591–6.

31. Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018; 73:439–45.

32. Chung SW, Han SS, Lee JW, Oh KS, Kim NR, Yoon JP, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop. 2018; 89:468–73.

33. Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017; 88:581–6.

34. Brett A, Miller CG, Hayes CW, Krasnow J, Ozanian T, Abrams K, et al. Development of a clinical workflow tool to enhance the detection of vertebral fractures: accuracy and precision evaluation. Spine (Phila Pa 1976). 2009; 34:2437–43.

35. Adams M, Chen W, Holcdorf D, McCusker MW, Howe PD, Gaillard F. Computer vs human: deep learning versus perceptual training for the detection of neck of femur fractures. J Med Imaging Radiat Oncol. 2019; 63:27–32.

36. Kitamura G. Deep learning evaluation of pelvic radiographs for position, hardware presence, and fracture detection. Eur J Radiol. 2020; 130:109139.

37. Mutasa S, Varada S, Goel A, Wong TT, Rasiej MJ. Advanced deep learning techniques applied to automated femoral neck fracture detection and classification. J Digit Imaging. 2020; 33:1209–17.

38. Urakawa T, Tanaka Y, Goto S, Matsuzawa H, Watanabe K, Endo N. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiol. 2019; 48:239–44.

39. Mawatari T, Hayashida Y, Katsuragawa S, Yoshimatsu Y, Hamamura T, Anai K, et al. The effect of deep convolutional neural networks on radiologists’ performance in the detection of hip fractures on digital pelvic radiographs. Eur J Radiol. 2020; 130:109188.

40. Jimenez-Sanchez A, Kazi A, Albarqouni S, Kirchhoff C, Biberthaler P, Navab N, et al. Precise proximal femur fracture classification for interactive training and surgical planning. Int J Comput Assist Radiol Surg. 2020; 15:847–57.

41. Yamada Y, Maki S, Kishida S, Nagai H, Arima J, Yamakawa N, et al. Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs. Acta Orthop. 2020; 91:699–704.

42. Yu JS, Yu SM, Erdal BS, Demirer M, Gupta V, Bigelow M, et al. Detection and localisation of hip fractures on anteroposterior radiographs with artificial intelligence: proof of concept. Clin Radiol. 2020; 75:237.

43. Murata K, Endo K, Aihara T, Suzuki H, Sawaji Y, Matsuoka Y, et al. Artificial intelligence for the detection of vertebral fractures on plain spinal radiography. Sci Rep. 2020; 10:20031.

44. FDA cleared AI algorithms [Internet]. Reston: Data Science Institute American College of Radiology;2021. [cited 2021 Sep 23]. Available from: https://models.acrdsi.org .

45. Badgeley MA, Zech JR, Oakden-Rayner L, Glicksberg BS, Liu M, Gale W, et al. Deep learning predicts hip fracture using confounding patient and healthcare variables. NPJ Digit Med. 2019; 2:31.

46. Valentinitsch A, Trebeschi S, Kaesmacher J, Lorenz C, Loffler MT, Zimmer C, et al. Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporos Int. 2019; 30:1275–85.

47. Burns JE, Yao J, Summers RM. Vertebral body compression fractures and bone density: automated detection and classification on CT images. Radiology. 2017; 284:788–97.

48. Pranata YD, Wang KC, Wang JC, Idram I, Lai JY, Liu JW, et al. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput Methods Programs Biomed. 2019; 171:27–37.

49. Carballido-Gamio J, Yu A, Wang L, Su Y, Burghardt AJ, Lang TF, et al. Hip fracture discrimination based on statistical multi-parametric modeling (SMPM). Ann Biomed Eng. 2019; 47:2199–212.

50. Gebre RK, Hirvasniemi J, Lantto I, Saarakkala S, Leppilahti J, Jamsa T. Discrimination of low-energy acetabular fractures from controls using computed tomography-based bone characteristics. Ann Biomed Eng. 2021; 49:367–81.

51. Chen YF, Lin CS, Wang KA, Rahman OA, Lee DJ, Chung WS, et al. Design of a clinical decision support system for fracture prediction using imbalanced dataset. J Healthc Eng. 2018; 2018:9621640.

52. Korfiatis VC, Tassani S, Matsopoulos GK, Korfiatis VC, Tassani S, Matsopoulos GK. A new ensemble classification system for fracture zone prediction using imbalanced micro-CT bone morphometrical data. IEEE J Biomed Health Inform. 2018; 22:1189–96.

53. Su Y, Kwok TC, Cummings SR, Yip BH, Cawthon PM. Can classification and regression tree analysis help identify clinically meaningful risk groups for hip fracture prediction in older American men (The MrOS Cohort Study)? JBMR Plus. 2019; 3:e10207.

54. Kong SH, Ahn D, Kim BR, Srinivasan K, Ram S, Kim H, et al. A novel fracture prediction model using machine learning in a community-based cohort. JBMR Plus. 2020; 4:e10337.

55. Engels A, Reber KC, Lindlbauer I, Rapp K, Buchele G, Klenk J, et al. Osteoporotic hip fracture prediction from risk factors available in administrative claims data: a machine learning approach. PLoS One. 2020; 15:e0232969.

56. Muehlematter UJ, Mannil M, Becker AS, Vokinger KN, Finkenstaedt T, Osterhoff G, et al. Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning. Eur Radiol. 2019; 29:2207–17.

57. Almog YA, Rai A, Zhang P, Moulaison A, Powell R, Mishra A, et al. Deep learning with electronic health records for short-term fracture risk identification: crystal bone algorithm development and validation. J Med Internet Res. 2020; 22:e22550.

58. Kruse C, Eiken P, Vestergaard P. Clinical fracture risk evaluated by hierarchical agglomerative clustering. Osteoporos Int. 2017; 28:819–32.

59. Wang Y, Zhao Y, Therneau TM, Atkinson EJ, Tafti AP, Zhang N, et al. Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records. J Biomed Inform. 2020; 102:103364.

60. Shioji M, Yamamoto T, Ibata T, Tsuda T, Adachi K, Yoshimura N. Artificial neural networks to predict future bone mineral density and bone loss rate in Japanese postmenopausal women. BMC Res Notes. 2017; 10:590.

61. Ye C, Li J, Hao S, Liu M, Jin H, Zheng L, et al. Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. Int J Med Inform. 2020; 137:104105.

62. Cuaya-Simbro G, Perez-Sanpablo AI, Munoz-Melendez A, Quinones I, Morales-Manzanares EF, Nunez-Carrera L. Comparison of machine learning models to predict risk. Found Comput Decis Sci. 2020; 45:65–77.

63. Kruse C, Eiken P, Vestergaard P. Machine learning principles can improve hip fracture prediction. Calcif Tissue Int. 2017; 100:348–60.

64. Fan Y, Li Y, Li Y, Feng S, Bao X, Feng M, et al. Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly. Endocrine. 2020; 67:412–22.

65. Basu S, Raghavan S, Wexler DJ, Berkowitz SA. Characteristics associated with decreased or increased mortality risk from glycemic therapy among patients with type 2 diabetes and high cardiovascular risk: machine learning analysis of the ACCORD trial. Diabetes Care. 2018; 41:604–12.

66. Leiserson MD, Syrgkanis V, Gilson A, Dudik M, Gillett S, Chayes J, et al. A multifactorial model of T cell expansion and durable clinical benefit in response to a PD-L1 inhibitor. PLoS One. 2018; 13:e0208422.

67. Snyder A, Nathanson T, Funt SA, Ahuja A, Buros Novik J, Hellmann MD, et al. Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: an exploratory multi-omic analysis. PLoS Med. 2017; 14:e1002309.

68. Williams SA, Kivimaki M, Langenberg C, Hingorani AD, Casas JP, Bouchard C, et al. Plasma protein patterns as comprehensive indicators of health. Nat Med. 2019; 25:1851–7.

69. The Lancet. Is digital medicine different? Lancet. 2018; 392:95.

70. AI diagnostics need attention. Nature. 2018; 555:285.

71. 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; 20:405–10.

72. Ibrahim H, Liu X, Rivera SC, Moher D, Chan AW, Sydes MR, et al. Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines. Trials. 2021; 22:11.

73. Sounderajah V, Ashrafian H, Aggarwal R, De Fauw J, Denniston AK, Greaves F, et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI Steering Group. Nat Med. 2020; 26:807–8.

74. England JR, Cheng PM. Artificial intelligence for medical image analysis: a guide for authors and reviewers. AJR Am J Roentgenol. 2019; 212:513–9.

75. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology. 2018; 286:800–9.