Artificial intelligence effectivity in fracture detection

V. Boginskis; S. Zadoroznijs; I. Cernavska; D. Beikmane; J.  Sauka

doi:10.26641/2307-0404.2023.3.288965

Авторы

V. Boginskis Hospital of Traumatology and Orthopaedics, Duntes str., 22, Riga, LV-1005, Latvia https://orcid.org/0009-0001-1320-8471
S. Zadoroznijs Hospital of Traumatology and Orthopaedics, Duntes str., 22, Riga, LV-1005, Latvia https://orcid.org/0009-0000-5207-3576
I. Cernavska Hospital of Traumatology and Orthopaedics, Duntes str., 22, Riga, LV-1005, Latvia https://orcid.org/0009-0006-7429-543X
D. Beikmane Riga Stradins University, Dzirciema str., 16, Riga, LV-1007, Latvia https://orcid.org/0009-0003-0322-4093
J. Sauka Hospital of Traumatology and Orthopaedics 1 Duntes str., 22, Riga, LV-1005, Latvia https://orcid.org/0009-0003-2422-2994

DOI:

https://doi.org/10.26641/2307-0404.2023.3.288965

Ключевые слова:

traumatology, radiology, clinical studies, fluoroscopy, orthopaedics diagnostics

Аннотация

The scientific study aimed to explore the practical implementation of artificial intelligence (AI) technologies in radiology and traumatology for fracture detection, as well as evaluate their overall effectiveness in modern medicine. In recent years, AI has gained significant traction in the healthcare industry, enabling the analysis of patients' clinical data and facilitating disease diagnosis, monitoring, risk assessment, and surgical intervention possibilities. The relevance of the scientific work is in the gradual expansion of practical applications of artificial intelligence technologies in medicine, particularly in radiology for diagnosing fractures. The study aimed to investigate the practical effectiveness of AI technology in fracture detection on example of Hospital of Traumatology and Orthopaedics in Riga, Latvia. The methodological approach combined system analysis of AI system implementation in modern medical institutions for creating X-ray images with a clinical study of fracture diagnosis experience at the Hospital of Orthopedics and Traumatology in Riga, Latvia. Fractures were detected by radiologists, attending physicians, and the AI program, with comparisons made between them. Results were analyzed to assess the program's efficacy. The results of the study demonstrated the high effectiveness of AI technologies in fracture detection. The application of these systems in clinical practice led to a significant reduction in diagnostic errors (by 2-3 times) and an increase in diagnostic accuracy (from 78.1% to 85.2%). Moreover, AI systems proved to be capable of detecting fractures that were not initially identified during routine examinations by paramedics and medical practitioners. This emphasized the practicality of expanding the use of these systems in clinical practice. The practical significance of the obtained results is in their potential use in the development of software systems based on AI, aimed at enhancing fracture diagnosis in medical institutions. These findings provided valuable insights for further advancements in AI-based technologies for fracture detection.

Библиографические ссылки

Yin J, Ngiam KY, Teo HH. Role of artificial intel-ligence applications in real-life clinical practice: Syste-matic review. J Med Internet Res. 2021;23(4):1-17. doi: https://doi.org/10.2196/25759

Sandris Rūsiņš A. DRB was the first in Latvia to use artificial intelligence in diagnostics [Internet]. Nasha. 2022 [cited 2023 Jun 7]. Available from: https://nasha.la.lv/drb-pervaya-v-latvii-stala-ispolzovat-iskusstvennyj-intellekt-pri-diagnostike

Duron L, Ducarouge A, Gillibert A, Lainé J, Al-louche C, Cherel N, et al. Assessment of an AI aid in detection of adult appendicular skeletal fractures by emer¬gency physicians and radiologists: A multicenter cross-sectional diagnostic study. Radiology. 2021;300(1):120-9. doi: https://doi.org/10.1148/radiol.2021203886

Boginskis V. Effectiveness of artificial intelligence in fracture diagnosis. Riga: University of Latvia; 2022.

Yokota H, Goto M, Bamba C, Kiba M, Yamada K. Reading efficiency can be improved by minor mo-dification of assigned duties: a pilot study on a small team of general radiologists. Jpn J Radiol. 2017;35(5):262-8. doi: https://doi.org/10.1007/s11604-017-0629-8

Hayashi D, Kompell AJ, Ventre J, Ducarouge A, Nguyen T, Regnard N-E, et al. Automated detection of acute appendicular skeletal fractures in pediatric patients using deep learning. Skeletal Radiol. 2022;51(11):2129-39. doi: https://doi.org/10.1007/s00256-022-04070-0

Guermazi A, Tannoury C, Kompel AJ, Murakami AM, Ducarouge A, Gillibert A, et al. Improving radio-graphic fracture recognition performance and efficiency using artificial intelligence. Radiology. 2021;302(3):1-10. doi: https://doi.org/10.1148/radiol.210937

Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. 2021;139(1):4-15. doi: https://doi.org/10.1093/bmb/ldab016

Medical Diagnostic Information System [Internet]. 2022 [cited 2023 Jul 7]. Available from: https://datamed.lv/

Backer HC, Wu CH, Strauch RJ. Systematic review of diagnosis of clinically suspected scaphoid fractures. J Wrist Surg. 2020;9(1):81-9. doi: https://doi.org/10.1055/s-0039-1693147

Conant EF, Toledano AY, Periaswamy S, Fo-tin SV, Go J, Boatsman JE, et al. Improving accuracy and efficiency with concurrent use of artificial intelligence for digital breast tomosynthesis. Radiol Artif Intell. 2019;1(4):1-12. doi: https://doi.org/10.1148/ryai.2019180096

Canoni-Meynet L, Verdot P, Danner A, Calame P, Aubry S. Added value of an artificial intelligence solution for fracture detection in the radiologist’s daily trauma emer¬gencies workflow. Diagn Interv Imaging. 2022;103(12):594-600. doi: https://doi.org/10.1016/j.diii.2022.06.004

Al-Sani F, Prasad S, Panwar J, Stimec J, Khosroawshahi A, Mizzi T, et al. Adverse events from emer-gency physician pediatric extremity radiograph interpre-tations: A prospective cohort study. Acad Emerg Med. 2020;27(2):128-38. doi: https://doi.org/10.1111/acem.13884

Alberich-Bayarri A, Marti-Bonmati L, Angeles Pérez M, Sanz-Requena R, Lerma-Garrido JJ, García-Martí G, et al. Assessment of 2D and 3D fractal dimension measurements of trabecular bone from high-spatial resolution magnetic resonance images at 3 T. Med Phys. 2010;7(9):4930-7. doi: https://doi.org/10.1118/1.3481509

Kuo RYL, Harrison C, Curran TA, Jones B, Freethy A, Cussons D, et al. Artificial intelligence in frac-ture detection: A systematic review and meta-analysis. Radiology. 2022;304(1):50-62. doi: https://doi.org/10.1148/radiol.211785

Aliaga I, Vera V, Vera M, Garcia E, Pedrera M, Pajares G. Automatic computation of mandibular indices in dental panoramic radiographs for early osteoporosis detection. Artif Intell Med. 2020;103:101816. doi: https://doi.org/10.1016/j.artmed.2020.101816

Reichert G, Bellamine A, Fontaine M, Naipeanu B, Altar A, Mejean E, et al. How can a deep learning algorithm improve fracture detection on x-rays in the emergency room? J Imaging. 2021;7(7):1-10. doi: https://doi.org/10.3390/jimaging7070105

Rosenberg GS, Cina A, Schiró GR, Giorgi PD, Gueorguiev B, Alini M, et al. Artificial intelligence ac-curately detects 39 traumatic thoracolumbar fractures on sagittal radiographs. Medicina. 2022;58(8):1-12. doi: https://doi.org/10.3390/medicina58080998

Tarantino U, Giai Via A, Macrì E, Eramo A, Marino V, Marsella LT. Professional liability in ortho-paedics and traumatology in Italy. Clin Orthop Relat Res. 2013;471(10):3349-57. doi: https://doi.org/10.1007/s11999-013-3165-6

Rebours C, Glatre R, Plaisance P, Dohan A, Truchot J, Chauvin A. Diagnostic errors of nasal fractures in the emergency department: A monocentric retrospective study. World J Emerg Med. 2022;13(2):120-3. doi: https://doi.org/10.5847/wjem.j.1920-8642.2022.014

Khan IH, Jamil W, Lynn SM, Khan OH, Markland K, Giddins G. Analysis of NHSLA claims in ortho-pedic surgery. Orthopedics. 2012;35(5):726-31. doi: https://doi.org/10.3928/01477447-20120426-28