Comparison of algorithms for detection of active inflammatory lesions in sacroiliitis
DOI:
https://doi.org/10.15584/ejcem.2024.1.11Keywords:
artificial intelligence, axial spondyloarthritis, bone marrow edemaAbstract
Introduction and aim. Artificial intelligence is increasingly being used in the medicine, particularly in radiological diagnosis of diseases such as an axial spondyloarthritis (axSpA). The aim of this study is to compare the available algorithms designed to detect active sacroiliitis in patients with axSpA.
Material and methods. Four algorithms, two semi-automated and two full-automated for the assessment of bone marrow ede ma (BME) on magnetic resonance imaging (MRI) of the sacroiliac joints (SIJs) were included in the study. They were described and compared in terms of specificity, sensitivity, and correlation of BME detection findings between AI and experts.
Analysis of the literature. Among all automated algorithms, the one created by Bressem et al. had the highest number of ex aminations analyzed in the study, involving 593 MRIs of SIJs. The sensitivity and specificity, as well as the correlation between the AI’s detection of BME versus manual, were not calculated for each algorithm. Rzecki’s algorithm had the greatest sensitivity and specificity for BME detection reaching 0.95 and 0.96, respectively. In addition, its Speraman’s coefficient of correlation be tween manual and automated measurements was 0.866.
Conclusion. Each of described algorithms is certainly useful in assessing BME in the MRI examinations of SIJs.
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References
Stoel B. Use of Artificial Intelligence in Imaging in Rheumatology-Current Status and Future. Perspectives RMD Open. 2020;6.
Slobodin G, Hussein H, Rosner I, Eshed I. Sacroiliitis – early diagnosis is key. J Inflamm Res. 2018;11:339-344. doi: 10.2147/jir.s149494
Chahal BS, Kwan ALC, Dhillon SS, et al. Radiation exposure to the sacroiliac joint from low-dose CT compared with radiography. AJR Am J Roentgenol. 2018;211(5):1058-1062. doi: 10.2214/ajr.18.19678
Gutierrez M, Rodriguez S, Soto-Fajardo C, et al. Ultrasound of sacroiliac joints in spondyloarthritis: a systematic review. Rheumatol Int. 2018;38(10):1791-1805. doi: 10.1007/s00296-018-4126-x
Rudwaleit M, van der Heijde D, Landewe R, et al. The development of Assessment of SpondyloArthritis international Society classification criteria for axial spondyloarthritis (part II): validation and final selection. Ann Rheum Dis. 2009;68(6):777-783. doi: 10.1136/ard.2009.108233
Sieper J, Poddubnyy D. Axial spondyloarthritis. Lancet. 2017;390(10089):73-84. doi: 10.1016/s0140-6736(16)31591-4
Kucybała I, Tabor Z, Polak J, Urbanik A, Wojciechowski W. The semi-automated algorithm for the detection of bone marrow oedema lesions in patients with axial spondyloarthritis. Rheumatol Int. 2020;40(4):625-633. doi: 10.1007/s00296-020-04511-w
Kumthekar A, Bittar M, Dubreuil M. Educational needs and challenges in axial spondyloarthritis. Curr Opin Rheumatol. 2021;33(4):313-318. doi: 10.1097/BOR.0000000000000806
Sieper J, Rudwaleit M, Baraliakos X, et al. The Assessment of SpondyloArthritis international Society (ASAS) handbook: a guide to assess spondyloarthritis. Ann Rheum Dis. 2009;68(2):ii1-ii44. doi: 10.1136/ard.2008.104018
Tsoi C, Griffith JF, Lee R, Wong P, Tam LS. Imaging of Sacroiliitis: Current Status, Limitations and Pitfalls. Quant. Imaging Med Surg. 2019;9:318-335.
Maksymowych WP, Lambert RG, Baraliakos X, et al. Data-driven definitions for active and structural MRI lesions in the sacroiliac joint in spondyloarthritis and their predictive utility. Rheumatology (Oxford). 2021;60(10):4778-4789. doi: 10.1093/rheumatology/keab099
Zarco P, SCAISS Study Group, Almodóvar R, Bueno Á, Molinero LM. Development and validation of SCAISS, a tool for semi-automated quantification of sacroilitis by magnetic resonance in spondyloarthritis. Rheumatol Int. 2018;38(10):1919-1926. doi: 10.1007/s00296-018-4104-3
Rzecki K, Kucybała I, Gut D, et al. Fully Automated Algorithm for the Detection of Bone Marrow Oedema Lesions in Patients with Axial Spondyloarthritis-Feasibility Study. Biocybern Biomed Eng. 2021;41:833-853.
Garrido-González C, Pineda ML, Garrido-Castro JL, et al. Collantes Estevez POS0958 Responsiveness of Conventional, Semi-Automatic and Full-Automatic Methods to Quantify Marrow Bone Edema Lesions in MRI. Ann Rheum Dis. 2021;80:743-744.
Bressem KK, Adams LC, Proft F, et al. Deep learning detects changes indicative of axial spondyloarthritis at MRI of sacroiliac joints. Radiology. 2022;305(3):655-665. doi: 10.1148/radiol.212526
Ożga J, Wyka M, Raczko A, et al. Performance of fully automated algorithm detecting bone marrow edema in sacroiliac joints. J Clin Med. 2023;12(14):4852. doi: 10.3390/jcm12144852
Weber U, Ostergaard M, Lambert RG, et al. Candidate lesion-based criteria for defining a positive sacroiliac joint MRI in two cohorts of patients with axial spondyloarthritis. Ann Rheum Dis. 2015;74(11):1976-1982.
Maksymowych WP, Dhillon SS, Chiowchanwisawakit P, et al. Development and validation of web-based training modules for systematic evaluation of active inflammatory lesions in the spine and sacroiliac joints in spondyloarthritis. J Rheumatol Suppl. 2009;84(0):48-57. doi: 10.3899/jrheum.090620
Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31-38. doi: 10.1038/s41591-021-01614-0
Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020;2020. doi: 10.1093/database/baaa010
Matheny ME, Whicher D, Thadaney Israni S. Artificial intelligence in health care: A report from the national academy of medicine. JAMA. 2020;323(6):509. doi: 10.1001/jama.2019.21579
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