Share:
Share this content in WeChat
X
Review
Research progress of artificial intelligence in imaging evaluation of rheumatoid arthritis
LI Shenghu  WANG Lili 

Cite this article as: LI S H, WANG L L. Research progress of artificial intelligence in imaging evaluation of rheumatoid arthritis[J]. Chin J Magn Reson Imaging, 2024, 15(8): 229-234. DOI:10.12015/issn.1674-8034.2024.08.037.


[Abstract] Rheumatoid arthritis is a common autoimmune disease, which seriously affects the quality of life of patients. Imaging evaluation plays an important role in the diagnosis, treatment and prognosis of rheumatoid arthritis. In recent years, the rapid development of artificial intelligence, especially deep learning technology, has brought new breakthroughs to the image evaluation of rheumatoid arthritis. This paper first expounds the related concepts of artificial intelligence, then mainly based on the application of artificial intelligence in X-ray, CT, MRI and other imaging modalities, summarizes the bone lesions, synovial lesions and cartilage lesions, etc. Finally, puts forward the disadvantages of artificial intelligence at present, and prospects the application prospect of artificial intelligence in RA.
[Keywords] rheumatoid arthritis;medical imaging;artificial intelligence;machine learning;deep learning;magnetic resonance imaging

LI Shenghu1   WANG Lili2*  

1 Department of Radiology, Wuxi Traditional Chinese Medicine Hospital, Wuxi 214000, China

2 Department of Radiology, Gansu Provincial People's Hospital, Lanzhou 730000, China

Corresponding author: WANG L L, E-mail: wanglilihq@163.com

Conflicts of interest   None.

Received  2024-04-20
Accepted  2024-08-09
DOI: 10.12015/issn.1674-8034.2024.08.037
Cite this article as: LI S H, WANG L L. Research progress of artificial intelligence in imaging evaluation of rheumatoid arthritis[J]. Chin J Magn Reson Imaging, 2024, 15(8): 229-234. DOI:10.12015/issn.1674-8034.2024.08.037.

[1]
SCOTT D L, WOLFE F, HUIZINGA T W J. Rheumatoid arthritis[J]. Lancet, 2010, 376(9746): 1094-1108. DOI: 10.1016/S0140-6736(10)60826-4.
[2]
RADU A F, BUNGAU S G. Management of rheumatoid arthritis: an overview[J/OL]. Cells, 2021, 10(11): 2857 [2024-03-30]. https://pubmed.ncbi.nlm.nih.gov/34831081/. DOI: 10.3390/cells10112857.
[3]
IMID COLLABORATORS GBD. Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019[J/OL]. EClinicalMedicine, 2023, 64: 102193 [2024-03-30]. https://pubmed.ncbi.nlm.nih.gov/37731935/. DOI: 10.1016/j.eclinm.2023.102193.
[4]
VALNER A, KIRSIMÄGI Ü, MÜLLER R, et al. Factors associated with hand bone changes in early rheumatoid arthritis[J]. Musculoskeletal Care, 2023, 21(1): 108-116. DOI: 10.1002/msc.1671.
[5]
O'NEIL L J, ALPÍZAR-RODRÍGUEZ D, DEANE K D. Rheumatoid arthritis: the continuum of disease and strategies for prediction, early intervention, and prevention[J]. J Rheumatol, 2024, 51(4): 337-349. DOI: 10.3899/jrheum.2023-0334.
[6]
ARMSTRONG T M, GRAINGER A J, ROWBOTHAM E. Imaging of rheumatological disorders[J]. Magn Reson Imaging Clin N Am, 2023, 31(2): 309-320. DOI: 10.1016/j.mric.2023.01.008.
[7]
SALAFFI F, CAROTTI M, BECI G, et al. Radiographic scoring methods in rheumatoid arthritis and psoriatic arthritis[J]. Radiol Med, 2019, 124(11): 1071-1086. DOI: 10.1007/s11547-019-01001-3.
[8]
ULIJN E, DEN BROEDER N, CATE D TEN, et al. Limited diagnostic and prognostic value of routine radiographs in newly presenting arthritis suspected of rheumatoid arthritis: a retrospective study[J]. Arthritis Care Res, 2024, 76(4): 497-502. DOI: 10.1002/acr.25271.
[9]
GANDIKOTA G, FAKUDA T, FINZEL S. Computed tomography in rheumatology-From DECT to high-resolution peripheral quantitative CT[J/OL]. Best Pract Res Clin Rheumatol, 2020, 34(6): 101641 [2024-03-30]. https://pubmed.ncbi.nlm.nih.gov/33281053/. DOI: 10.1016/j.berh.2020.101641.
[10]
ØSTERGAARD M, BOESEN M. Imaging in rheumatoid arthritis: the role of magnetic resonance imaging and computed tomography[J]. Radiol Med, 2019, 124(11): 1128-1141. DOI: 10.1007/s11547-019-01014-y.
[11]
PETROVSKÁ N, PRAJZLEROVÁ K, VENCOVSKÝ J, et al. The pre-clinical phase of rheumatoid arthritis: from risk factors to prevention of arthritis[J/OL]. Autoimmun Rev, 2021, 20(5): 102797 [2024-03-30]. https://pubmed.ncbi.nlm.nih.gov/33746022/. DOI: 10.1016/j.autrev.2021.102797.
[12]
ADAMS L C, BRESSEM K K, ZIEGELER K, et al. Artificial intelligence to analyze magnetic resonance imaging in rheumatology[J/OL]. Joint Bone Spine, 2024, 91(3): 105651 [2024-03-31]. https://pubmed.ncbi.nlm.nih.gov/37797827/. DOI: 10.1016/j.jbspin.2023.105651.
[13]
MOE R H, VLIET VLIELAND T P M. Current and future challenges for rehabilitation for inflammatory arthritis[J/OL]. J Clin Med, 2024, 13(6): 1808 [2024-03-31]. https://pubmed.ncbi.nlm.nih.gov/385420310/. DOI: 10.3390/jcm13061808.
[14]
MORALES M A, MANNING W J, NEZAFAT R. Present and future innovations in AI and cardiac MRI[J/OL]. Radiology, 2024, 310(1): e231269 [2024-03-31]. https://pubmed.ncbi.nlm.nih.gov/38193835/. DOI: 10.1148/radiol.231269.
[15]
PITARCH C, UNGAN G, JULIÀ-SAPÉ M, et al. Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology[J/OL]. Cancers, 2024, 16(2): 300 [2024-03-31]. https://pubmed.ncbi.nlm.nih.gov/38254790/. DOI: 10.3390/cancers16020300.
[16]
ZHENG Y, WANG L, FENG B J, et al. Innovating healthcare: the role of ChatGPT in streamlining hospital workflow in the future[J]. Ann Biomed Eng, 2024, 52(4): 750-753. DOI: 10.1007/s10439-023-03323-w.
[17]
YIN J M, NGIAM K Y, TEO H H. Role of artificial intelligence applications in real-life clinical practice: systematic review[J/OL]. J Med Internet Res, 2021, 23(4): e25759 [2024-03-31]. https://pubmed.ncbi.nlm.nih.gov/33885365/. DOI: 10.2196/25759.
[18]
ZHANG D, FAN B, LV L, et al. Research hotspots and trends of artificial intelligence in rheumatoid arthritis: a bibliometric and visualized study[J]. Math Biosci Eng, 2023, 20(12): 20405-20421. DOI: 10.3934/mbe.2023902.
[19]
MADRID-GARCÍA A, MERINO-BARBANCHO B, RODRÍGUEZ-GONZÁLEZ A, et al. Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature[J/OL]. Semin Arthritis Rheum, 2023, 61: 152213 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37315379/. DOI: 10.1016/j.semarthrit.2023.152213.
[20]
KANIEWSKA M, DEININGER-CZERMAK E, GETZMANN J M, et al. Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time[J]. Eur Radiol, 2023, 33(3): 1513-1525. DOI: 10.1007/s00330-022-09151-1.
[21]
MCMASTER C, BIRD A, LIEW D F L, et al. Artificial intelligence and deep learning for rheumatologists[J]. Arthritis Rheumatol, 2022, 74(12): 1893-1905. DOI: 10.1002/art.42296.
[22]
BIRD A, OAKDEN-RAYNER L, MCMASTER C, et al. Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint[J/OL]. Arthritis Res Ther, 2022, 24(1): 268 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/36510330/. DOI: 10.1186/s13075-022-02972-x.
[23]
WANG H C, FU T F, DU Y Q, et al. Scientific discovery in the age of artificial intelligence[J]. Nature, 2023, 620(7972): 47-60. DOI: 10.1038/s41586-023-06221-2.
[24]
MINTZ Y, BRODIE R. Introduction to artificial intelligence in medicine[J]. Minim Invasive Ther Allied Technol, 2019, 28(2): 73-81. DOI: 10.1080/13645706.2019.1575882.
[25]
ERICKSON B J. Basic artificial intelligence techniques: machine learning and deep learning[J]. Radiol Clin North Am, 2021, 59(6): 933-940. DOI: 10.1016/j.rcl.2021.06.004.
[26]
HOLZINGER A, KEIBLINGER K, HOLUB P, et al. AI for life: trends in artificial intelligence for biotechnology[J]. N Biotechnol, 2023, 74: 16-24. DOI: 10.1016/j.nbt.2023.02.001.
[27]
IGLESIAS L L, BELLÓN P S, DEL BARRIO A P, et al. A primer on deep learning and convolutional neural networks for clinicians[J/OL]. Insights Imaging, 2021, 12(1): 117 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/34383173/. DOI: 10.1186/s13244-021-01052-z.
[28]
KARIM M R, BEYAN O, ZAPPA A, et al. Deep learning-based clustering approaches for bioinformatics[J]. Brief Bioinform, 2021, 22(1): 393-415. DOI: 10.1093/bib/bbz170.
[29]
GEUBBELMANS M, ROUSSEAU A J, BURZYKOWSKI T, et al. Artificial neural networks and deep learning[J]. Am J Orthod Dentofacial Orthop, 2024, 165(2): 248-251. DOI: 10.1016/j.ajodo.2023.11.003.
[30]
MESKÓ B, GÖRÖG M. A short guide for medical professionals in the era of artificial intelligence[J/OL]. NPJ Digit Med, 2020, 3: 126 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/33043150/. DOI: 10.1038/s41746-020-00333-z.
[31]
ALAM M R, SEO K J, ABDUL-GHAFAR J, et al. Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers[J/OL]. Brief Bioinform, 2023, 24(3): bbad151 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37114657/. DOI: 10.1093/bib/bbad151.
[32]
BARRETT J S, OSKOUI S E, RUSSELL S, et al. Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development[J/OL]. Front Pharmacol, 2023, 14: 1115356 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37033647/. DOI: 10.3389/fphar.2023.1115356.
[33]
VAN DER VELDEN B H M, KUIJF H J, GILHUIJS K G A, et al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis[J/OL]. Med Image Anal, 2022, 79: 102470 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/35576821/. DOI: 10.1016/j.media.2022.102470.
[34]
CHEN X X, WANG X M, ZHANG K, et al. Recent advances and clinical applications of deep learning in medical image analysis[J/OL]. Med Image Anal, 2022, 79: 102444 [2024-04-03]. https://pubmed.ncbi.nlm.nih.gov/35472844/. DOI: 10.1016/j.media.2022.102444.
[35]
TRIPOLITI E E, FOTIADIS D I, ARGYROPOULOU M. Automated segmentation and quantification of inflammatory tissue of the hand in rheumatoid arthritis patients using magnetic resonance imaging data[J]. Artif Intell Med, 2007, 40(2): 65-85. DOI: 10.1016/j.artmed.2007.02.003.
[36]
WEI X N, XING J Q, WANG Z Y, et al. Magnetic resonance image segmentation of articular synovium based on improved U-Net[J]. J Comput Appl, 2020, 40(11): 3340-3345. DOI: 10.11772/j.issn.1001-9081.2020030390.
[37]
WANG Y S, DENG A Q, MAO J L, et al. Automatic segmentation of knee joint synovial magnetic resonance images based on 3D VNetTrans[J]. Chin J Magn Reson, 2022, 39(3): 303-315. DOI: 10.11938/cjmr20222988.
[38]
TOLPADI A A, LUITJENS J, GASSERT F G, et al. Synthetic inflammation imaging with PatchGAN deep learning networks[J/OL]. Bioengineering, 2023, 10(5): 516 [2024-04-03]. https://pubmed.ncbi.nlm.nih.gov/37237586/. DOI: 10.3390/bioengineering10050516.
[39]
HEMALATHA R J, VIJAYBASKAR V, THAMIZHVANI T R. Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning[J]. Proc Inst Mech Eng H, 2019, 233(6): 657-667. DOI: 10.1177/0954411919845747.
[40]
TANG J, JIN Z B, ZHOU X, et al. Enhancing convolutional neural network scheme for rheumatoid arthritis grading with limited clinical data[J/OL]. Chin Phys B, 2019, 28(3): 038701 [2024-06-25]. https://iopscience.iop.org/article/10.1088/1674-1056/28/3/038701. DOI: 10.1088/1674-1056/28/3/038701.
[41]
BOESEN M, KUBASSOVA O, BOUERT R, et al. Correlation between computer-aided dynamic gadolinium-enhanced MRI assessment of inflammation and semi-quantitative synovitis and bone marrow oedema scores of the wrist in patients with rheumatoid arthritis: a cohort study[J]. Rheumatology, 2012, 51(1): 134-143. DOI: 10.1093/rheumatology/ker220.
[42]
AIZENBERG E, ROEX E A H, NIEUWENHUIS W P, et al. Erratum to: automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: a feasibility study[J]. Magn Reson Med, 2018, 79(2): 1127-1134. DOI: 10.1002/mrm.26712.
[43]
ZHENG Y, BAI C, ZHANG K, et al. Deep-learning based quantification model for hip bone marrow edema and synovitis in patients with spondyloarthritis based on magnetic resonance images[J/OL]. Front Physiol, 2023, 14: 1132214 [2024-04-03]. https://pubmed.ncbi.nlm.nih.gov/36935744/. DOI: 10.3389/fphys.2023.1132214.
[44]
DISINI L, FOSTER M, MILLIGAN P J, et al. Cancellous bone changes in the radius of patients with rheumatoid arthritis: a cross-sectional quantitative macroradiographic study[J]. Rheumatology, 2004, 43(9): 1150-1157. DOI: 10.1093/rheumatology/keh270.
[45]
TÖPFER D, FINZEL S, MUSEYKO O, et al. Segmentation and quantification of bone erosions in high-resolution peripheral quantitative computed tomography datasets of the metacarpophalangeal joints of patients with rheumatoid arthritis[J]. Rheumatology, 2014, 53(1): 65-71. DOI: 10.1093/rheumatology/ket259.
[46]
LANGS G, PELOSCHEK P, BISCHOF H, et al. Automatic quantification of joint space narrowing and erosions in rheumatoid arthritis[J]. IEEE Trans Med Imaging, 2009, 28(1): 151-164. DOI: 10.1109/TMI.2008.2004401.
[47]
HIRANO T, NISHIDE M, NONAKA N, et al. Development and validation of a deep-learning model for scoring of radiographic finger joint destruction in rheumatoid arthritis[J/OL]. Rheumatol Adv Pract, 2019, 3(2): rkz047 [2024-04-03]. https://pubmed.ncbi.nlm.nih.gov/31872173/. DOI: 10.1093/rap/rkz047.
[48]
MA Y T, PAN I, KIM S Y, et al. Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography[J]. Skeletal Radiol, 2024, 53(2): 377-383. DOI: 10.1007/s00256-023-04408-2.
[49]
ALLANDER E, FORSGREN P O, PETTERSSON H, et al. Computerized assessment of radiological changes of the hand in rheumatic diseases[J]. Scand J Rheumatol, 1989, 18(5): 291-296. DOI: 10.3109/03009748909095032.
[50]
HUO Y H, VINCKEN K L, VAN DER HEIJDE D, et al. Automatic quantification of radiographic wrist joint space width of patients with rheumatoid arthritis[J]. IEEE Trans Biomed Eng, 2017, 64(11): 2695-2703. DOI: 10.1109/TBME.2017.2659223.
[51]
LIU F, ZHOU Z Y, SAMSONOV A, et al. Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection[J]. Radiology, 2018, 289(1): 160-169. DOI: 10.1148/radiol.2018172986.
[52]
BRUI E, EFIMTCEV A Y, FOKIN V A, et al. Deep learning-based fully automatic segmentation of wrist cartilage in MR images[J/OL]. NMR Biomed, 2020, 33(8): e4320 [2024-04-05]. https://pubmed.ncbi.nlm.nih.gov/32394453/. DOI: 10.1002/nbm.4320.
[53]
WANG H L, OU Y F, FANG W X, et al. A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis[J/OL]. Comput Med Imaging Graph, 2023, 108: 102273 [2024-06-26]. https://pubmed.ncbi.nlm.nih.gov/37531811/. DOI: 10.1016/j.compmedimag.2023.102273.
[54]
OKITA Y, HIRANO T, WANG B W, et al. Automatic evaluation of atlantoaxial subluxation in rheumatoid arthritis by a deep learning model[J/OL]. Arthritis Res Ther, 2023, 25(1): 181 [2024-06-26]. https://pubmed.ncbi.nlm.nih.gov/37749583/. DOI: 10.1186/s13075-023-03172-x.
[55]
FUJIWARA K, FANG W X, OKINO T, et al. Quick and accurate selection of hand images among radiographs from various body parts using deep learning[J]. J Xray Sci Technol, 2020, 28(6): 1199-1206. DOI: 10.3233/XST-200694.
[56]
SCHLERETH M, KLEYER A, UTZ J, et al. pos0900 automatic scoring of erosion, synovitis and bone oedema in rheumatoid arthritis using deep learning on hand magnetic resonance imaging[C]//Scientific Abstracts. BMJ Publishing Group Ltd and European League Against Rheumatism, 2023 [2024-06-26]. https://ard.bmj.com/content/82/Suppl_1/758.1. DOI: 10.1136/annrheumdis-2023-eular.1028.
[57]
FOLLE L, BAYAT S, KLEYER A, et al. Advanced neural networks for classification of MRI in psoriatic arthritis, seronegative, and seropositive rheumatoid arthritis[J]. Rheumatology, 2022, 61(12): 4945-4951. DOI: 10.1093/rheumatology/keac197.
[58]
KATO M, IKEDA K, SUGIYAMA T, et al. Associations of ultrasound-based inflammation patterns with peripheral innate lymphoid cell populations, serum cytokines/chemokines, and treatment response to methotrexate in rheumatoid arthritis and spondyloarthritis[J/OL]. PLoS One, 2021, 16(5): e0252116 [2024-06-26]. https://pubmed.ncbi.nlm.nih.gov/34019595/. DOI: 10.1371/journal.pone.0252116.
[59]
COBB R, COOK G J R, READER A J. Deep learned segmentations of inflammation for novel ⁹⁹mTc-maraciclatide imaging of rheumatoid arthritis[J/OL]. Diagnostics, 2023, 13(21): 3298 [2024-06-26]. https://pubmed.ncbi.nlm.nih.gov/37958194/. DOI: 10.3390/diagnostics13213298.
[60]
REED M, LE SOUËF T, RAMPONO E. Pilot study of a machine-learning tool to assist in the diagnosis of hand arthritis[J]. Intern Med J, 2022, 52(6): 959-967. DOI: 10.1111/imj.15173.
[61]
ALARCÓN-PAREDES A, GUZMÁN-GUZMÁN I P, HERNÁNDEZ-ROSALES D E, et al. Computer-aided diagnosis based on hand thermal, RGB images, and grip force using artificial intelligence as screening tool for rheumatoid arthritis in women[J]. Med Biol Eng Comput, 2021, 59(2): 287-300. DOI: 10.1007/s11517-020-02294-7.
[62]
STOEL B C. Artificial intelligence in detecting early RA[J]. Semin Arthritis Rheum, 2019, 49(3S): S25-S28. DOI: 10.1016/j.semarthrit.2019.09.020.

PREV Research progress of cardiac magnetic resonance feature tracking technique in evaluating myocardial strain in autoimmune rheumatic diseases
NEXT
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn