The Use of Artificial Intelligence in the Evaluation of Knee Pathology

Elisabeth R. Garwood; Ryan Tai; Ganesh Joshi; George J. Watts V

doi:10.1055/s-0039-3400264

Seminars in Musculoskeletal Radiology, Table of Contents

Semin Musculoskelet Radiol 2020; 24(01): 021-029
DOI: 10.1055/s-0039-3400264

Review Article

Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

The Use of Artificial Intelligence in the Evaluation of Knee Pathology

Elisabeth R. Garwood

¹Division of Musculoskeletal Imaging and Intervention, Department of Radiology, University of Massachusetts Memorial Medical Center and University of Massachusetts Medical School, Worcester, Massachusetts

,

Ryan Tai

¹Division of Musculoskeletal Imaging and Intervention, Department of Radiology, University of Massachusetts Memorial Medical Center and University of Massachusetts Medical School, Worcester, Massachusetts

,

Ganesh Joshi

¹Division of Musculoskeletal Imaging and Intervention, Department of Radiology, University of Massachusetts Memorial Medical Center and University of Massachusetts Medical School, Worcester, Massachusetts

,

George J. Watts V

¹Division of Musculoskeletal Imaging and Intervention, Department of Radiology, University of Massachusetts Memorial Medical Center and University of Massachusetts Medical School, Worcester, Massachusetts

› Author Affiliations

Abstract

Artificial intelligence (AI) holds the potential to revolutionize the field of radiology by increasing the efficiency and accuracy of both interpretive and noninterpretive tasks. We have only just begun to explore AI applications in the diagnostic evaluation of knee pathology. Experimental algorithms have already been developed that can assess the severity of knee osteoarthritis from radiographs, detect and classify cartilage lesions, meniscal tears, and ligament tears on magnetic resonance imaging, provide automatic quantitative assessment of tendon healing, detect fractures on radiographs, and predict those at highest risk for recurrent bone tumors. This article reviews and summarizes the most current literature.

Keywords

artificial intelligence - magnetic resonance imaging - deep learning - knee

Full Text

References

References
1 Gage BE, McIlvain NM, Collins CL, Fields SK, Comstock RD. Epidemiology of 6.6 million knee injuries presenting to United States emergency departments from 1999 through 2008. Acad Emerg Med 2012; 19 (04) 378-385
2 Gyftopoulos S, Harkey P, Hemingway J, Hughes DR, Rosenkrantz AB, Duszak Jr R. Changing musculoskeletal extremity imaging utilization from 1994 through 2013: a Medicare beneficiary perspective. AJR Am J Roentgenol 2017; 209 (05) 1103-1109
3 Bien N, Rajpurkar P, Ball RL. , et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Med 2018; 15 (11) e1002699
4 Hirschmann A, Cyriac J, Stieltjes B, Kober T, Richiardi J, Omoumi P. Artificial intelligence in musculoskeletal imaging: review of current literature, challenges, and trends. Semin Musculoskelet Radiol 2019; 23 (03) 304-311
5 Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18 (08) 500-510
6 Cross M, Smith E, Hoy D. , et al. The global burden of hip and knee osteoarthritis: estimates from the Global Burden of Disease 2010 study. Ann Rheum Dis 2014; 73 (07) 1323-1330
7 Norman B, Pedoia V, Majumdar S. Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry. Radiology 2018; 288 (01) 177-185
8 Lawrence RC, Felson DT, Helmick CG. , et al; National Arthritis Data Workgroup. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part II. Arthritis Rheum 2008; 58 (01) 26-35
9 Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis 1957; 16 (04) 494-502
10 Gossec L, Jordan JM, Mazzuca SA. , et al; OARSI-OMERACT Task Force Total Articular Replacement as Outcome Measure in OA. Comparative evaluation of three semi-quantitative radiographic grading techniques for knee osteoarthritis in terms of validity and reproducibility in 1759 X-rays: report of the OARSI-OMERACT task force. Osteoarthritis Cartilage 2008; 16 (07) 742-748
11 Culvenor AG, Engen CN, Øiestad BE, Engebretsen L, Risberg MA. Defining the presence of radiographic knee osteoarthritis: a comparison between the Kellgren and Lawrence system and OARSI atlas criteria. Knee Surg Sports Traumatol Arthrosc 2015; 23 (12) 3532-3539
12 Dacre JE, Huskisson EC. The automatic assessment of knee radiographs in osteoarthritis using digital image analysis. Br J Rheumatol 1989; 28 (06) 506-510
13 Antony J, McGuinness K, O'Connor NE, Moran K. Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks. Abstract available at: https://arxiv.org/abs/1609.02469 . Accessed July 7, 2019
14 Oka H, Muraki S, Akune T. , et al. Fully automatic quantification of knee osteoarthritis severity on plain radiographs. Osteoarthritis Cartilage 2008; 16 (11) 1300-1306
15 Shamir L, Ling SM, Scott W, Hochberg M, Ferrucci L, Goldberg IG. Early detection of radiographic knee osteoarthritis using computer-aided analysis. Osteoarthritis Cartilage 2009; 17 (10) 1307-1312
16 Shamir L, Ling SM, Scott Jr WW. , et al. Knee x-ray image analysis method for automated detection of osteoarthritis. IEEE Trans Biomed Eng 2009; 56 (02) 407-415
17 Antony J, McGuinness K, Moran K, O'Connor N. Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks. In: Perner P. , ed. Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science. Vol 10358. Cham, Switzerland: Springer; 2017
18 Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S. Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci Rep 2018; 8 (01) 1727
19 Norman B, Pedoia V, Noworolski A, Link TM, Majumdar S. Applying densely connected convolutional neural networks for staging osteoarthritis severity from plain radiographs. J Digit Imaging 2019; 32 (03) 471-477
20 Abedin J, Antony J, McGuinness K. , et al. Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images. Sci Rep 2019; 9 (01) 5761
21 Quatman CE, Hettrich CM, Schmitt LC, Spindler KP. The clinical utility and diagnostic performance of magnetic resonance imaging for identification of early and advanced knee osteoarthritis: a systematic review. Am J Sports Med 2011; 39 (07) 1557-1568
22 Kijowski R, Blankenbaker DG, Munoz Del Rio A, Baer GS, Graf BK. Evaluation of the articular cartilage of the knee joint: value of adding a T2 mapping sequence to a routine MR imaging protocol. Radiology 2013; 267 (02) 503-513
23 Liu F, Zhou Z, Samsonov A. , et al. Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection. Radiology 2018; 289 (01) 160-169
24 Pedoia V, Norman B, Mehany SN, Bucknor MD, Link TM, Majumdar S. 3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects. J Magn Reson Imaging 2019; 49 (02) 400-410
25 Spindler KP, Wright RW. Clinical practice. Anterior cruciate ligament tear. N Engl J Med 2008; 359 (20) 2135-2142
26 Lohmander LS, Englund PM, Dahl LL, Roos EM. The long-term consequence of anterior cruciate ligament and meniscus injuries: osteoarthritis. Am J Sports Med 2007; 35 (10) 1756-1769
27 Fridén T, Zätterström R, Lindstrand A, Moritz U. Disability in anterior cruciate ligament insufficiency. An analysis of 19 untreated patients. Acta Orthop Scand 1990; 61 (02) 131-135
28 Everhart JS, Kirven JC, Abouljoud MM, DiBartola AC, Kaeding CC, Flanigan DC. Effect of delayed primary anterior cruciate ligament reconstruction on medial compartment cartilage and meniscal health. Am J Sports Med 2019; 47 (08) 1816-1824
29 Mink JH, Levy T, Crues III JV. Tears of the anterior cruciate ligament and menisci of the knee: MR imaging evaluation. Radiology 1988; 167 (03) 769-774
30 Robertson PL, Schweitzer ME, Bartolozzi AR, Ugoni A. Anterior cruciate ligament tears: evaluation of multiple signs with MR imaging. Radiology 1994; 193 (03) 829-834
31 Štajduhar I, Mamula M, Miletić D, Ünal G. Semi-automated detection of anterior cruciate ligament injury from MRI. Comput Methods Programs Biomed 2017; 140: 151-164
32 Chang PD, Wong TT, Rasiej MJ. Deep learning for detection of complete anterior cruciate ligament tear. J Digit Imaging 2019 ; March 11 (Epub ahead of print)
33 Liu F, Guan B, Zhou Z. , et al. Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning. Radiol Artif Intell 2019; 1 (03)
34 Zikria B, Hafezi-Nejad N, Roemer FW, Guermazi A, Demehri S. Meniscal surgery: risk of radiographic joint space narrowing progression and subsequent knee replacement-data from the Osteoarthritis Initiative. Radiology 2017; 282 (03) 807-816
35 Recht MP, Kramer J. MR imaging of the postoperative knee: a pictorial essay. Radiographics 2002; 22 (04) 765-774
36 De Smet AA. How I diagnose meniscal tears on knee MRI. AJR Am J Roentgenol 2012; 199 (03) 481-499
37 Nguyen JC, De Smet AA, Graf BK, Rosas HG. MR imaging-based diagnosis and classification of meniscal tears. Radiographics 2014; 34 (04) 981-999
38 Boniatis I, Panayiotakis G, Panagiotopoulos E. A computer-based system for the discrimination between normal and degenerated menisci from magnetic resonance images. Paper presented at: 2008 IEEE International Workshop on Imaging Systems and Techniques, September 10–12, 2008; Crete, Greece. Doi: 10.1109/IST.2008.4659996
39 Fu JC, Lin CC, Wang CN, Ou YK. Computer-aided diagnosis for knee meniscus tears in magnetic resonance imaging. J Ind Prod Eng 2013; 30: 67-77
40 Ramakrishna B, Liu W, Saiprasad G. , et al. An automatic computer-aided detection system for meniscal tears on magnetic resonance images. IEEE Trans Med Imaging 2009; 28 (08) 1308-1316
41 Köse C, Gençalioğlu O, Şevik U. An automatic diagnosis method for the knee meniscus tears in MR images. Expert Syst Appl 2009; 36 (2, Part 1): 1208-1216
42 Saygılı A, Albayrak S. An efficient and fast computer-aided method for fully automated diagnosis of meniscal tears from magnetic resonance images. Artif Intell Med 2019; 97: 118-130
43 Zarandi MHF, Khadangi A, Karimi F, Turksen IB. A computer-aided type-II fuzzy image processing for diagnosis of meniscus tear. J Digit Imaging 2016; 29 (06) 677-695
44 Couteaux V, Si-Mohamed S, Nempont O. , et al. Automatic knee meniscus tear detection and orientation classification with Mask-RCNN. Diagn Interv Imaging 2019; 100 (04) 235-242
45 Roblot V, Giret Y, Bou Antoun M. , et al. Artificial intelligence to diagnose meniscus tears on MRI. Diagn Interv Imaging 2019; 100 (04) 243-249
46 Kapiński N, Zieliński J, Borucki BA. , et al. Monitoring of the Achilles tendon healing process: can artificial intelligence be helpful?. Acta Bioeng Biomech 2019; 21 (01) 103-111
47 Garwood ER, Duarte A, Bencardino JT. MR imaging of entrapment neuropathies of the lower extremity. Radiol Clin North Am 2018; 56 (06) 997-1012
48 Balsiger F, Steindel C, Arn M. , et al. Segmentation of peripheral nerves from magnetic resonance neurography: a fully-automatic, deep learning-based approach. Front Neurol 2018; 9: 777
49 Liu S, Wang Y, Yang X. , et al. Deep learning in medical ultrasound analysis: a review. Engineering 2019; 5 (02) 261-275
50 Peleteiro-Pensado M, Barrientos-Ruiz I, Ortiz-Cruz EJ. Bone tumors around the knee. In: Rodríguez-Merchán EC, Liddle AD. , eds. Joint Preservation in the Adult Knee. Cham, Switzerland;: Springer; 2017: 153-173
51 He Y, Guo J, Ding X. , et al. Convolutional neural network to predict the local recurrence of giant cell tumor of bone after curettage based on pre-surgery magnetic resonance images. Eur Radiol 2019; 29 (10) 5441-5451
52 Lindsey R, Daluiski A, Chopra S. , et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A 2018; 115 (45) 11591-11596
53 Joseph GB, McCulloch CE, Nevitt MC. , et al. Tool for osteoarthritis risk prediction (TOARP) over 8 years using baseline clinical data, X-ray, and MRI: data from the Osteoarthritis Initiative. J Magn Reson Imaging 2018; 47 (06) 1517-1526
54 Lim J, Kim J, Cheon S. A deep neural network-based method for early detection of osteoarthritis using statistical data. Int J Environ Res Public Health 2019; 16 (07) E1281