A comparative analysis of automatic classification and grading methods for knee osteoarthritis focussing on X-ray images

• Autorzy: Saini Deepak , Chand Trilok , Chouhan Devendra K. , Prakash Mahesh

Identyfikatory

DOI

10.1016/j.bbe.2021.03.002

• Języki publikacji: EN

Abstrakty

EN

Objective: The purpose of present review paper is to introduce the reader to key directions of manual, semi-automatic and automatic knee osteoarthritis (OA) severity classification from plain radiographs. This is a narrative review article in which we have described recent developments in severity evaluation of knee OA from X-ray images. We have primarily focussed on automatic analysis and have reviewed articles in which machine learning, transfer learning, active learning, etc. have been employed on X-ray images to access and classify the severity of knee OA. Methods: All original research articles on OA detection and classification using X-ray images published in English were searched on PubMed database, Google Scholar, RSNA radiology databases in year 2019. The search terms of ‘‘knee Osteoarthritis” were combined with search terms ‘‘Machine Learning”, ‘severity” and ‘‘X-ray”. Results: The initial search on various publication databases revealed a total of 743 results, out of which only 26 articles were considered relevant to radiographic knee OA severity analysis. The majority of the articles were based on automatic analysis. Manual segmentation based articles were least in numbers. Conclusion: Computer aided methods to diagnose knee OA are great tools to detect OA at ealry stages. Advancements in Human Computer Interface systems have led the researchers to bridge the gap between machine learning algorithms and expert healthcare professionals to provide better and timely treatment options to the knee OA affected patients.

Słowa kluczowe

EN

knee osteoarthritis; convolution neural network; deep learning; machine learning; computer aided diagnosis

PL

staw kolanowy; choroba zwyrodnieniowa; sieć neuronowa konwolucyjna; uczenie głębokie; uczenie maszynowe; diagnoza wspomagana komputerowo

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 2
• Strony: 419--444
• Opis fizyczny: Bibliogr. 103 poz., rys., tab.

Twórcy

autor

Saini Deepak

• Punjab Engineering College(Deemed to be University), Sector-12, Chandigarh, India

autor

Chand Trilok

• Punjab Engineering College(Deemed to be University), Sector-12, Chandigarh, India

autor

Chouhan Devendra K.

• Department of Orthopaedics, Post Graduate Institute of Medical Education & Research (PGIMER), Chandigarh, India

autor

Prakash Mahesh

• Department of Radiology, Post Graduate Institute of Medical Education & Research (PGIMER), Chandigarh, India

Bibliografia

• [1] Eijgenraam SM, Chaudhari AS, Reijman M, Bierma-Zeinstra SM, Hargreaves BA, Runhaar J, Heijboer FW, Gold GE, Oei EH. Time-saving opportunities in knee osteoarthritis: T 2 mapping and structural imaging of the knee using a single 5-min mri scan. Eur Radiol 2020;30(4):2231–40.
• [2] versusarthritis.org, ”Versus arthritis, 2019, osteoarthritis (oa).” 2019.
• [3] Ackerman IN, Kemp JL, Crossley KM, Culvenor AG, Hinman RS. Hip and knee osteoarthritis affects younger people, too. J Orthopaedic Sports Phys Therapy 2017;47(2):67–79.
• [4] Safiri S, Kolahi AA, Hoy D, Smith E, Bettampadi D, Mansournia MA, Almasi-Hashiani A, Ashrafi-Asgarabad A, Moradi-Lakeh M, Qorbani M. Global, regional and national burden of rheumatoid arthritis 1990–2017: a systematic analysis of the global burden of disease study 2017. Ann Rheumatic Diseases 2019;78(11):1463–71.
• [5] 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 (1):1727.
• [6] Cross M, Smith E, Hoy D, Nolte S, Ackerman I, Fransen M, Bridgett L, Williams S, Guillemin F, Hill CL, Laslett LL, Jones G, Cicuttini F, Osborne R, Vos T, Buchbinder R, Woolf A, March L. The global burden of hip and knee osteoarthritis: estimates from the global burden of disease 2010 study. Ann Rheumatic Diseases 2014;73(7):1323–30.
• [7] Tiulpin A, Saarakkala S, Automatic grading of individual knee osteoarthritis features in plain radiographs using deep convolutional neural networks, arXiv preprint arXiv:1907.08020; 2019.
• [8] Kawathekar PP, Karande KJ, Severity analysis of osteoarthritis of knee joint from x-ray images: A literature review. In :2014 International Conference on Signal propagation and computer technology (ICSPCT 2014), pp. 648–652, IEEE, 2014.
• [9] Kijowski R, Demehri S, Roemer F, Guermazi A. Osteoarthritis year in review 2019: imaging. Osteoarthritis Cartilage 2020;28(3):285–95.
• [10] Giger ML, Machine learning in medical imaging, J Am College Radiology, vol. 15, no. 3, Part B, pp. 512–520, 2018. Data Science: Big Data Machine Learning and Artificial Intelligence.
• [11] Barr AJ, Dube B, Hensor EM, Kingsbury SR, Peat G, Bowes MA, Sharples LD, Conaghan PG. The relationship between three-dimensional knee mri bone shape and total knee replacement—a case control study: data from the osteoarthritis initiative. Rheumatology 2016;55(9):1585–93.
• [12] Hayashi D, Roemer FW, Guermazi A. Magnetic resonance imaging assessment of knee osteoarthritis: current and developing new concepts and techniques. Clin Exp Rheumatol 2019;37(120):S88–95.
• [13] Hamai S, Dunbar NJ, Moro-oka T-A, Miura H, Iwamoto Y, Banks SA. Physiological sagittal plane patellar kinematics during dynamic deep knee flexion. Int Orthopaedics 2013;37 (8):1477–82.
• [14] Holzer L, Kraiger M, Talakic E, Fritz G, Avian A, Hofmeister A, et al. Microstructural analysis of subchondral bone in knee osteoarthritis. Osteoporosis Int 2020:1–9.
• [15] Majidi H, Niksolat F, Anbari K. Comparing the accuracy of radiography and sonography in detection of knee osteoarthritis: A diagnostic study. Open Access Macedonian J Med Sci 2019;7(23):4015.
• [16] Desai P, Hacihaliloglu I. Knee-cartilage segmentation and thickness measurement from 2d ultrasound. J Imaging 2019;5(4):43.
• [17] Lovrenovic Z, Doumit M. Development and testing of a passive walking assist exoskeleton. Biocybernetics Biomed Eng 2019;39(4):992–1004.
• [18] Aprovitola A, Gallo L. Knee bone segmentation from mri: A classification and literature review. Biocybernetics Biomed Eng 2016;36(2):437–49.
• [19] Saleem M, Farid MS, Saleem S, Khan MH. X-ray image analysis for automated knee osteoarthritis detection. Signal, Image Video Processing 2020:1–9.
• [20] Kellgren JH, Lawrence JS. Radiological assessment of osteoarthrosis. Ann Rheumatic Diseases 1957;16(4):494–502.
• [21] Antony AJ, Automatic quantification of radiographic knee osteoarthritis severity and associated diagnostic features using deep convolutional neural networks. PhD thesis, Dublin City University; 2018.
• [22] Chen P, Gao L, Shi X, Allen K, Yang L. Fully automatic knee osteoarthritis severity gradingusing deep neural networks with anovelordinalloss. Comput Med Imaging Graphics2019;75:06.
• [23] Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E, Bereket M, Patel BN, Yeom KW, Shpanskaya K, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet. PLoS Med. 2018;15(11) e1002699.
• [24] Culvenor AG, Øiestad BE, Hart HF, Stefanik JJ, Guermazi A, Crossley KM. Prevalence of knee osteoarthritis features on magnetic resonance imaging in asymptomatic uninjured adults: a systematic review and meta-analysis. British J Sports Med 2019;53(20):1268–78.
• [25] Nagai K, Nakamura T, Fu FH. The diagnosis of early osteoarthritis of the knee using magnetic resonance imaging. Ann Jt 2018;3:1–5.
• [26] Hossain MB, Pingguan-Murphy B, Chai HY, Salim MIM, Dewi DEO, Supriyanto E, Lai KW. Improved ultrasound imaging for knee osteoarthritis detection. In: Medical Imaging Technology. Springer; 2015. p. 1–40.
• [27] Saygılı A, Albayrak S. A new computer-based approach for fully automated segmentation of knee meniscus from magnetic resonance images. Biocybernetics Biomed Eng 2017;37(3):432–42.
• [28] Rath PD, Pandey SC. Ultrasound findings in knee of patients of osteoarthritis and their correlation with pain. Int J Res Orthopaedics 2019;5(5):944.
• [29] Cai G, Cicuttini F, Aitken D, Laslett LL, Zhu Z, Winzenberg T, Jones G. Comparison of radiographic and mri osteoarthritis definitions and their combination for prediction of tibial cartilage loss, knee symptoms and total knee replacement: a longitudinal study. Osteoarthritis and Cartilage 2020.
• [30] Liu B, Luo J, Huang H. Toward automatic quantification of knee osteoarthritis severity using improved faster r-cnn. Int J Computer Assisted Radiol Surgery 2020;15(3):457–66.
• [31] Yoo TK, Kim DW, Choi SB, Park JS. Simple scoring system and artificial neural network for knee osteoarthritis risk prediction: a cross-sectional study. PloS one 2016;11(2) e0148724.
• [32] Brahim A, Jennane R, Riad R, Janvier T, Khedher L, Toumi H, Lespessailles E. A decision support tool for early detection of knee osteoarthritis using x-ray imaging and machine learning: Data from the osteoarthritis initiative. Computerized Med Imaging Graphics 2019;73:11–8.
• [33] Charlesworth J, Fitzpatrick J, Perera NKP, Orchard J. Osteoarthritis-a systematic review of long-term safety implications for osteoarthritis of the knee. BMC Musculoskeletal Disorders 2019;20(1):1–12.
• [34] Lespasio MJ, Piuzzi NS, Husni ME, Muschler GF, Guarino A, MontMA.Kneeosteoarthritis: aprimer. Permanente J 2017;21.
• [35] Hernández-Molina G, Neogi T, Hunter DJ, Niu J, Guermazi A, Reichenbach S, Roemer F, McLennan C, Felson DT. The association of bone attrition with knee pain and other mri features of osteoarthritis. Ann Rheumatic Diseases 2008;67 (1):43–7.
• [36] Raeissadat SA, Ghorbani E, Taheri MS, Soleimani R, Rayegani SM, Babaee M, Payami S. Mri changes after platelet rich plasma injection in knee osteoarthritis (randomized clinical trial). J Pain Res 2020;13:65.
• [37] Liu J, Chen L, Tu Y, Chen X, Hu K, Tu Y, Lin M, Xie G, Chen S, Huang J, et al. Different exercise modalities relieve pain syndrome in patients with knee osteoarthritis and modulate the dorsolateral prefrontal cortex: a multiple mode mri study. Brain, Behavior, Immunity 2019;82:253–63.
• [38] Kiselev J, Ziegler B, Schwalbe H-J, Franke R, Wolf U. Detection of osteoarthritis using acoustic emission analysis. Med Eng Phys 2019;65:57–60.
• [39] 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(7):1281.
• [40] ‘‘Oai.” https://nda.nih.gov/oai, 2020. (Accessed on 08/12/2020).
• [41] ‘‘Most dataset.” http://most.ucsf.edu/default.asp, 2019. (Accessed on 08/12/2019).
• [42] Shock NW, Normal human aging: The Baltimore longitudinal study of aging. US Department of Health and Human Services, Public Health Service, National; 1984.
• [43] ‘‘The baltimore longitudinal study of aging — national institute on aging.” https://www.nia.nih.gov/research/labs/blsa, 2020. (Accessed on 08/12/2020).
• [44] Marijnissen AC, Vincken KL, Vos PA, Saris D, Viergever M, Bijlsma J, Bartels L, Lafeber F. Knee images digital analysis (kida): a novel method to quantify individual radiographic features of knee osteoarthritis in detail. Osteoarthritis Cartilage 2008;16(2):234–43.
• [45] Podsiadlo P, Wolski M, Stachowiak G. Automated selection of trabecular bone regions in knee radiographs. Med Phys 2008;35(5):1870–83.
• [46] Yoshimura N, Muraki S, Oka H, Kawaguchi H, Nakamura K, Akune T. Cohort profile: research on osteoarthritis/osteoporosis against disability study. Int J Epidemiol 2010;39(4):988–95.
• [47] Woloszynski T, Podsiadlo P, Stachowiak G, Kurzynski M. A signature dissimilarity measure for trabecular bone texture in knee radiographs. Med Phys 2010;37(5):2030–42.
• [48] Subramoniam M, Rajini V, Statistical feature based classification of arthritis in knee x-ray images using local binary pattern. In: 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT), pp. 873–875, IEEE, 2013.
• [49] Hirvasniemi J, Thevenot J, Immonen V, Liikavainio T, Pulkkinen P, Jämsä T, Arokoski J, Saarakkala S. Quantification of differences in bone texture from plain radiographs in knees with and without osteoarthritis. Osteoarthritis Cartilage 2014;22(10):1724–31.
• [50] Subramoniam M, Barani S, Rajini M. A non-invasive computer aided diagnosis of osteoarthritis from digital x-ray images. Biomed Res 2015;26(4).
• [51] Gornale SS, Patravali PU, Manza RR. Detection of osteoarthritis using knee X-ray image analyses: a machine vision based approach. Int J Computer Appl 2016;1.
• [52] Oka H, Muraki S, Akune T, Mabuchi A, Suzuki T, Yoshida H, Yamamoto S, Nakamura K, Yoshimura N, Kawaguchi H. Fully automatic quantification of knee osteoarthritis severity on plain radiographs. Osteoarthritis Cartilage 2008;16(11):1300–6.
• [53] Shamir L, Ling SM, Scott Jr WW, Bos A, Orlov N, Macura TJ, Eckley DM, Ferrucci L, Goldberg IG. Knee x-ray image analysis method for automated detection of osteoarthritis. IEEE Trans Biomed Eng 2008;56(2):407–15.
• [54] 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):1307–12.
• [55] Anifah L, Purnama IKE, Hariadi M, Purnomo MH. Automatic segmentation of impaired joint space area for osteoarthritis knee on X-ray image using gabor filter based morphology process. IPTEK J Technol Sci 2011;22(3).
• [56] Thomson J, O’Neill T, Felson D, Cootes T. Automated shape and texture analysis for detection of osteoarthritis from radiographs of the knee. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2015. p. 127–34.
• [57] Antony J, McGuinness K, O’Connor NE, Moran K. Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE; 2016. p. 1195–200.
• [58] Antony J, McGuinness K, Moran K, O’Connor NE. Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks. In: International conference on machine learning and data mining in pattern recognition. Springer; 2017. p. 376–90.
• [59] Suresha S, Kidziński L, Halilaj E, Gold G, Delp S. Automated staging of knee osteoarthritis severity using deep neural networks. Osteoarthritis Cartilage 2018;26:S441.
• [60] Norman B, Pedoia V, Noworolski A, Link TM, Majumdar S. Applying densely connected convolutional neural networks for staging osteoarthritis severity from plain radiographs. J Digital Imaging 2019;32(3):471–7.
• [61] Brahim A, Riad R, Jennane R, Knee osteoarthritis detection using power spectral density: Data from the osteoarthritis initiative. In: Proceedings of International Conference on Computer Analysis of Images and Patterns, (Salerno, Italy), pp. 480–487, Springer, 2019.
• [62] Guan B, Liu F, Mizaian A, Demhri S, Neogi T, Guermazi A, Kijowski R. Deep learning approach to predict radiographic knee osteoarthritis progression. Osteoarthritis Cartilage 2019;27:S395–6.
• [63] Tiulpin A, Saarakkala S. Auomatic grading of individual knee osteoarthritis features in plain radiographs using deep convolutional neural networks. Osteoarthritis Cartilage 2020;28:S308.
• [64] Thomas KA, Kidziński Ł, Halilaj E, Fleming SL, Venkataraman GR, Oei EH, Gold GE, Delp SL. Automated classification of radiographic knee osteoarthritis severity using deep neural networks. Radiol Artificial Intell 2020;2(2) e190065.
• [65] Leung K, Zhang B, Tan J, Shen Y, Geras KJ, Babb JS, Cho K, Chang G, Deniz CM. Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: data from the osteoarthritis initiative. Radiology 2020. p. 192091.
• [66] Hügle M, Omoumi P, van Laar JM, Boedecker J, Hügle T. Applied machine learning and artificial intelligence in rheumatology. Rheumatol Adv Practice 2020;4(1). p. rkaa005.
• [67] Abedin J, Antony J, McGuinness K, Moran K, O’Connor NE, Rebholz-Schuhmann D, Newell J. Predicting knee osteoarthritis severity: comparative modeling based on patient’s data and plain x-ray images. Sci Rep 2019;9(1):1–11.
• [68] Breiman L. Random forests. Mach Lsearning 2001;45(1): 5–32.
• [69] Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM 2017;60(6):84–90.
• [70] Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T. Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia. p. 675–8.
• [71] Github - bvlc/caffe: Caffe: a fast open framework for deep learning. https://github.com/BVLC/caffe. (Accessed on 12/16/2020).
• [72] Simonyan K, Zisserman A, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, 2014.
• [73] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. p. 770–8.
• [74] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer; 2015. p. 234–41.
• [75] Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. p. 1492–500.
• [76] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A, Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015.
• [77] Hu J, Shen L, Sun G, Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141, 2018.
• [78] Chicco D. Siamese neural networks: An overview. Artificial Neural Networks 2020:73–94.
• [79] Li MD, Chang K, Bearce B, Chang CY, Huang AJ, Campbell JP, Brown JM, Singh P, Hoebel KV, Erdoğmuş D, et al. Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging. NPJ Digital Med. 2020;3(1):1–9.
• [80] ‘‘Siamese networks. line by line explanation for beginners—by krishna prasad — towards data science.” https://towardsdatascience.com/siamese-networks-line-byline-explanation-for-beginners-55b8be1d2fc6. (Accessed on 12/18/2020).
• [81] Starmans MP, van der Voort SR, Tovar JMC, Veenland JF, Klein S, Niessen WJ. Radiomics: data mining using quantitative medical image features. In: Handbook of Medical Image Computing and Computer Assisted Intervention. Elsevier; 2020. p. 429–56.
• [82] Duryea J, Li J, Peterfy C, Gordon C, Genant H. Trainable rulebased algorithm for the measurement of joint space width in digital radiographic images of the knee. Med Phys 2000;27 (3):580–91.
• [83] Tiulpin A, Thevenot J, Rahtu E, Saarakkala S, A novel method for automatic localization of joint area on knee plain radiographs. In: Scandinavian Conference on Image Analysis, pp. 290–301, Springer, 2017.
• [84] Bindushree R, Kubakaddi S, Urs N, Detection of knee osteoarthritis by measuring the joint space width in knee x ray images. Int J Electron Commun, ISSN, pp. 2321–5984, 2015.
• [85] Sharma N, Aggarwal LM. Automated medical image segmentation techniques. J Med. Phys/Assoc Med Phys India 2010;35(1):3.
• [86] Heye T, Merkle EM, Reiner CS, Davenport MS, Horvath JJ, Feuerlein S, Breault SR, Gall P, Bashir MR, Dale BM, et al. Reproducibility of dynamic contrast-enhanced mr imaging. part ii. comparison of intra-and interobserver variability with manual region of interest placement versus semiautomatic lesion segmentation and histogram analysis. Radiology 2013;266(3):812–21.
• [87] Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 2006;31(3):1116–28.
• [88] Parmar C, Velazquez ER, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, Mitra S, Shankar BU, Kikinis R, Haibe-Kains B, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PloS one 2014;9(7)e102107.
• [89] Cootes TF, Ionita MC, Lindner C, Sauer P, Robust and accurate shape model fitting using random forest regression voting. In: European Conference on Computer Vision, pp. 278–291, Springer, 2012.
• [90] Lindner C, Thiagarajah S, Wilkinson JM, Wallis GA, Cootes TF, arcOGEN Consortium, et al., ‘‘Fully automatic segmentation of the proximal femur using random forest regression voting,” IEEE transactions on medical imaging, vol. 32, no. 8, pp. 1462–1472, 2013.
• [91] Lee H-C, Lee J-S, Lin MC-J, Wu C-H, Sun Y-N. Automatic assessment of knee osteoarthritis parameters from two-dimensional x-ray image. First International Conference on Innovative Computing, Information and Control-Volume I (ICICIC’06), vol. 2. IEEE; 2006. p. 673–6.
• [92] Ebrahimkhani S, Jaward MH, Cicuttini FM, Dharmaratne A, Wang Y, de Herrera AGS, A review on segmentation of knee articular cartilage: from conventional methods towards deep learning, Artificial Intelligence in Medicine, p. 101851, 2020.
• [93] Tiulpin A, Klein S, Bierma-Zeinstra SM, Thevenot J, Rahtu E, van Meurs J, Oei EH, Saarakkala S. Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data. Sci Rep 2019;9(1):1–11.
• [94] Kwon SB, Han H-S, Lee MC, Kim HC, Ku Y, et al. Machine learning-based automatic classification of knee osteoarthritis severity using gait data and radiographic images. IEEE Access 2020;8:120597–603.
• [95] Jamshidi A, Leclercq M, Labbe A, Pelletier J-P, Abram F, Droit A, Martel-Pelletier J, Identification of the most important features of knee osteoarthritis structural progressors using machine learning methods, Therapeutic advances in musculoskeletal disease, vol. 12, p. 1759720X20933468, 2020.
• [96] Attur M, Krasnokutsky S, Zhou H, Samuels J, Chang G, Bencardino J, Rosenthal P, Rybak L, Huebner JL, Kraus VB, et al. The combination of an inflammatory peripheral blood gene expression and imaging biomarkers enhance prediction of radiographic progression in knee osteoarthritis. Arthritis Res. Therapy 2020;22(1):1–17.
• [97] Antony J, McGuinness K, Moran K, O’Connor NE. Feature learning to automatically assess radiographic knee osteoarthritis severity. In: Deep Learners and Deep Learner Descriptors for Medical Applications. Springer; 2020. p. 9–93.
• [98] ‘‘Google ai blog: Federated learning: Collaborative machine learning without centralized training data.” https://ai.googleblog.com/2017/04/federated-learning-collaborative.html. (Accessed on 12/14/2020).
• [99] Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 2020;37(3):50–60.
• [100] Adadi A, Berrada M. Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access 2018;6:52138–60.
• [101] Moustakidis S, Christodoulou E, Papageorgiou E, Kokkotis C, Papandrianos N, Tsaopoulos D. Application of machine intelligence for osteoarthritis classification: a classical implementation and a quantum perspective. Quantum Mach Intell 2019;1(3):73–86.
• [102] Farhi E, Neven H, Classification with quantum neural networks on near term processors, arXiv preprintarXiv:1802.06002, 2018.
• [103] Nielsen MA, Neural networks and deep learning, vol. 2018. Determination press San Francisco, CA, 2015.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).