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For: Panfilov E, Tiulpin A, Nieminen MT, Saarakkala S, Casula V. Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the Osteoarthritis Initiative. J Orthop Res 2022;40:1113-24. [PMID: 34324223 DOI: 10.1002/jor.25150] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Kijowski R, Fritz J, Deniz CM. Deep learning applications in osteoarthritis imaging. Skeletal Radiol 2023. [PMID: 36759367 DOI: 10.1007/s00256-023-04296-6] [Reference Citation Analysis]
2 Siripurapu S, Darimireddy NK, Chehri A, Sridhar B, Paramkusam AV. Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review-Part-I (AI, Big Data, Block Chain, Open-Source Technologies, and Cloud Computing). Electronics 2023;12:750. [DOI: 10.3390/electronics12030750] [Reference Citation Analysis]
3 Bonaldi L, Pretto A, Pirri C, Uccheddu F, Fontanella CG, Stecco C. Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies. Bioengineering (Basel) 2023;10. [PMID: 36829631 DOI: 10.3390/bioengineering10020137] [Reference Citation Analysis]
4 Hirvasniemi J, Runhaar J, van der Heijden RA, Zokaeinikoo M, Yang M, Li X, Tan J, Rajamohan HR, Zhou Y, Deniz CM, Caliva F, Iriondo C, Lee JJ, Liu F, Martinez AM, Namiri N, Pedoia V, Panfilov E, Bayramoglu N, Nguyen HH, Nieminen MT, Saarakkala S, Tiulpin A, Lin E, Li A, Li V, Dam EB, Chaudhari AS, Kijowski R, Bierma-Zeinstra S, Oei EHG, Klein S. The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images. Osteoarthritis Cartilage 2023;31:115-25. [PMID: 36243308 DOI: 10.1016/j.joca.2022.10.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
5 Xie D, Murray J, Lartey R, Gaj S, Kim J, Li M, Eck BL, Winalski CS, Altahawi F, Jones MH, Obuchowski NA, Huston LJ, Harkins KD, Friel HT, Damon BM, Knopp MV, Kaeding CC, Spindler KP, Li X. Multi-vendor multi-site quantitative MRI analysis of cartilage degeneration 10 Years after anterior cruciate ligament reconstruction: MOON-MRI protocol and preliminary results. Osteoarthritis Cartilage 2022;30:1647-57. [PMID: 36049665 DOI: 10.1016/j.joca.2022.08.006] [Reference Citation Analysis]
6 Tiulpin A, Saarakkala S, Mathiessen A, Hammer HB, Furnes O, Nordsletten L, Englund M, Magnusson K. Predicting total knee arthroplasty from ultrasonography using machine learning. Osteoarthritis and Cartilage Open 2022;4:100319. [DOI: 10.1016/j.ocarto.2022.100319] [Reference Citation Analysis]
7 Väärälä A, Casula V, Peuna A, Panfilov E, Mobasheri A, Haapea M, Lammentausta E, Nieminen MT. Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS: 6-Year data from osteoarthritis initiative. J Orthop Res 2022;40:2597-608. [PMID: 35152476 DOI: 10.1002/jor.25293] [Reference Citation Analysis]
8 Chadoulos CG, Tsaopoulos DE, Moustakidis S, Tsakiridis NL, Theocharis JB. A novel multi-atlas segmentation approach under the semi-supervised learning framework: Application to knee cartilage segmentation. Computer Methods and Programs in Biomedicine 2022. [DOI: 10.1016/j.cmpb.2022.107208] [Reference Citation Analysis]
9 Xiongfeng T, Yingzhi L, Xianyue S, Meng H, Bo C, Deming G, Yanguo Q. Automated detection of knee cystic lesions on magnetic resonance imaging using deep learning. Front Med 2022;9. [DOI: 10.3389/fmed.2022.928642] [Reference Citation Analysis]