Artificial Intelligence for Ultrasound Informative Image Selection of Metacarpal Head Cartilage. A Pilot Study
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Fox, 2009, The basic science of articular cartilage: structure, composition, and function, Sports Health, 1, 461, 10.1177/1941738109350438
Pap, 2015, Cartilage damage in osteoarthritis and rheumatoid arthritis-two unequal siblings, Nat Rev Rheumatol, 11, 606, 10.1038/nrrheum.201595
Navarro-compán, 2014, Relationship between types of radiographic damage and disability in patients with rheumatoid arthritis in the EURIDISS cohort: a longitudinal study, Rheumatology, 54, 83, 10.1093/rheumatology/keu284
Mandl, 2015, Relationship between radiographic joint space narrowing, sonographic cartilage thickness and anatomy in rheumatoid arthritis and control joints, Ann Rheum Dis, 74, 2022, 10.1136/annrheumdis-2014-205585
Døhn, 2006, Are bone erosions detected by magnetic resonance imaging and ultrasonography true erosions? A comparison with computed tomography in rheumatoid arthritis metacarpophalangeal joints, Arthritis Res Ther, 8, R110, 10.1186/ar1995
Wakefield, 2000, The value of sonography in the detection of bone erosions in patients with rheumatoid arthritis: a comparison with conventional radiography, Arthritis Rheum, 43, 2762, 10.1002/1529-0131(200012)43:12<2762::AID-ANR16>3.0.CO;2
Szkudlarek, 2006, Ultrasonography of the metacarpophalangeal and proximal interphalangeal joints in rheumatoid arthritis: a comparison with magnetic resonance imaging, conventional radiography and clinical examination, Arthritis Res Ther, 8, R52, 10.1186/ar1904
Scheel, 2006, Prospective 7 year follow up imaging study comparing radiography, ultrasonography, and magnetic resonance imaging in rheumatoid arthritis finger joints, Ann Rheum Dis, 65, 595, 10.1136/ard.2005041814
Funck-Brentano, 2009, Benefits of ultrasonography in the management of early arthritis: a cross-sectional study of baseline data from the ESPOIR cohort, Rheumatology, 48, 1515, 10.1093/rheumatology/kep279
Wiell, 2007, Ultrasonography, magnetic resonance imaging, radiography, and clinical assessment of inflammatory and destructive changes in fingers and toes of patients with psoriatic arthritis, Arthritis Res Ther, 9, R119, 10.1186/ar2327
Hurnakova, 2019, Prevalence and distribution of cartilage damage at the metacarpal head level in rheumatoid arthritis and osteoarthritis: an ultrasound study, Rheumatology, 58, 1206, 10.1093/rheumatology/key443
Möller, 2009, Measuring finger joint cartilage by ultrasound as a promising alternative to conventional radiograph imaging, Arthritis Rheum, 61, 435, 10.1002/art24424
Filippucci, 2010, Interobserver reliability of ultrasonography in the assessment of cartilage damage in rheumatoid arthritis, Ann Rheum Dis, 69, 1845, 10.1136/ard.2009125179
Cipolletta, 2020, The reliability of ultrasound in the assessment of hyaline cartilage in rheumatoid and healthy metacarpal heads, Ultraschall Med, 10.1055/a-1285-4602
Iagnocco, 2012, The reliability of musculoskeletal ultrasound in the detection of cartilage abnormalities at the metacarpo-phalangeal joints, Osteoarthritis Cartilage, 20, 1142, 10.1016/j.joca.2012.07003
Cipolletta, 2019, FRI0634 standard reference values of metacarpal head cartilage thickness measurement by ultrasound in healthy subjects, Ann Rheum Dis, 78, 1014, 10.1136/annrheumdis-2019-eular5807
Cipolletta, 2020, Prevalence and distribution of cartilage and bone damage at metacarpal head in healthy subjects, Clin Exp Rheumatol
Hammer, 2016, Global ultrasound assessment of structural lesions in osteoarthritis: a reliability study by the OMERACT ultrasonography group on scoring cartilage and osteophytes in finger joints, Ann Rheum Dis, 75, 402, 10.1136/annrheumdis-2014-206289
Gutiérrez, 2013, Ultrasound learning curve in gout: a disease-oriented training program, Arthritis Care Res, 65, 1265, 10.1002/acr22009
Gutierrez, 2011, Inter-observer reliability of high-resolution ultrasonography in the assessment of bone erosions in patients with rheumatoid arthritis: experience of an intensive dedicated training programme, Rheumatology, 50, 373, 10.1093/rheumatology/keq320
Filippucci, 2007, E-learning in ultrasonography: a web-based approach, Ann Rheum Dis, 66, 962, 10.1136/ard.2006064568
Akkus, 2019, A survey of deep-learning applications in ultrasound: artificial intelligence–powered ultrasound for improving clinical workflow, J Am Coll Radiol, 16, 1318, 10.1016/j.jacr.2019.06004
Liu, 2019, Deep learning in medical ultrasound analysis: a review, Engineering, 5, 261, 10.1016/j.eng.2018.11020
Litjens, 2017, A survey on deep learning in medical image analysis, Med Image Anal, 42, 60, 10.1016/j.media.2017.07005
Stoel, 2020, Use of artificial intelligence in imaging in rheumatology – current status and future perspectives, RMD Open, 6, e001063, 10.1136/rmdopen-2019-001063
Zaffino, 2020, A review on advances in intra-operative imaging for surgery and therapy: imagining the operating room of the future, Ann Biomed Eng, 48, 2171, 10.1007/s10439-020-02553-6
Moccia, 2018, Blood vessel segmentation algorithms - review of methods, datasets and evaluation metrics, Comput Methods Programs Biomed, 158, 71, 10.1016/j.cmpb.2018.02001
Chang, 2016, Computer-aided diagnosis of different rotator cuff lesions using shoulder musculoskeletal ultrasound, Ultrasound Med Biol, 42, 2315, 10.1016/j.ultrasmedbio.2016.05016
Klauser, 2011, Contrast-enhanced ultrasonography for the detection of joint vascularity in arthritis - subjective grading versus computer-aided objective quantification, Ultraschall der Medizin, 32, E31, 10.1055/s-0031-1281671
Chang, 2019, Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition, PLoS ONE, 14, e0212741, 10.1371/journal.pone0212741
Andersen, 2019, Neural networks for automatic scoring of arthritis disease activity on ultrasound images, RMD Open, 5, e000891, 10.1136/rmdopen-2018-000891
Christensen, 2020, Applying cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients, Ann Rheum Dis, 79, 1189, 10.1136/annrheumdis-2019-216636
Fiorentino, 2019, A learning approach for informative-frame selection in US rheumatology images, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 228
Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056
Tajbakhsh, 2016, Convolutional neural networks for medical image analysis: full training or fine tuning?, IEEE Trans Med Imaging, 35, 1299, 10.1109/TMI.20162535302
Mandl, 2019, Development of semiquantitative ultrasound scoring system to assess cartilage in rheumatoid arthritis, Rheumatology, 58, 1802, 10.1093/rheumatology/kez153
Torp-Pedersen, 2010, Articular Cartilage thickness measured with US is not as easy as it appears: a systematic review of measurement techniques and image interpretation, Ultraschall der Medizin - Eur J Ultrasound, 32, 54, 10.1055/s-0029-1245386
Möller, 2017, The 2017 EULAR standardised procedures for ultrasound imaging in rheumatology, Ann Rheum Dis, 76, 1974, 10.1136/annrheumdis-2017-211585
Patrini, 2020, Transfer learning for informative-frame selection in laryngoscopic videos through learned features, Med Biol Eng Comput, 58, 1225, 10.1007/s11517-020-02127-7
Landis, 1977, The measurement of observer agreement for categorical data, Biometrics, 33, 159, 10.2307/2529310
Filippucci, 2019, Ultrasound imaging in rheumatoid arthritis, Radiol Med, 124, 1087, 10.1007/s11547-019-01002-2
Ruta, 2015, Pan-American League Against Rheumatisms (PANLAR) Ultrasound Study Group. General applications of ultrasound in rheumatology: why we need it in our daily practice, J Clin Rheumatol, 21, 133, 10.1097/RHU0000000000000230
Naredo, 2010, Current state of musculoskeletal ultrasound training and implementation in Europe: results of a survey of experts and scientific societies, Rheumatology, 49, 2438, 10.1093/rheumatology/keq243
Bruyn, 2019, OMERACT definitions for ultrasonographic pathologies and elementary lesions of rheumatic disorders 15 years on, J Rheumatol, 46, 1388, 10.3899/jrheum181095
Davis, 2020, Artificial intelligence and echocardiography: a primer for cardiac sonographers, J Am Soc Echocardiogr, 33, 1061, 10.1016/j.echo.2020.04025
Moccia, 2018, Learning-based classification of informative laryngoscopic frames, Comput Methods Programs Biomed, 158, 21, 10.1016/j.cmpb.2018.01030