Use of artificial intelligence in imaging in rheumatology – current status and future perspectives
Tóm tắt
Từ khóa
Tài liệu tham khảo
Aizenberg, 2019, Automatic quantification of tenosynovitis on MRI of the wrist in patients with early arthritis: a feasibility study, Eur Radiol, 29, 4477, 10.1007/s00330-018-5807-2
Yosinski J , Clune J , Nguyen A , et al . Understanding neural networks through deep visualization. arXiv. preprint arXiv 2015;150606579.
Kubassova, 2007, Fast and robust analysis of dynamic contrast enhanced MRI datasets, Med Image Comput Comput Assist Interv, 10, 261
Czaplicka, 2015, Automated assessment of synovitis in 0.2T magnetic resonance images of the wrist, Comput Biol Med, 67, 116, 10.1016/j.compbiomed.2015.10.009
Cupek, 2016, Automated assessment of joint synovitis activity from medical ultrasound and power Doppler examinations using image processing and machine learning methods, R, 5, 239
Cao, 2016, Toward quantitative assessment of rheumatoid arthritis using volumetric ultrasound, IEEE Trans Biomed Eng, 63, 449, 10.1109/TBME.2015.2463711
Hemalatha, 2019, Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning, Proc Inst Mech Eng H, 233, 657, 10.1177/0954411919845747
Andersen, 2019, Neural networks for automatic scoring of arthritis disease activity on ultrasound images, RMD Open, 5, 10.1136/rmdopen-2018-000891
Aizenberg, 2018, Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: a feasibility study, Magn. Reson. Med., 79, 1127, 10.1002/mrm.26712
Rohrbach, 2019, Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks, Computers & Electrical Engineering, 78, 472, 10.1016/j.compeleceng.2019.08.003
Ren J , Moaddel A , Hauge EM , et al . Automatic detection and localization of bone erosion in hand HR-pQCT. medical imaging 2019: computer-aided diagnosis. Int Soc Opt Photonics 2019:1095022.
Allander, 1989, Computerized assessment of radiological changes of the hand in rheumatic diseases, Scand J Rheumatol, 18, 291, 10.3109/03009748909095032
Huo, 2017, Automatic quantification of radiographic wrist joint space width of patients with rheumatoid arthritis, IEEE Trans Biomed Eng, 64, 2695, 10.1109/TBME.2017.2659223
Eckstein, 2016, A 20 years of progress and future of quantitative magnetic resonance imaging (qMRI) of cartilage and articular tissues—personal perspective, Semin Arthritis Rheum, 45, 639, 10.1016/j.semarthrit.2015.11.005
Prasoon, 2013, Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network, Med Image Comput Comput Assist Interv, 16, 246
Liu, 2018, Deep learning approach for evaluating knee Mr images: achieving high diagnostic performance for cartilage lesion detection, Radiology, 289, 160, 10.1148/radiol.2018172986
Brui E , Efimtcev AY , Fokin VA , et al . Deep learning-based fully automatic segmentation of wrist cartilage in Mr images. arXiv preprint arXiv 2018.
Anderson, 2005, Computerized nailfold video capillaroscopy-a new tool for assessment of Raynaud's phenomenon, J Rheumatol, 32, 841
Schaefer, 2013, Scleroderma capillary pattern identification using texture descriptors and ensemble classification, Conf Proc IEEE Eng Med Biol Soc, 2013, 5473
Zhai, 2019, Pulmonary vascular morphology associated with gas exchange in systemic sclerosis without lung fibrosis, J Thorac Imaging, 34, 373, 10.1097/RTI.0000000000000395
Zhai Z , Staring M , Zhou X , et al . Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation. medical image computing and computer assisted intervention. Shenzhen 2019.
Genovese, 2019, Machine Learning-Based three-dimensional echocardiographic quantification of right ventricular size and function: validation against cardiac magnetic resonance, J Am Soc Echocardiogr, 32, 969, 10.1016/j.echo.2019.04.001
Mao, 2014, Model-Based automatic segmentation algorithm accurately assesses the whole cardiac volumetric parameters in patients with cardiac CT angiography: a validation study for evaluating the accuracy of the workstation software and establishing the reference values, Acad Radiol, 21, 639, 10.1016/j.acra.2014.01.010
Kang, 2014, Skin imaging in systemic sclerosis, Eur J Rheumatol, 1, 111, 10.5152/eurjrheumatol.2014.036
Lagarde, 2005, Automatic measurement of dermal thickness from B-scan ultrasound images using active contours, Skin Res Technol, 11, 79, 10.1111/j.1600-0846.2005.00108.x
Sciolla, 2018, Joint segmentation and characterization of the dermis in 50 MHz ultrasound 2D and 3D images of the skin, Comput Biol Med, 103, 277, 10.1016/j.compbiomed.2018.10.029
Ognard, 2019, Edge detector-based automatic segmentation of the skin layers and application to moisturization in high-resolution 3 tesla magnetic resonance imaging, Skin Res Technol, 25, 339, 10.1111/srt.12654
Aizenberg, 2017, Computer-Aided evaluation of inflammatory changes over time on MRI of the spine in patients with suspected axial spondyloarthritis: a feasibility study, BMC Med Imaging, 17, 10.1186/s12880-017-0226-4
Ichikawa, 2017, Computer-Based radiographic quantification of joint space narrowing progression using sequential hand radiographs: validation study in rheumatoid arthritis patients from multiple institutions, J Digit Imaging, 30, 648, 10.1007/s10278-017-9970-9
Staring, 2014, Towards local progression estimation of pulmonary emphysema using CT, Med Phys, 41, 10.1118/1.4851535
Tiulpin, 2018, Automatic knee osteoarthritis diagnosis from plain radiographs: a deep Learning-Based approach, Sci Rep, 8, 10.1038/s41598-018-20132-7