Machine Learning in Orthopedics: A Literature Review
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abraham, 2011, Prevalence of electronic health records in us hospitals, J. Healthcare Eng., 2, 121, 10.1260/2040-2295.2.2.121
Adankon, 2012, Non invasive classification system of scoliosis curve types using least-squares support vector machines, Artif. Intell. Med., 56, 99, 10.1016/j.artmed.2012.07.002
Ahmed, 2016, Protein oxidation, nitration and glycation biomarkers for early-stage diagnosis of osteoarthritis of the knee and typing and progression of arthritic disease, Arthr. Res. Ther., 18, 250, 10.1186/s13075-016-1154-3
Akben, 2016, Importance of the shape and orientation of the spine and pelvis for the vertebral column pathologies diagnosis with using machine learning methods, Biomed. Res., S337
Al-Helo, 2013, Compression fracture diagnosis in lumbar: a clinical CAD system, Int. J. Comput. Assist. Radiol. Surg., 8, 461, 10.1007/s11548-012-0796-0
Alaqtash, 2011, Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2011, 453, 10.1109/IEMBS.2011.6090063
Ashinsky, 2017, Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative, J. Orthop. Res., 35, 2243, 10.1002/jor.23519
Ashinsky, 2015, Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging, Osteoarthr. Cartil., 23, 1704, 10.1016/j.joca.2015.05.028
Atkinson, 2012, Assessing fracture risk using gradient boosting machine (GBM) models, J. Bone Min. Res., 27, 1397, 10.1002/jbmr.1577
Baka, 2017, Random forest-based bone segmentation in ultrasound, Ultr. Med. Biol., 43, 2426, 10.1016/j.ultrasmedbio.2017.04.022
Bar, 2017, Compression fractures detection on ct, Medical Imaging 2017: Computer-Aided Diagnosis
Beauchamp, 1984, Applications of Walsh and Related Functions: With an Introduction to Sequency Theory
Bejnordi, 2017, Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer, JAMA, 318, 2199, 10.1001/jama.2017.14585
Bellamy, 1988, Validation study of womac: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee, J. Rheumatol., 15, 1833
Ben-Hur, 2008, Support vector machines and kernels for computational biology, PLoS Comput. Biol., 4, e1000173, 10.1371/journal.pcbi.1000173
Berg, 2017, Will intelligent machine learning revolutionize orthopedic imaging?, Acta Orthopaed., 88, 577, 10.1080/17453674.2017.1387732
Boulesteix, 2012, Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics, Wiley Interdisc. Rev., 2, 493, 10.1002/widm.1072
Brynjolfsson, 2017, What can machine learning do? workforce implications, Science, 358, 1530, 10.1126/science.aap8062
Cabitza, 2017, Machine learning in laboratory medicine: waiting for the flood?, Clin. Chem. Lab. Med, 56, 516, 10.1515/cclm-2017-0287
Cabitza, , A giant with feet of clay: on the validity of the data that feed machine learning in medicine, 10.1007/978-3-319-90503-7_10
Cabitza, , Unintended consequences of machine learning in medicine, JAMA, 318, 517, 10.1001/jama.2017.7797
Caruana, 2015, Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1721, 10.1145/2783258.2788613
Chen, 2017, Machine learning and prediction in medicine–beyond the peak of inflated expectations, N. Engl. J. Med., 376, 2507, 10.1056/NEJMp1702071
Cohen, 2016, Stard 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration, BMJ Open, 6, e012799, 10.1136/bmjopen-2016-012799
Cunha, 2014, Impact of ensemble learning in the assessment of skeletal maturity, J. Med. Sys., 38, 87, 10.1007/s10916-014-0087-0
Daenzer, 2014, VolHOG: a volumetric object recognition approach based on bivariate histograms of oriented gradients for vertebra detection in cervical spine MRI, Med. Phys., 41, 082305, 10.1118/1.4890587
Dam, 2015, Automatic segmentation of high-and low-field knee mris using knee image quantification with data from the osteoarthritis initiative, J. Med. Imaging, 2, 024001, 10.1117/1.JMI.2.2.024001
Dellacasa Bellingegni, 2017, NLR, MLP, SVM, and LDA: a comparative analysis on EMG data from people with trans-radial amputation, J. Neuroeng. Rehabilit., 14, 82, 10.1186/s12984-017-0290-6
Diciotti, 2013, The “peeking” effect in supervised feature selection on diffusion tensor imaging data, Am. J. Neuroradiol., 34, E107, 10.3174/ajnr.A3685
Dolatabadi, 2017, An automated classification of pathological gait using unobtrusive sensing technology, IEEE Trans. Neural. Syst. Rehabil. Eng, 25, 2336, 10.1109/TNSRE.2017.2736939
Dutta, 2011, Sensor-fault tolerant control of a powered lower limb prosthesis by mixing mode-specific adaptive Kalman filters, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2011, 3696, 10.1109/IEMBS.2011.6090626
Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056
Fernández-Delgado, 2014, Do we need hundreds of classifiers to solve real world classification problems, J. Mach. Learn. Res., 15, 3133
Forsberg, 2017, Detection and abeling of vertebrae in MR images using deep learning with clinical annotations as training data, J. Digit Imaging, 30, 406, 10.1007/s10278-017-9945-x
Giordano, 2016, Modeling skeletal bone development with hidden Markov models, Comput. Methods Prog. Biomed., 124, 138, 10.1016/j.cmpb.2015.10.012
Groopman, 2007, How Doctors Think
Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 2402, 10.1001/jama.2016.17216
Hainc, 2017, The bright, artificial intelligence-augmented future of neuroimaging reading, Front. Neurol., 8, 489, 10.3389/fneur.2017.00489
Hammond, 2001, Classifying vertical facial deformity using supervised and unsupervised learning, Methods Inform. Med., 40, 365, 10.1055/s-0038-1634194
Hetherington, 2017, SLIDE: automatic spine level identification system using a deep convolutional neural network, Int. J. Comput. Assist. Radiol. Surg., 12, 1189, 10.1007/s11548-017-1575-8
Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput., 18, 1527, 10.1162/neco.2006.18.7.1527
Islam, 2016, Gait state estimation for a powered ankle orthosis using modified fractional timing and artificialc neural network1, J. Med. Dev., 10, 020920, 10.1115/1.4033220
Jamaludin, 2017, ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist, Eur. Spine J., 26, 1374, 10.1007/s00586-017-4956-3
Jang, 2014, Computed tomographic image analysis based on FEM performance comparison of segmentation on knee joint reconstruction, Sci. World J., 2014, 235858, 10.1155/2014/235858
Jones, 2016, Gait comparison of unicompartmental and total knee arthroplasties with healthy controls, Bone Joint J., 16, 10.1302/0301-620X.98B10.BJJ.2016.0473.R1
JøRgensen, 2013, Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps, Comput. Biol. Med., 43, 1045, 10.1016/j.compbiomed.2013.05.012
Kadoury, 2017, 3-D morphology prediction of progressive spinal deformities from probabilistic modeling of discriminant manifolds, IEEE Trans. Med. Imaging, 36, 1194, 10.1109/TMI.2017.2657225
Kalanovic, 2000, Feedback error learning neural network for trans-femoral prosthesis, IEEE Trans. Rehabilit. Eng., 8, 71, 10.1109/86.830951
Karabulut, 2014, Effective automated prediction of vertebral column pathologies based on logistic model tree with SMOTE preprocessing, J. Med. Syst., 38, 50, 10.1007/s10916-014-0050-0
Kashyap, 2017, Industrial applications of machine learning, Machine Learning for Decision Makers, 189, 10.1007/978-1-4842-2988-0_5
Kellgren, 1957, Radiological assessment of osteo-arthrosis, Anna. Rheumat. Dis., 16, 494, 10.1136/ard.16.4.494
Kotti, 2017, Detecting knee osteoarthritis and its discriminating parameters using random forests, Med. Eng. Phys., 43, 19, 10.1016/j.medengphy.2017.02.004
Kreif, 2016, Evaluating treatment effectiveness under model misspecification: a comparison of targeted maximum likelihood estimation with bias-corrected matching, Stat. Methods Med. Res., 25, 2315, 10.1177/0962280214521341
Kruse, , Clinical fracture risk evaluated by hierarchical agglomerative clustering, Osteoporos Int., 28, 819, 10.1007/s00198-016-3828-8
Kruse, , Machine learning principles can improve hip fracture prediction, Calcif. Tiss. Int., 100, 348, 10.1007/s00223-017-0238-7
Laroche, 2014, A lassification study of kinematic gait trajectories in hip osteoarthritis, Comput. Biol. Med., 55, 42, 10.1016/j.compbiomed.2014.09.012
Lee, 2016, Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests, J. Rehabil. Res. Dev., 53, 1007, 10.1682/JRRD.2014.12.0319
Lemoyne, 2015, Implementation of machine learning for classifying prosthesis type through conventional gait analysis, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 10.1109/EMBC.2015.7318335
Li, 2016, Automatic Lumbar vertebrae detection based on feature fusion deep learning for partial occluded C-arm X-ray images, Conference of the IEEE Engineering in Medicine and Biology Society, 10.1109/EMBC.2016.7590785
Litjens, 2017, A survey on deep learning in medical image analysis, Med. Image Anal., 42, 60, 10.1016/j.media.2017.07.005
Liu, 2018, Deep convolutional neural network and 3d deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging, Magnet. Reson. Med., 79, 2379, 10.1002/mrm.26841
Madelin, 2015, Classification of sodium MRI data of cartilage using machine learning, Magn. Reson. Med., 74, 1435, 10.1002/mrm.25515
Mallikarjunaswamy, 2015, Knee joint menisci segmentation, visualization and Quantification using seeded region growing algorithm, J. Med. Imag. Health Informat., 5, 552, 10.1166/jmihi.2015.1435
Marcus, 2018, Deep learning: a critical appraisal
Marques, 2013, Diagnosis of osteoarthritis and prognosis of tibial cartilage loss by quantification of tibia trabecular bone from MRI, Magn. Reson. Med., 70, 568, 10.1002/mrm.24477
Marques, 2012, Automatic analysis of trabecular bone structure from knee MRI, Comput. Biol. Med., 42, 735, 10.1016/j.compbiomed.2012.04.005
Matić, 2016, Infrared assessment of knee instability in ACL deficient patients, Int. Orthopaed. (SICOT), 40, 385, 10.1007/s00264-015-2839-y
Miller, 2017, Artificial intelligence in medical practice: the question to the answer?, Am. J. Med., 131, 129, 10.1016/j.amjmed.2017.10.035
Mirzaalian, 2013, Fast and robust 3d vertebra segmentation using statistical shape models, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 3379, 10.1109/EMBC.2013.6610266
Mordenti, 2013, Validation of a new multiple osteochondromas classification through switching neural networks, Am. J. Med. Genet. A., 161, 556, 10.1002/ajmg.a.35819
Nagarajan, 2014, Computer-aided diagnosis for phase-contrast X-ray computed tomography: quantitative characterization of human patellar cartilage with high-dimensional geometric features, J. Digit. Imaging, 27, 98, 10.1007/s10278-013-9634-3
Nair, 2010, The pplication of machine learning algorithms to the analysis of electromyographic patterns from arthritic patients, IEEE Trans. Neural Sys. Rehabilit. Eng., 18, 174, 10.1109/TNSRE.2009.2032638
Nishiyama, 2014, Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis, Osteoporos Int., 25, 619, 10.1007/s00198-013-2459-6
Nowak, 2016, The LET procedure for prosthetic myocontrol: towards multi-DOF control using single-DOF activations, PLoS ONE, 11, e0161678, 10.1371/journal.pone.0161678
Obermeyer, 2016, Predicting the future-big data, machine learning, and clinical medicine, New Engl. J. Med., 375, 1216, 10.1056/NEJMp1606181
Obermeyer, 2017, Lost in thought—the limits of the human mind and the future of medicine, New Engl. J. Med., 377, 1209, 10.1056/NEJMp1705348
Oktay, 2011, Localization of the lumbar discs using machine learning and exact probabilistic inference, Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011, Lecture Notes in Computer Science, 158
Oktay, 2014, Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images, Comput. Med. Imaging Graph., 38, 613, 10.1016/j.compmedimag.2014.04.006
Olczak, 2017, Artificial intelligence for analyzing orthopedic trauma radiographs, Acta Orthop., 88, 581, 10.1080/17453674.2017.1344459
Pedoia, 2017, MRI and biomechanics multidimensional data analysis reveals R2 -R1r as an early predictor of cartilage lesion progression in knee osteoarthritis, J. Magn. Reson. Imaging, 47, 78, 10.1002/jmri.25750
Pesteie, 2015, Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features, Int. J. Comput. Assist. Radiol. Surg., 10, 901, 10.1007/s11548-015-1202-5
Phinyomark, 2016, Gender differences in gait kinematics for patients with knee osteoarthritis, BMC Musculoskelet Disord, 17, 157, 10.1186/s12891-016-1013-z
Pogorelc, 2010, Diagnosing health problems from gait patterns of elderly, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2010, 2238, 10.1109/IEMBS.2010.5627417
Prasoon, 2013, Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network, International Conference on Medical Image Computing and Computer-Assisted Intervention, 246
Pritzker, 2006, Osteoarthritis cartilage histopathology: grading and staging, Osteoarthritis Cartilage, 14, 13, 10.1016/j.joca.2005.07.014
Rajpurkar, 2017, Cardiologist-level arrhythmia detection with convolutional neural networks
Ramirez, 2008, A machine learning approach to assess changes in scoliosis, Stud. Health Technol. Informat., 140, 254
Ramirez, 2006, A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography, IEEE Trans. Informat. Technol. Biomed., 10, 84, 10.1109/TITB.2005.855526
Rampasek, 2016, Tensorflow: biology's gateway to deep learning?, Cell Sys., 2, 12, 10.1016/j.cels.2016.01.009
Schwarzenberg, 2014, Cube-Cut: vertebral body segmentation in MRI-Data through cubic-shaped divergences, PLoS ONE, 9, e93389, 10.1371/journal.pone.0093389
Shamir, 2008, Wndchrm–an open source utility for biological image analysis, Source Code Biol. Med., 3, 13, 10.1186/1751-0473-3-13
Silver, 2006, Using support vector machines to optimally classify rotator cuff strength data and quantify post-operative strength in rotator cuff tear patients, J. Biomech., 39, 973, 10.1016/j.jbiomech.2005.01.011
Smith, 2016, Introducing machine learning concepts with weka, Methods Mol. Bol., 1418, 353, 10.1007/978-1-4939-3578-9_17
Spampinato, 2017, Deep learning for automated skeletal bone age assessment in X-ray images, Med. Image Anal., 36, 41, 10.1016/j.media.2016.10.010
Stajduhar, 2017, Semi-automated detection of anterior cruciate ligament injury from MRI, Comp. Methods Prog. Biomed., 140, 151, 10.1016/j.cmpb.2016.12.006
Stankovski, 2001, Induction of hypotheses concerning hip arthroplasty: a modified methodology for medical research, Methods Inf. Med., 40, 392, 10.1055/s-0038-1634198
Steinhubl, 2018, Digital medicine, on its way to being just plain medicine, npj Dig. Med., 1, 1, 10.1038/s41746-017-0005-1
Thong, 2016, Three-dimensional morphology study of surgical adolescent idiopathic scoliosis patient from encoded geometric models, Eur. Spine J., 25, 3104, 10.1007/s00586-016-4426-3
Tomar, 2013, A survey on data mining approaches for healthcare, Int. J. Bio-Sci. Bio-Technol., 5, 241, 10.14257/ijbsbt.2013.5.5.25
Wang, 2017, A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks, Comput. Biol. Med., 84, 137, 10.1016/j.compbiomed.2017.03.024
Wolfswinkel, 2013, Using grounded theory as a method for rigorously reviewing literature, Eur. J. Informat. Syst., 22, 45, 10.1057/ejis.2011.51
Xue, 2017, A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis, PLoS ONE, 12, e0178992, 10.1371/journal.pone.0178992
Yoo, , Interpretation of movement during stair ascent for predicting severity and prognosis of knee osteoarthritis in elderly women using support vector machine, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 192
Yoo, , Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning, Yonsei Med. J., 54, 1321, 10.3349/ymj.2013.54.6.1321
Yu, 2014, Feature extraction and classification for ultrasound images of lumbar spine with support vector machine, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2014, 4659, 10.1109/EMBC.2014.6944663
Yu, 2015, Lumbar ultrasound image feature extraction and classification with support vector machine, Ultr. Med. Biol., 41, 2677, 10.1016/j.ultrasmedbio.2015.05.015
Zarychta, 2015, Features extraction in anterior and posterior cruciate ligaments analysis, Comput. Med. Imaging Graph., 46, 108, 10.1016/j.compmedimag.2015.03.001
Zhang, 2013, Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies, Magn. Reson. Imaging, 31, 1731, 10.1016/j.mri.2013.06.005