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Syst."],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Lumbar disc herniation is a common disease that causes low back pain. Due to the high cost of medical diagnosis, as well as a shortage and uneven distribution of medical resources, a system that can automatically analyze and diagnose lumbar spine Magnetic Resonance Imaging (MRI) is becoming an urgent need. This study uses deep learning methods to establish a classifier to diagnose lumbar disc herniation. An MRI classification dataset of lumbar disc herniation consisting of public MRI images is presented and is used to train the proposed classifier. Because a common difficulty in applying computer vision technology to medical images is labeling training data, we use a semi-supervised model training method, while multilayer transverse axial MRI images are used as the model input. In this method, we first use unlabelled MRI images for random self-supervised pre-training and the pre-trained model as a feature extractor for MRI images. Then, all marked cross-sections of each intervertebral disc are used to calculate the feature vector through the feature extractor. The information of all feature vectors is integrated, while a multilayer perceptron is used for classification training. After training, the model achieved 87.11<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>%<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> accuracy, 87.50<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>%<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> sensitivity, 86.72<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>%<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> specificity and 0.9487 AUC (Area Under the ROC Curve) index on the test set. To analyze the rationality of the diagnostic results more quickly, we output the severity of degenerative changes in each region using a heatmap.<\/jats:p>","DOI":"10.1007\/s40747-023-00981-0","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T08:04:27Z","timestamp":1679645067000},"page":"5567-5584","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An MRI image automatic diagnosis model for lumbar disc herniation using semi-supervised learning"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0692-7328","authenticated-orcid":false,"given":"Chao","family":"Hou","sequence":"first","affiliation":[]},{"given":"Xiaogang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hongbo","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Weiqi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Defeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yuzhen","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,24]]},"reference":[{"key":"981_CR1","unstructured":"Waxenbaum JA, Reddy V, Futterman B (2017) Anatomy, back, intervertebral discs"},{"key":"981_CR2","unstructured":"National Institute of Neurological Disorders and Stroke (NINDS) (2008) Low back pain fact sheet, NIND brochure"},{"issue":"11","key":"981_CR3","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1016\/j.spinee.2014.04.022","volume":"14","author":"DF Fardon","year":"2014","unstructured":"Fardon DF, Williams AL, Dohring EJ, Reed Murtagh F, Gabriel Rothman SL, Sze GK (2014) Lumbar disc nomenclature: version 2.0: recommendations of the combined task forces of the north American spine society, the American society of spine radiology and the American society of neuroradiology. 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