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China","award":["NS2023002"],"award-info":[{"award-number":["NS2023002"]}]},{"name":"the National Natural Science Foundation of China","award":["2022YFC3602203"],"award-info":[{"award-number":["2022YFC3602203"]}]},{"name":"the Nanshan District Health Science and Technology Major Project","award":["A2023195"],"award-info":[{"award-number":["A2023195"]}]},{"name":"the Nanshan District Health Science and Technology Major Project","award":["82102640"],"award-info":[{"award-number":["82102640"]}]},{"name":"the Nanshan District Health Science and Technology Major Project","award":["NSZD2023023"],"award-info":[{"award-number":["NSZD2023023"]}]},{"name":"the Nanshan District Health Science and Technology Major Project","award":["NS2023044"],"award-info":[{"award-number":["NS2023044"]}]},{"name":"the Nanshan District Health Science and Technology Major Project","award":["NS2023002"],"award-info":[{"award-number":["NS2023002"]}]},{"name":"the Nanshan District Health Science and Technology Major Project","award":["2022YFC3602203"],"award-info":[{"award-number":["2022YFC3602203"]}]},{"name":"the Nanshan District Health Science and Technology Project","award":["A2023195"],"award-info":[{"award-number":["A2023195"]}]},{"name":"the Nanshan District Health Science and Technology Project","award":["82102640"],"award-info":[{"award-number":["82102640"]}]},{"name":"the Nanshan District Health Science and Technology Project","award":["NSZD2023023"],"award-info":[{"award-number":["NSZD2023023"]}]},{"name":"the Nanshan District Health Science and Technology Project","award":["NS2023044"],"award-info":[{"award-number":["NS2023044"]}]},{"name":"the Nanshan District Health Science and Technology Project","award":["NS2023002"],"award-info":[{"award-number":["NS2023002"]}]},{"name":"the Nanshan District Health Science and Technology Project","award":["2022YFC3602203"],"award-info":[{"award-number":["2022YFC3602203"]}]},{"name":"the National Key Research and Development Program of China","award":["A2023195"],"award-info":[{"award-number":["A2023195"]}]},{"name":"the National Key Research and Development Program of China","award":["82102640"],"award-info":[{"award-number":["82102640"]}]},{"name":"the National Key Research and Development Program of China","award":["NSZD2023023"],"award-info":[{"award-number":["NSZD2023023"]}]},{"name":"the National Key Research and Development Program of China","award":["NS2023044"],"award-info":[{"award-number":["NS2023044"]}]},{"name":"the National Key Research and Development Program of China","award":["NS2023002"],"award-info":[{"award-number":["NS2023002"]}]},{"name":"the National Key Research and Development Program of China","award":["2022YFC3602203"],"award-info":[{"award-number":["2022YFC3602203"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients. Methods: A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision\u2013recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not. Results: The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis. Conclusions: ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.<\/jats:p>","DOI":"10.3390\/diagnostics14010053","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T05:19:07Z","timestamp":1703567947000},"page":"53","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography"],"prefix":"10.3390","volume":"14","author":[{"given":"Guoxin","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China"},{"name":"Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5143-3947","authenticated-orcid":false,"given":"Dongdong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics, Putuo People\u2019s Hospital, Tongji University, Shanghai 200060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufeng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Sports Medicine, Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Guangzhou 510700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1001\/jama.2022.5921","article-title":"Diagnosis and Management of Lumbar Spinal Stenosis: A Review","volume":"327","author":"Katz","year":"2022","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1038\/nrneurol.2009.90","article-title":"Lumbar spinal stenosis: Syndrome, diagnostics and treatment","volume":"5","author":"Siebert","year":"2009","journal-title":"Nat. Rev. Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1097\/01.brs.0000166503.37969.8a","article-title":"United States trends in lumbar fusion surgery for degenerative conditions","volume":"30","author":"Deyo","year":"2005","journal-title":"Spine"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1001\/jama.2010.338","article-title":"Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults","volume":"303","author":"Deyo","year":"2010","journal-title":"JAMA"},{"key":"ref_5","first-page":"49","article-title":"Percutaneous Endoscopic Lumbar Discectomy: Indications and Complications","volume":"23","author":"Pan","year":"2020","journal-title":"Pain Physician"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1038\/nrneurol.2012.110","article-title":"Paradigm changes in spine surgery: Evolution of minimally invasive techniques","volume":"8","author":"Smith","year":"2012","journal-title":"Nat. Rev. Neurol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.ijsu.2020.11.014","article-title":"Management for lumbar spinal stenosis: A network meta-analysis and systematic review","volume":"85","author":"Wei","year":"2021","journal-title":"Int. J. Surg."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.future.2018.10.009","article-title":"Optimal deep learning model for classification of lung cancer on CT images","volume":"92","author":"Lakshmanaprabu","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105487","DOI":"10.1016\/j.asoc.2019.105487","article-title":"Online clinical decision support system using optimal deep neural networks","volume":"81","author":"Lakshmanaprabu","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e262","DOI":"10.1016\/S1470-2045(19)30149-4","article-title":"Big data and machine learning algorithms for health-care delivery","volume":"20","author":"Ngiam","year":"2019","journal-title":"Lancet Oncol."},{"key":"ref_11","first-page":"1586","article-title":"Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study","volume":"40","author":"Gaonkar","year":"2019","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ejrad.2019.02.023","article-title":"Qualitative versus quantitative lumbar spinal stenosis grading by machine learning supported texture analysis-Experience from the LSOS study cohort","volume":"114","author":"Huber","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1148\/radiol.2021204289","article-title":"Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI","volume":"300","author":"Hallinan","year":"2021","journal-title":"Radiology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1097\/BRS.0000000000003377","article-title":"Spinal Stenosis Grading in Magnetic Resonance Imaging Using Deep Convolutional Neural Networks","volume":"45","author":"Won","year":"2020","journal-title":"Spine"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3435","DOI":"10.1007\/s00330-023-09483-6","article-title":"Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI","volume":"33","author":"Bharadwaj","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s12021-018-9365-1","article-title":"Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning","volume":"16","author":"Han","year":"2018","journal-title":"Neuroinformatics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"E180","DOI":"10.1097\/BSD.0000000000001418","article-title":"LSS-VGG16: Diagnosis of Lumbar Spinal Stenosis With Deep Learning","volume":"36","author":"Altun","year":"2023","journal-title":"Clin. Spine Surg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"E5","DOI":"10.3171\/2019.2.FOCUS18723","article-title":"Machine learning-based preoperative predictive analytics for lumbar spinal stenosis","volume":"46","author":"Siccoli","year":"2019","journal-title":"Neurosurg. Focus"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.spinee.2020.10.026","article-title":"Development of prediction models for clinically meaningful improvement in PROMIS scores after lumbar decompression","volume":"21","author":"Karhade","year":"2021","journal-title":"Spine J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1007\/s00256-018-2919-3","article-title":"Texture analysis of paraspinal musculature in MRI of the lumbar spine: Analysis of the lumbar stenosis outcome study (LSOS) data","volume":"47","author":"Mannil","year":"2018","journal-title":"Skelet. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s00330-018-5552-6","article-title":"Correlation of texture analysis of paraspinal musculature on MRI with different clinical endpoints: Lumbar Stenosis Outcome Study (LSOS)","volume":"29","author":"Mannil","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1016\/j.spinee.2022.06.008","article-title":"Development and validation of machine learning-based predictive model for clinical outcome of decompression surgery for lumbar spinal canal stenosis","volume":"22","author":"Yagi","year":"2022","journal-title":"Spine J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/j.spinee.2023.01.023","article-title":"A decision tree analysis to predict clinical outcome of minimally invasive lumbar decompression surgery for lumbar spinal stenosis with and without coexisting spondylolisthesis and scoliosis","volume":"23","author":"Toyoda","year":"2023","journal-title":"Spine J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.spinee.2019.10.007","article-title":"Can a machine learning model accurately predict patient resource utilization following lumbar spinal fusion?","volume":"20","author":"Karnuta","year":"2020","journal-title":"Spine J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1007\/s00586-019-05928-z","article-title":"Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods","volume":"28","author":"Ogink","year":"2019","journal-title":"Eur. Spine J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1016\/j.spinee.2019.12.019","article-title":"Predicting prolonged opioid prescriptions in opioid-na\u00efve lumbar spine surgery patients","volume":"20","author":"Karhade","year":"2020","journal-title":"Spine J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Saravi, B., Zink, A., \u00dclk\u00fcmen, S., Couillard-Despres, S., Hassel, F., and Lang, G. (2022). Performance of Artificial Intelligence-Based Algorithms to Predict Prolonged Length of Stay after Lumbar Decompression Surgery. J. Clin. Med., 11.","DOI":"10.3390\/jcm11144050"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1093\/neuros\/nyab085","article-title":"Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning","volume":"89","author":"Wilson","year":"2021","journal-title":"Neurosurgery"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"De Barros, A., Abel, F., Kolisnyk, S., Geraci, G.C., Hill, F., Engrav, M., Samavedi, S., Suldina, O., Kim, J., and Rusakov, A. (2023). Determining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning. Glob. Spine J., 21925682231155844.","DOI":"10.1177\/21925682231155844"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"891056","DOI":"10.3389\/fonc.2022.891056","article-title":"Commentary: Radiomics in oncology: A 10-year bibliometric analysis","volume":"12","author":"Fan","year":"2022","journal-title":"Front. Oncol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e185","DOI":"10.1002\/mp.13678","article-title":"Machine and deep learning methods for radiomics","volume":"47","author":"Avanzo","year":"2020","journal-title":"Med. Phys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jujjavarapu, C., Suri, P., Pejaver, V., Friedly, J., Gold, L.S., Meier, E., Cohen, T., Mooney, S.D., Heagerty, P.J., and Jarvik, J.G. (2023). Predicting decompression surgery by applying multimodal deep learning to patients\u2019 structured and unstructured health data. BMC Med. Inf. Decis. Mak., 23.","DOI":"10.1186\/s12911-022-02096-x"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s00256-020-03505-w","article-title":"Accurate prediction of lumbar microdecompression level with an automated MRI grading system","volume":"50","author":"Roller","year":"2021","journal-title":"Skelet. Radiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","article-title":"3D Slicer as an image computing platform for the Quantitative Imaging Network","volume":"30","author":"Fedorov","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3174\/ajnr.A6070","article-title":"Deep Learning-Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study","volume":"40","author":"Fan","year":"2019","journal-title":"AJNR. Am. J. Neuroradiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1302\/2058-5241.1.000030","article-title":"Management of degenerative lumbar spinal stenosis: An evidence-based review","volume":"1","author":"Covaro","year":"2016","journal-title":"EFORT Open Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3389\/fsurg.2016.00019","article-title":"Outcome after Surgery of Lumbar Spinal Stenosis: A Randomized Comparison of Bilateral Laminotomy, Trumpet Laminectomy, and Conventional Laminectomy","volume":"3","author":"Haddadi","year":"2016","journal-title":"Front. Surg."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e235","DOI":"10.1016\/j.wneu.2017.12.033","article-title":"Comparison of Single-Level and Multilevel Decompressive Laminectomy for Multilevel Lumbar Spinal Stenosis","volume":"111","author":"Adilay","year":"2018","journal-title":"World Neurosurg."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1097\/BRS.0000000000004447","article-title":"Multilevel Decompression Surgery for Degenerative Lumbar Spinal Canal Stenosis Is Similarly Effective With Single-level Decompression Surgery","volume":"47","author":"Yamamoto","year":"2022","journal-title":"Spine"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7214129","DOI":"10.1155\/2019\/7214129","article-title":"Microdecompression versus Open Laminectomy and Posterior Stabilization for Multilevel Lumbar Spine Stenosis: A Randomized Controlled Trial","volume":"2019","author":"Hamawandi","year":"2019","journal-title":"Pain Res. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"h1603","DOI":"10.1136\/bmj.h1603","article-title":"Minimally invasive decompression versus open laminectomy for central stenosis of the lumbar spine: Pragmatic comparative effectiveness study","volume":"350","author":"Nerland","year":"2015","journal-title":"BMJ"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1007\/s40122-021-00293-6","article-title":"Decompression Using Minimally Invasive Surgery for Lumbar Spinal Stenosis Associated with Degenerative Spondylolisthesis: A Review","volume":"10","author":"Zhang","year":"2021","journal-title":"Pain Ther."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"488","DOI":"10.22603\/ssrr.2021-0200","article-title":"Long-Term Outcomes after Selective Microendoscopic Laminotomy for Multilevel Lumbar Spinal Stenosis with and without Remaining Radiographic Stenosis: A 10-Year Follow-Up Study","volume":"6","author":"Murata","year":"2022","journal-title":"Spine Surg. Relat. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.2214\/ajr.158.5.1533084","article-title":"Diagnosis of lumbar spinal stenosis in adults: A metaanalysis of the accuracy of CT, MR, and myelography","volume":"158","author":"Kent","year":"1992","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1097\/BSD.0b013e3181d4c993","article-title":"Comparison between MRI and myelography in lumbar spinal canal stenosis for the decision of levels of decompression surgery","volume":"24","author":"Morita","year":"2011","journal-title":"J. Spinal Disord. Tech."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"S77","DOI":"10.1007\/s00590-013-1209-y","article-title":"Can recumbent magnetic resonance imaging replace myelography or computed tomography myelography for detecting lumbar spinal stenosis?","volume":"23","author":"Sasaki","year":"2013","journal-title":"Eur. J. Orthop. Surg. Traumatol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"100073","DOI":"10.1016\/j.wnsx.2020.100073","article-title":"Natural Course and Diagnosis of Lumbar Spinal Stenosis: WFNS Spine Committee Recommendations","volume":"7","author":"Zileli","year":"2020","journal-title":"World Neurosurg. X"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"788","DOI":"10.14444\/8101","article-title":"Resurgence of the Role of Computed-Tomography Myelogram in the Era of MRI for Surgical Decision-Making of Lumbar Degenerative Disc Disorder","volume":"15","author":"Akbari","year":"2021","journal-title":"Int. J. Spine Surg."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.spinee.2021.11.016","article-title":"When does CT-Myelography add value beyond MRI for lumbar degenerative disease?","volume":"22","author":"Weisenthal","year":"2021","journal-title":"Spine J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1007\/s00586-019-06287-5","article-title":"The normal appearance of CT myelograms","volume":"29","author":"Rocos","year":"2020","journal-title":"Eur. Spine J."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"792760","DOI":"10.3389\/fendo.2021.792760","article-title":"Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT","volume":"12","author":"Dieckmeyer","year":"2021","journal-title":"Front. Endocrinol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2176","DOI":"10.1007\/s00586-020-06613-2","article-title":"Artificial intelligence facilitates decision-making in the treatment of lumbar disc herniations","volume":"30","author":"Wirries","year":"2021","journal-title":"Eur. Spine J."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/1\/53\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:42:12Z","timestamp":1760132532000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/1\/53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["diagnostics14010053"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14010053","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,26]]}}}