{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T04:46:00Z","timestamp":1764996360257,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:00:00Z","timestamp":1634688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Patients with back pain are common and present a challenge in everyday medical practice due to the multitude of possible causes and the individual effects of treatments. Predicting causes and therapy efficien cy with the help of artificial intelligence could improve and simplify the treatment. In an exemplary collective of 1000 conservatively treated back pain patients, it was investigated whether the prediction of therapy efficiency and the underlying diagnosis is possible by combining different artificial intelligence approaches. For this purpose, supervised and unsupervised artificial intelligence methods were analyzed and a methodology for combining the predictions was developed. Supervised AI is suitable for predicting therapy efficiency at the borderline of minimal clinical difference. Non-supervised AI can show patterns in the dataset. We can show that the identification of the underlying diagnostic groups only becomes possible through a combination of different AI approaches and the baseline data. The presented methodology for the combined application of artificial intelligence algorithms shows a transferable path to establish correlations in heterogeneous data sets when individual AI approaches only provide weak results.<\/jats:p>","DOI":"10.3390\/diagnostics11111934","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T03:01:13Z","timestamp":1634698873000},"page":"1934","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain\u2014A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2052-9206","authenticated-orcid":false,"given":"Andr\u00e9","family":"Wirries","sequence":"first","affiliation":[{"name":"Spine Center, Hessing Foundation, Hessingstrasse 17, 86199 Augsburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian","family":"Geiger","sequence":"additional","affiliation":[{"name":"Spine Center, Hessing Foundation, Hessingstrasse 17, 86199 Augsburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Hammad","sequence":"additional","affiliation":[{"name":"Spine Center, Hessing Foundation, Hessingstrasse 17, 86199 Augsburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Redder","sequence":"additional","affiliation":[{"name":"Spine Center, Hessing Foundation, Hessingstrasse 17, 86199 Augsburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9239-5182","authenticated-orcid":false,"given":"Ludwig","family":"Oberkircher","sequence":"additional","affiliation":[{"name":"Center for Orthopaedics and Trauma Surgery, Philipps University of Marburg, Baldingerstrasse, 35043 Marburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steffen","family":"Ruchholtz","sequence":"additional","affiliation":[{"name":"Center for Orthopaedics and Trauma Surgery, Philipps University of Marburg, Baldingerstrasse, 35043 Marburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ingmar","family":"Bluemcke","sequence":"additional","affiliation":[{"name":"Neuropathological Institute, University Hospitals Erlangen, Schwabachanlage 6, 91054 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samir","family":"Jabari","sequence":"additional","affiliation":[{"name":"Neuropathological Institute, University Hospitals Erlangen, Schwabachanlage 6, 91054 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"811","DOI":"10.3174\/ajnr.A4173","article-title":"Systematic literature review of imaging features of spinal degeneration in asymptomatic populations","volume":"36","author":"Brinjikji","year":"2015","journal-title":"Am. J. Neuroradiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1177\/2192568220973984","article-title":"Intelligence-Based Spine Care Model: A New Era of Research and Clinical Decision-Making","volume":"11","author":"Mallow","year":"2021","journal-title":"Glob. Spine J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Harada, G.K., Siyaji, Z.K., Mallow, G.M., Hornung, A.L., Hassan, F., Basques, B.A., Mohammed, H.A., Sayari, A.J., Samartzis, D., and An, H.S. (2021). Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: Development of the \u201cRAD\u201d risk profile. Eur. Spine J.","DOI":"10.1007\/s00586-021-06866-5"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Durand, W.M., Lafage, R., Hamilton, D.K., Passias, P.G., Kim, H.J., Protopsaltis, T., Lafage, V., Smith, J.S., Shaffrey, C., and Gupta, M. (2021). Artificial intelligence clustering of adult spinal deformity sagittal plane morphology predicts surgical characteristics, alignment, and outcomes. Eur. Spine J.","DOI":"10.1007\/s00586-021-06799-z"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1007\/s00586-020-06447-y","article-title":"Using artificial intelligence algorithms to identify existing knowledge within the back pain literature","volume":"29","author":"Kawchuk","year":"2020","journal-title":"Eur. Spine J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.wneu.2020.04.022","article-title":"Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care","volume":"140","author":"Khan","year":"2020","journal-title":"World Neurosurg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s00586-004-0815-0","article-title":"Development of a German version of the Oswestry Disability Index. Part 1: Cross-cultural adaptation, reliability, and validity","volume":"15","author":"Mannion","year":"2006","journal-title":"Eur. Spine J."},{"key":"ref_8","first-page":"1","article-title":"Pandas development Pandas-dev\/pandas: Pandas","volume":"21","author":"Team","year":"2020","journal-title":"Zenodo"},{"key":"ref_9","unstructured":"Van Rossum, G., and Drake, F.L. (1995). Python reference manual. Centrum voor Wiskunde en Informatica. Amsterdam, The Netherlands. Python Reference Manual, CWI (Centre for Mathematics and Computer Science)."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"Seaborn: Statistical data visualization","volume":"6","author":"Waskom","year":"2021","journal-title":"J. Open Source Softw."},{"key":"ref_12","unstructured":"Chollet, F.K. (2021, August 08). GitHub Repository. Available online: https:\/\/github.com\/fchollet\/keras-resources."},{"key":"ref_13","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"205","DOI":"10.21105\/joss.00205","article-title":"hdbscan: Hierarchical density based clustering","volume":"2","author":"McInnes","year":"2017","journal-title":"J. Open Sour. Softw."},{"key":"ref_16","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s00586-004-0816-z","article-title":"Development of a German version of the Oswestry Disability Index. Part 2: Sensitivity to change after spinal surgery","volume":"15","author":"Mannion","year":"2006","journal-title":"Eur. Spine J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.jbspin.2019.09.001","article-title":"Big Data and artificial intelligence: Will they change our practice?","volume":"87","author":"Kedra","year":"2020","journal-title":"Jt. Bone Spine"},{"key":"ref_19","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":"2020","journal-title":"Eur. Spine J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e1044","DOI":"10.1002\/jsp2.1044","article-title":"Artificial intelligence and machine learning in spine research","volume":"2","author":"Galbusera","year":"2019","journal-title":"JOR Spine"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1016\/j.spinee.2016.06.010","article-title":"Determination of the Oswestry Disability Index score equivalent to a \u201csatisfactory symptom state\u201d in patients undergoing surgery for degenerative disorders of the lumbar spine\u2014A Spine Tango registry-based study","volume":"16","author":"Mannion","year":"2016","journal-title":"Spine J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"E448","DOI":"10.1097\/01.brs.0000222054.89431.42","article-title":"Cross-cultural adaptation of a German version of the Oswestry Disability Index and evaluation of its measurement properties","volume":"31","author":"Osthus","year":"2006","journal-title":"Spine (Phila Pa 1976)"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1097\/BRS.0000000000002974","article-title":"Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: Towards a new classification scheme that predicts quality and value","volume":"44","author":"Ames","year":"2019","journal-title":"Spine"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/11\/1934\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:18:32Z","timestamp":1760167112000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/11\/1934"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,20]]},"references-count":24,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["diagnostics11111934"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics11111934","relation":{},"ISSN":["2075-4418"],"issn-type":[{"type":"electronic","value":"2075-4418"}],"subject":[],"published":{"date-parts":[[2021,10,20]]}}}