{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T20:47:20Z","timestamp":1785444440872,"version":"3.56.0"},"reference-count":14,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,10]],"date-time":"2023-09-10T00:00:00Z","timestamp":1694304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Lumbar spine magnetic resonance imaging (MRI) is a critical diagnostic tool for the assessment of various spinal pathologies, including degenerative disc disease, spinal stenosis, and spondylolisthesis. The accurate identification and quantification of the dural sack cross-sectional area are essential for the evaluation of these conditions. Current manual measurement methods are time-consuming and prone to inter-observer variability. Our study developed and validated deep learning models, specifically U-Net, Attention U-Net, and MultiResUNet, for the automated detection and measurement of the dural sack area in lumbar spine MRI, using a dataset of 515 patients with symptomatic back pain and externally validating the results based on 50 patient scans. The U-Net model achieved an accuracy of 0.9990 and 0.9987 on the initial and external validation datasets, respectively. The Attention U-Net model reported an accuracy of 0.9992 and 0.9989, while the MultiResUNet model displayed a remarkable accuracy of 0.9996 and 0.9995, respectively. All models showed promising precision, recall, and F1-score metrics, along with reduced mean absolute errors compared to the ground truth manual method. In conclusion, our study demonstrates the potential of these deep learning models for the automated detection and measurement of the dural sack cross-sectional area in lumbar spine MRI. The proposed models achieve high-performance metrics in both the initial and external validation datasets, indicating their potential utility as valuable clinical tools for the evaluation of lumbar spine pathologies. Future studies with larger sample sizes and multicenter data are warranted to validate the generalizability of the model further and to explore the potential integration of this approach into routine clinical practice.<\/jats:p>","DOI":"10.3390\/bioengineering10091072","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T08:58:08Z","timestamp":1694422688000},"page":"1072","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automated Detection and Measurement of Dural Sack Cross-Sectional Area in Lumbar Spine MRI Using Deep Learning"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2298-3696","authenticated-orcid":false,"given":"Babak","family":"Saravi","sequence":"first","affiliation":[{"name":"Department of Orthopedics and Trauma Surgery, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany"},{"name":"Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany"},{"name":"Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria"},{"name":"Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alisia","family":"Zink","sequence":"additional","affiliation":[{"name":"Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"\u00dclk\u00fcmen","sequence":"additional","affiliation":[{"name":"Department of Orthopedics and Trauma Surgery, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-6412","authenticated-orcid":false,"given":"Sebastien","family":"Couillard-Despres","sequence":"additional","affiliation":[{"name":"Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria"},{"name":"Austrian Cluster for Tissue Regeneration, 1200 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6767-7978","authenticated-orcid":false,"given":"Jakob","family":"Wollborn","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0685-1909","authenticated-orcid":false,"given":"Gernot","family":"Lang","sequence":"additional","affiliation":[{"name":"Department of Orthopedics and Trauma Surgery, Medical Center\u2014University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5618-9285","authenticated-orcid":false,"given":"Frank","family":"Hassel","sequence":"additional","affiliation":[{"name":"Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,10]]},"reference":[{"key":"ref_1","first-page":"89","article-title":"Dynamic MRI in the evaluation of the spine: State of the art","volume":"89","author":"Michelini","year":"2018","journal-title":"Acta Biomed."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e9087","DOI":"10.1097\/MD.0000000000009087","article-title":"Dural sac area is a more sensitive parameter for evaluating lumbar spinal stenosis than spinal canal area: A Retrospective Study","volume":"96","author":"Lim","year":"2017","journal-title":"Medicine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1097\/BRS.0b013e3181791af3","article-title":"Reliability of Readings of Magnetic Resonance Imaging Features of Lumbar Spinal Stenosis","volume":"33","author":"Lurie","year":"2008","journal-title":"Spine"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. 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