{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:45:04Z","timestamp":1791953104486,"version":"4.3.4"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Arch Otorhinolaryngol"],"DOI":"10.1007\/s00405-026-10667-1","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:00:12Z","timestamp":1791925212000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence\u2013based prediction of cerebrospinal fluid leak in skull base surgery: a scoping review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6929-4981","authenticated-orcid":false,"given":"Evren","family":"Aydogmus","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2956-2379","authenticated-orcid":false,"given":"Banu","family":"Atalay Erdogan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"10667_CR1","doi-asserted-by":"publisher","unstructured":"van Aken MO, Feelders RA, de Marie S, van de Berge JH, Dallenga AH, Delwel EJ, Poublon RM, Romijn JA, van der Lely AJ, Lamberts SW, de Herder WW (2004) Cerebrospinal fluid leakage during transsphenoidal surgery: postoperative external lumbar drainage reduces the risk for meningitis. Pituitary. ;7(2):89\u201393. https:\/\/doi.org\/10.1007\/s11102-005-5351-3. PMID: 15761657","DOI":"10.1007\/s11102-005-5351-3"},{"key":"10667_CR2","doi-asserted-by":"publisher","unstructured":"Grotenhuis JA (2005) Costs of postoperative cerebrospinal fluid leakage: 1-year, retrospective analysis of 412 consecutive nontrauma cases. Surg Neurol. ;64(6):490-3, discussion 493-4. https:\/\/doi.org\/10.1016\/j.surneu.2005.03.041. PMID: 16293457","DOI":"10.1016\/j.surneu.2005.03.041"},{"key":"10667_CR3","doi-asserted-by":"publisher","unstructured":"Staartjes VE, Zattra CM, Akeret K, Maldaner N, Muscas G, van Bas CH, Fierstra J, Regli L, Serra C (2019) Neural network-based identification of patients at high risk for intraoperative cerebrospinal fluid leaks in endoscopic pituitary surgery. J Neurosurg. ;133(2):329\u2013335. https:\/\/doi.org\/10.3171\/2019.4.JNS19477. PMID: 31226693","DOI":"10.3171\/2019.4.JNS19477"},{"key":"10667_CR4","doi-asserted-by":"publisher","first-page":"934721","DOI":"10.3389\/fsurg.2022.934721","volume":"9","author":"JF Villalonga","year":"2022","unstructured":"Villalonga JF, Solari D, Cuocolo R, De Lucia V, Ugga L, Gragnaniello C, Pailler JI, Cervio A, Campero A, Cavallo LM, Cappabianca P (2022) Clinical application of the sellar barrier\u2019s concept for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning analysis. Front Surg 9:934721. https:\/\/doi.org\/10.3389\/fsurg.2022.934721 PMID: 36157423; PMCID: PMC9492953","journal-title":"Front Surg"},{"key":"10667_CR5","doi-asserted-by":"publisher","unstructured":"Tariciotti L, Fiore G, Carrabba G, Bertani GA, Schisano L, Borsa S, Ferrante E, Caccavella VM, Mattogno P, Giordano M, Remoli G, Mantovani G, Locatelli M (2023) A supervised machine-learning algorithm predicts intraoperative CSF leak in endoscopic transsphenoidal surgery for pituitary adenomas. J Neurosurg Sci. ;67(4):393\u2013407. https:\/\/doi.org\/10.23736\/S0390-5616.21.05295-4. Epub 2021 Aug 3. PMID: 34342190","DOI":"10.23736\/S0390-5616.21.05295-4"},{"key":"10667_CR6","doi-asserted-by":"publisher","first-page":"1263308","DOI":"10.3389\/fendo.2023.1263308","volume":"14","author":"J Zhao","year":"2024","unstructured":"Zhao J, Wang S, Zhao X, Cui H, Zou C (2024) Risk factors of cerebrospinal fluid leakage after neuroendoscopic transsphenoidal pituitary adenoma resection: a systematic review and meta-analysis. Front Endocrinol (Lausanne) 14:1263308. https:\/\/doi.org\/10.3389\/fendo.2023.1263308PMID: 38260170; PMCID: PMC10800519","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"5","key":"10667_CR7","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1055\/s-0041-1740621","volume":"83","author":"PP Mattogno","year":"2022","unstructured":"Mattogno PP, Caccavella VM, Giordano M, D\u2019Alessandris QG, Chiloiro S, Tariciotti L, Olivi A, Lauretti L (2022) Interpretable Machine Learning-Based Prediction of Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Transsphenoidal Pituitary Surgery: A Pilot Study. J Neurol Surg B Skull Base 83(5):485\u2013495. https:\/\/doi.org\/10.1055\/s-0041-1740621 PMID: 36091632; PMCID: PMC9462964","journal-title":"J Neurol Surg B Skull Base"},{"key":"10667_CR8","doi-asserted-by":"publisher","first-page":"1203698","DOI":"10.3389\/fnins.2023.1203698","volume":"17","author":"H Chang","year":"2023","unstructured":"Chang H, Zhao K, Qiu J, Ji XJ, Chen WG, Li BY, Lv C, Xiong ZC, Chen SB, Shu XJ (2023) Prediction of intraoperative cerebrospinal fluid leaks in endoscopic endonasal transsphenoidal pituitary surgery based on a deep neural network model trained with MRI images: a pilot study. Front Neurosci 17:1203698. https:\/\/doi.org\/10.3389\/fnins.2023.1203698 PMID: 37575298; PMCID: PMC10413098","journal-title":"Front Neurosci"},{"key":"10667_CR9","doi-asserted-by":"publisher","first-page":"1046519","DOI":"10.3389\/fonc.2023.1046519","volume":"13","author":"CRANIAL Consortium","year":"2023","unstructured":"CRANIAL Consortium (2023) Machine learning driven prediction of cerebrospinal fluid rhinorrhoea following endonasal skull base surgery: a multicentre prospective observational study. Front Oncol 13:1046519. https:\/\/doi.org\/10.3389\/fonc.2023.1046519","journal-title":"Front Oncol"},{"key":"10667_CR10","doi-asserted-by":"publisher","first-page":"e324","DOI":"10.1016\/j.wneu.2024.06.043","volume":"189","author":"F Behzadi","year":"2024","unstructured":"Behzadi F, Alhusseini M, Yang SD, Mallik AK, Germanwala AV (2024) A Predictive Model for Intraoperative Cerebrospinal Fluid Leak During Endonasal Pituitary Adenoma Resection Using a Convolutional Neural Network. World Neurosurg 189:e324\u2013e330. https:\/\/doi.org\/10.1016\/j.wneu.2024.06.043 Epub 2024 Jun 12. PMID: 38876190","journal-title":"World Neurosurg"},{"issue":"12","key":"10667_CR11","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1007\/s11548-024-03259-z","volume":"19","author":"SD Curry","year":"2024","unstructured":"Curry SD, Boochoon KS, Casazza GC, Surdell DL, Cramer JA (2024) Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele. Int J Comput Assist Radiol Surg 19(12):2453\u20132461. https:\/\/doi.org\/10.1007\/s11548-024-03259-z Epub 2024 Aug 29. PMID: 39207718","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10667_CR12","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.wneu.2024.09.015","volume":"191","author":"B Hajikarimloo","year":"2024","unstructured":"Hajikarimloo B, Sabbagh Alvani M, Koohfar A, Goudarzi E, Dehghan M, Hojjat SH, Hashemi R, Tos SM, Akhlaghpasand M, Habibi MA (2024) Clinical Application of Artificial Intelligence in Prediction of Intraoperative Cerebrospinal Fluid Leakage in Pituitary Surgery: A Systematic Review and Meta-Analysis. World Neurosurg 191:303\u2013313e1 Epub 2024 Sep 10. PMID: 39265946","journal-title":"World Neurosurg"},{"key":"10667_CR13","doi-asserted-by":"publisher","unstructured":"Alfallaj R, Bazi Y, Al Rahhal MM, Zuair MA, Alqurashi A, Alroqi A et al (2025) A novel BERT-based machine learning approach for enhanced CSF leak prediction in endoscopic endonasal skull base surgery. J Neurol Surg B Skull Base Published online Oct 23. https:\/\/doi.org\/10.1055\/a-2719-8970","DOI":"10.1055\/a-2719-8970"},{"issue":"7","key":"10667_CR14","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7326\/M18-0850","volume":"169","author":"AC Tricco","year":"2018","unstructured":"Tricco AC, Lillie E, Zarin W et al (2018) PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 169(7):467\u2013473","journal-title":"Ann Intern Med"},{"key":"10667_CR15","doi-asserted-by":"crossref","unstructured":"Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. ;385:e078378.","DOI":"10.1136\/bmj-2023-078378"},{"key":"10667_CR16","doi-asserted-by":"crossref","unstructured":"Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH. MINIMAR (MINimum Information for Medical AI Reporting): developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc. 2020;27(12):2011-2015.","DOI":"10.1093\/jamia\/ocaa088"},{"key":"10667_CR17","doi-asserted-by":"crossref","unstructured":"Ribeiro, Singh MT, Guestrin S et al (2024) C. Why Should I Trust You? Explaining the Predictions of Any Classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016. pp. 1135-1144.","DOI":"10.1145\/2939672.2939778"}],"container-title":["European Archives of Oto-Rhino-Laryngology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00405-026-10667-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00405-026-10667-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00405-026-10667-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T03:49:36Z","timestamp":1791949776000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00405-026-10667-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,13]]},"references-count":17,"alternative-id":["10667"],"URL":"https:\/\/doi.org\/10.1007\/s00405-026-10667-1","relation":{},"ISSN":["0937-4477","1434-4726"],"issn-type":[{"value":"0937-4477","type":"print"},{"value":"1434-4726","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,13]]},"assertion":[{"value":"6 July 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}