Evidence map›Paper›PMID 39207718›Full record

ArticleInternational journal of computer assisted radiology and surgery2024

Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele.

Steven D Curry, Kieran S Boochoon, Geoffrey C Casazza, Daniel L Surdell, Justin A Cramer

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Article in International journal of computer assisted radiology and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Steven D CurryDepartment of Otolaryngology, Head and Neck Surgery, University of Nebraska Medical Center, 981225 Nebraska Medical Center, Omaha, NE, 68198-1225, USA. scurry@houseclinic.com.ORCID http://orcid.org/0000-0002-6247-7483
Kieran S BoochoonDepartment of Otolaryngology, Head and Neck Surgery, University of Nebraska Medical Center, 981225 Nebraska Medical Center, Omaha, NE, 68198-1225, USA.ORCID http://orcid.org/0000-0003-1985-8333
Geoffrey C CasazzaDepartment of Otolaryngology, Head and Neck Surgery, University of Nebraska Medical Center, 981225 Nebraska Medical Center, Omaha, NE, 68198-1225, USA.ORCID http://orcid.org/0000-0001-8590-3601
Daniel L SurdellDepartment of Neurosurgery, University of Nebraska Medical Center, 988437 Nebraska Medical Center, Omaha, NE, 68198-8437, USA.ORCID http://orcid.org/0000-0003-1433-2383
Justin A CramerDepartment of Radiology, Mayo Clinic, 5777 E Mayo Boulevard, Phoenix, AZ, 85054, USA.ORCID http://orcid.org/0000-0003-0350-184X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSkull base features, including increased foramen ovale (FO) cross-sectional area, are associated with lateral skull base spontaneous cerebrospinal fluid (sCSF) leak and encephalocele. Manual measurement requires skill in interpreting imaging studies and is time consuming. The goal of this study was to develop a fully automated deep learning method for FO segmentation and to determine the predictive value in identifying patients with sCSF leak or encephalocele.

methodsA retrospective cohort study at a tertiary care academic hospital of 34 adults with lateral skull base sCSF leak or encephalocele were compared with 815 control patients from 2013-2021. A convolutional neural network (CNN) was constructed for image segmentation of axial computed tomography (CT) studies. Predicted FO segmentations were compared to manual segmentations, and receiver operating characteristic (ROC) curves were constructed.

results295 CTs were used for training and validation of the CNN. A separate dataset of 554 control CTs was matched 5:1 on age and sex with the sCSF leak/encephalocele group. The mean Dice score was 0.81. The sCSF leak/encephalocele group had greater mean (SD) FO cross-sectional area compared to the control group, 29.0 (7.7) mm

conclusionCNNs can be used to segment the cross-sectional area of the FO accurately and efficiently. Used together with other predictors, this method could be used as part of a clinical tool to predict the risk of sCSF leak or encephalocele.

Indexed as

Cerebrospinal Fluid LeakDeep LearningEncephaloceleSkull BaseTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesRisk AssessmentAutomatic segmentationConvolutional neural networkDeep learningEncephaloceleIdiopathic intracranial hypertensionSpontaneous CSF leak

Identifiers

PMID39207718

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.