PDF herunterladen
Open Access
CC BY 4.0 · J Neurol Surg B Skull Base
DOI: 10.1055/a-2719-8970
Original Article

A Novel BERT-Based Machine Learning Approach for Enhanced CSF Leak Prediction in Endoscopic Endonasal Skull Base Surgery

Authors

  • Rayan Alfallaj

    1   Department of Otolaryngology - Head and Neck Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia

  • Yakoub Bazi

    2   Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

  • Mohamad M. A. Rahhal

    3   Applied Computer Science Department, College of Applied Computer Science, King Saud University, Riyadh, Saudi Arabia

  • Mansour Zuair

    2   Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

  • Ashwaq Alqurashi

    4   Devision of Neurosurgery, King Saud University, Riyadh, Saudi Arabia

  • Ahmad Alroqi

    1   Department of Otolaryngology - Head and Neck Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia

  • Abdulrazag Ajlan

    4   Devision of Neurosurgery, King Saud University, Riyadh, Saudi Arabia

  • Saad Alsaleh

    1   Department of Otolaryngology - Head and Neck Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia

  • Abdulaziz S. Alrasheed

    1   Department of Otolaryngology - Head and Neck Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia
Weitere Informationen
Auch verfügbar auf
  Lizenzen und Reprints
Preview

Abstract

Objectives

To evaluate the performance of the BERT (bidirectional encoder representations from transformers) model in predicting cerebrospinal fluid (CSF) leaks and compare it with traditional logistic regression analysis.

Methods

In this study, we employed a machine learning-based natural language processing (NLP) model, specifically BERT, and compared its performance to conventional statistical logistic regression in predicting CSF leaks. We analyzed all cases of skull base pathologies treated by a multidisciplinary team specializing in rhinology and skull base surgery, and neurosurgery at a single center between March 2015 and July 2020. The dataset included the following factors: (1) demographics, (2) perioperative clinical CSF leak indicators, (3) pathology-related factors, (4) surgical factors, and (5) perioperative CT scan features.

Results

The BERT model outperformed the traditional logistic regression model in predicting CSF leaks, achieving an AUC of 1.0000, sensitivity of 1.0000, specificity of 0.9808, positive predictive value (PPV) of 0.8889, negative predictive value (NPV) of 1.0000, and an F1 score of 0.9657. In contrast, the logistic regression model yielded an AUC of 0.847, with a sensitivity of 0.2143, specificity of 0.9060, PPV of 0.4471, and NPV of 0.7649.

Conclusion

BERT NLP model outperforms traditional logistic regression in predicting cerebrospinal fluid leaks after endoscopic skull base surgery, demonstrating superior accuracy with qualitative clinical data, enhancing risk stratification and decision-making.

Keywords

AI - CSF - machine learning - NLP - skull base - BERT - reconstruction - rhinology

Data Availability Statement

The datasets analyzed during the current study are available from the corresponding author on request.


Ethical Approval

Ethical approval for the study was obtained from the ethics committee at the College of Medicine at King Saud University (no. 23-8232).


Supplementary Material



Publikationsverlauf

Eingereicht: 01. August 2025

Angenommen: 05. Oktober 2025

Accepted Manuscript online:
10. Oktober 2025

Artikel online veröffentlicht:
23. Oktober 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany