Clinical Application of Artificial Intelligence in Prediction of Intraoperative Cerebrospinal Fluid Leakage in Pituitary Surgery: A Systematic Review and Meta-Analysis.

Abstract

BACKGROUND Postoperative cerebrospinal fluid (CSF) leakage is the leading adverse event in transsphenoidal surgery (TSS). Intraoperative CSF (ioCSF) leakage is one of the most important predictive factors for postoperative CSF leakage. This systematic review and meta-analysis aimed to evaluate the effectiveness of artificial intelligence (AI) models in predicting ioCSF. METHODS Literature records were retrieved on June 13th, 2024, using the relevant key terms without filters in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from the included studies were extracted. The quality assessment was performed using the QUADAS-2 tool. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software. RESULTS Our results demonstrate that the AI models achieved a pooled sensitivity of 93.4% (95% CI: 74.8%- 98.6%) and specificity of 91.7% (95% CI: 75%- 97.6%). The subgroup analysis revealed that the pooled sensitivities in ML and DL were 86.2% (95% CI: 83%- 88.8%) and 99% (95% CI: 93%- 99%), respectively (P<0.01). The subgroup analysis demonstrated a pooled specificity of 92.1% (95% CI: 63.1%- 98.7%) for ML and 90.6% (95% CI: 78.2%- 96.3%) for DL models (P= 0.87). The DOR meta-analysis revealed an odds ratio (OR) 114.6 (95% CI: 17.6- 750.9). The SROC curve demonstrated that the overall AUC of the studies was 0.955, which is a considerable performance. CONCLUSION AI models have demonstrated promising performance for predicting the ioCSF leakage in pituitary surgery and can optimize the treatment strategy.