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

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-09-10 DOI:10.1016/j.wneu.2024.09.015

Bardia Hajikarimloo,Mohammadamin Sabbagh Alvani,Amir Koohfar,Ehsan Goudarzi,Mandana Dehghan,Seyed Hesam Hojjat,Rana Hashemi,Salem M Tos,Mohammadhosein Akhlaghpasand,Mohammad Amin Habibi

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引用次数: 0

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.

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人工智能在垂体手术术中脑脊液漏预测中的临床应用：系统回顾与元分析》。

背景术后脑脊液（CSF）漏是经椎体手术（TSS）中最主要的不良事件。术中脑脊液（ioCSF）漏是术后脑脊液漏最重要的预测因素之一。本系统综述和荟萃分析旨在评估人工智能（AI）模型在预测ioCSF方面的有效性。方法于2024年6月13日在PubMed、Embase、Scopus和Web of Science中使用相关关键词检索文献记录，不加过滤。根据资格标准对记录进行筛选，并提取纳入研究的数据。采用 QUADAS-2 工具进行质量评估。结果表明，人工智能模型的集合灵敏度为 93.4%（95% CI：74.8%- 98.6%），特异性为 91.7%（95% CI：75%- 97.6%）。亚组分析显示，ML 和 DL 的集合灵敏度分别为 86.2% (95% CI: 83%- 88.8%) 和 99% (95% CI: 93%- 99%) (P<0.01)。亚组分析表明，ML 模型的集合特异性为 92.1%（95% CI：63.1%- 98.7%），DL 模型的集合特异性为 90.6%（95% CI：78.2%- 96.3%）（P= 0.87）。DOR荟萃分析显示，几率比（OR）为 114.6（95% CI：17.6- 750.9）。结论AI 模型在预测垂体手术中 ioCSF 渗漏方面表现良好，可优化治疗策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.