{"title":"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》。","authors":"Bardia Hajikarimloo,Mohammadamin Sabbagh Alvani,Amir Koohfar,Ehsan Goudarzi,Mandana Dehghan,Seyed Hesam Hojjat,Rana Hashemi,Salem M Tos,Mohammadhosein Akhlaghpasand,Mohammad Amin Habibi","doi":"10.1016/j.wneu.2024.09.015","DOIUrl":null,"url":null,"abstract":"BACKGROUND\r\nPostoperative 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.\r\n\r\nMETHODS\r\nLiterature 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.\r\n\r\nRESULTS\r\nOur 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.\r\n\r\nCONCLUSION\r\nAI models have demonstrated promising performance for predicting the ioCSF leakage in pituitary surgery and can optimize the treatment strategy.","PeriodicalId":23906,"journal":{"name":"World neurosurgery","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical Application of Artificial Intelligence in Prediction of Intraoperative Cerebrospinal Fluid Leakage in Pituitary Surgery: A Systematic Review and Meta-Analysis.\",\"authors\":\"Bardia Hajikarimloo,Mohammadamin Sabbagh Alvani,Amir Koohfar,Ehsan Goudarzi,Mandana Dehghan,Seyed Hesam Hojjat,Rana Hashemi,Salem M Tos,Mohammadhosein Akhlaghpasand,Mohammad Amin Habibi\",\"doi\":\"10.1016/j.wneu.2024.09.015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BACKGROUND\\r\\nPostoperative 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.\\r\\n\\r\\nMETHODS\\r\\nLiterature 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.\\r\\n\\r\\nRESULTS\\r\\nOur 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.\\r\\n\\r\\nCONCLUSION\\r\\nAI models have demonstrated promising performance for predicting the ioCSF leakage in pituitary surgery and can optimize the treatment strategy.\",\"PeriodicalId\":23906,\"journal\":{\"name\":\"World neurosurgery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World neurosurgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.wneu.2024.09.015\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World neurosurgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.wneu.2024.09.015","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Clinical Application of Artificial Intelligence in Prediction of Intraoperative Cerebrospinal Fluid Leakage in Pituitary Surgery: A Systematic Review and Meta-Analysis.
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.
期刊介绍:
World Neurosurgery has an open access mirror journal World Neurosurgery: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review.
The journal''s mission is to:
-To provide a first-class international forum and a 2-way conduit for dialogue that is relevant to neurosurgeons and providers who care for neurosurgery patients. The categories of the exchanged information include clinical and basic science, as well as global information that provide social, political, educational, economic, cultural or societal insights and knowledge that are of significance and relevance to worldwide neurosurgery patient care.
-To act as a primary intellectual catalyst for the stimulation of creativity, the creation of new knowledge, and the enhancement of quality neurosurgical care worldwide.
-To provide a forum for communication that enriches the lives of all neurosurgeons and their colleagues; and, in so doing, enriches the lives of their patients.
Topics to be addressed in World Neurosurgery include: EDUCATION, ECONOMICS, RESEARCH, POLITICS, HISTORY, CULTURE, CLINICAL SCIENCE, LABORATORY SCIENCE, TECHNOLOGY, OPERATIVE TECHNIQUES, CLINICAL IMAGES, VIDEOS