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人工智能在改进和加强腹主动脉瘤当前管理中的扩展:文献综述

The Expansion of Artificial Intelligence in Modifying and Enhancing the Current Management of Abdominal Aortic Aneurysms: A Literature Review.

作者信息

Khalid Usman, Stoev Hristo A, Yavorov Boyko, Ansari Areeb

机构信息

Medicine, Medical University of Plovdiv, Plovdiv, BGR.

Cardiac Surgery, St. George University Hospital, Plovdiv, BGR.

出版信息

Cureus. 2024 Aug 7;16(8):e66398. doi: 10.7759/cureus.66398. eCollection 2024 Aug.

DOI:10.7759/cureus.66398
PMID:39247022
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11379419/
Abstract

An abdominal aortic aneurysm (AAA) is a pathological dilation that is 3 cm or greater resulting in a bulging or balloon appearance. To meet a personalized therapeutic approach for patients, artificial intelligence (AI) can exhibit an array of applications ranging from decoding patterns from large data sets to predicting new data. The review aims to discuss how AI can assist and improve the standard of care and management plans for these patients. A comprehensive non-systematic literature review was carried out for published material on the use of AI relating to AAAs. The PubMed and Google Scholar databases were used to scout for articles relating to the title of this review. The review included 54 literature papers in this study. AI is involved on a genomic level, which assists in screening, diagnosing, and identifying individual risk factors of a patient. Personalized management plans can be created with AI predictions using patient data to reduce the risk of in-hospital mortality following a repair or due to complications. AI represents a promising group of programs aimed at improving patient management and assisting surgeons in making beneficial decisions to improve the patient's prognosis.

摘要

腹主动脉瘤(AAA)是一种病理性扩张,直径达3厘米或更大,呈现出凸起或气球样外观。为了满足患者的个性化治疗方法,人工智能(AI)可以展现出一系列应用,从解码大数据集的模式到预测新数据。本综述旨在讨论人工智能如何协助并改善这些患者的护理标准和管理计划。针对已发表的有关人工智能在腹主动脉瘤方面应用的材料进行了全面的非系统性文献综述。使用PubMed和谷歌学术数据库搜索与本综述标题相关的文章。本研究纳入了54篇文献。人工智能涉及基因组层面,有助于筛查、诊断和识别患者的个体风险因素。利用患者数据通过人工智能预测可制定个性化管理计划,以降低修复后或因并发症导致的院内死亡风险。人工智能代表了一组有前景的程序,旨在改善患者管理并协助外科医生做出有益决策以改善患者预后。

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Identifying abdominal aortic aneurysm size and presence using Natural Language Processing of radiology reports: a systematic review and meta-analysis.利用放射学报告的自然语言处理识别腹主动脉瘤的大小和存在情况:一项系统综述和荟萃分析
Abdom Radiol (NY). 2025 Jan 30. doi: 10.1007/s00261-025-04810-5.

本文引用的文献

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Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance.全球人工智能伦理:对200项人工智能治理指南和建议的综述。
Patterns (N Y). 2023 Oct 13;4(10):100857. doi: 10.1016/j.patter.2023.100857.
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Medical artificial intelligence ethics: A systematic review of empirical studies.医学人工智能伦理:实证研究的系统综述
Digit Health. 2023 Jul 6;9:20552076231186064. doi: 10.1177/20552076231186064. eCollection 2023 Jan-Dec.
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Artificial Intelligence in Surgical Risk Prediction.手术风险预测中的人工智能
J Clin Med. 2023 Jun 13;12(12):4016. doi: 10.3390/jcm12124016.
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Diagnostic Accuracy of AI for Opportunistic Screening of Abdominal Aortic Aneurysm in CT: A Systematic Review and Narrative Synthesis.人工智能在CT中对腹主动脉瘤进行机会性筛查的诊断准确性:一项系统评价和叙述性综合分析
Diagnostics (Basel). 2022 Dec 16;12(12):3197. doi: 10.3390/diagnostics12123197.
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Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare-A Scoping Review of Reviews.人工智能在医疗保健领域应用中的伦理困境——综述的范围界定研究
J Pers Med. 2022 Nov 16;12(11):1914. doi: 10.3390/jpm12111914.
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Use of random forest machine learning algorithm to predict short term outcomes following posterior cervical decompression with instrumented fusion.应用随机森林机器学习算法预测后路颈椎减压融合术后的短期转归。
J Clin Neurosci. 2023 Jan;107:167-171. doi: 10.1016/j.jocn.2022.10.029. Epub 2022 Nov 12.
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Abdominal Aortic Aneurysm.腹主动脉瘤。
Am Fam Physician. 2022 Aug;106(2):165-172.
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Single-Cell Sequencing Analysis and Multiple Machine Learning Methods Identified G0S2 and HPSE as Novel Biomarkers for Abdominal Aortic Aneurysm.单细胞测序分析和多种机器学习方法确定 G0S2 和 HPSE 为腹主动脉瘤的新型生物标志物。
Front Immunol. 2022 Jun 13;13:907309. doi: 10.3389/fimmu.2022.907309. eCollection 2022.
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Ethical Issues of Artificial Intelligence in Medicine and Healthcare.医学与医疗保健领域中人工智能的伦理问题。
Iran J Public Health. 2021 Nov;50(11):i-v. doi: 10.18502/ijph.v50i11.7600.