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在检测巴雷特食道的人工智能:一个系统的审查

Akash Patel¹, Gagandeep Singh Arora^2,3, Mona Roknsharifi³

¹Internal Medicine, Eisenhower Health, Rancho Mirage, USA.

|November 29, 2023

概括

机器学习 (ML) 显著提高了巴雷特食道 (BE) 诊断和管理. 机器学习技术显示出高精度和效率,往往超过传统方法,以改善患者护理.

科学领域:

胃肠病学胃肠病学
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

巴雷特食道 (BE) 是食道腺癌的关键前体.
精确的诊断和管理BE对于患者的结果至关重要.
机器学习 (ML) 提供了新的方法来改善BE诊断.

研究的目的:

系统地审查ML技术在BE诊断和管理中的有效性和准确性.
评估各种ML应用,包括计算机辅助系统,NLP,图像深度学习和视频分析.
综合当前关于BE的ML性能的证据.

主要方法:

在PubMed,Scopus和Web of Science的综合文献搜索到2023年.
研究被分为五组:计算机辅助系统,NLP,组织学/活检图像的深度学习,实时/视频分析,以及其他.
对ML应用报告的灵敏度,特异性和准确度指标的分析.

主要成果:

ML的应用表明了各类类别的高灵敏度和特异性.
计算机辅助系统:灵敏度为84-100%,特异性为64-90.7%.
对图像进行深度学习:>90%的灵敏度,100%的特异性.
在NLP系统中,准确度高达98.7%.
实时视频分析显示了高性能 (例如,75.3%的平均精度).

结论:

机器学习技术在诊断和管理BE方面越来越有能力和效率.
在准确性和速度方面,ML经常超过传统的诊断方法.
ML对提高BE的临床实践和患者护理具有重大前景.

关键词:

人工智能和机器学习胃肠病学中的人工智能医疗保健中的人工智能医学中的人工智能巴雷特的食道.巴雷特的食道 (be)临床胃肠病学临床胃肠病学深度学习人工智能人工智能一般内科内科一般内科.内部医学是内科的专业.

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