使用人工智能进行癌症检测:早期诊断的范例

IF 12.1 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Gayathri Bulusu, K. E. Ch Vidyasagar, Malini Mudigonda, Manob Jyoti Saikia
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引用次数: 0

摘要

长期以来,肿瘤检测一直是肿瘤学研究的关键。近年来,人工智能(AI)的革命及其在癌症领域的应用越来越有前景。本文详细综述了人工智能在不同癌症及其分期中的各个方面。研究了人工智能在图像数据解释和处理中的作用,以及其检测肿瘤的准确性和敏感性。通过MRI, CT,超声等成像方式获得的图像在本文中被考虑。此外,该综述还强调了最近肿瘤学研究中讨论的人工智能算法在乳腺癌、前列腺癌、肺癌等12种癌症中的应用。这篇评论总结了人工智能应用所面临的挑战。结果显示,人工智能在检测癌症的区域、大小、等级等方面的效果。虽然CT和超声被证明是癌症检测的理想成像方式,但MRI有助于癌症分期。该综述为充分利用人工智能在早期癌症检测和分期方面的潜力以提高患者生存率提供了路线图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cancer Detection Using Artificial Intelligence: A Paradigm in Early Diagnosis

Cancer detection has long been a continuous key performer in oncological research. The revolution of artificial intelligence (AI) and its application in the field of cancer turned out to be more promising in the recent years. This paper provides a detailed review of the various aspects of AI in different cancers and their staging. The role of AI in interpreting and processing the imaging data, its accuracy and sensitivity to detect the tumors is examined. The images obtained through imaging modalities like MRI, CT, ultrasound etc. are considered in this review. Further the review highlights the implementation of AI algorithms in 12 types of cancers like breast cancer, prostate cancer, lung cancer etc. as discussed in the recent oncological studies. The review served to summarize the challenges involved with AI application. It revealed the efficacy of AI in detecting the region, size, and grade of cancer. While CT and ultrasound proved to be the ideal imaging modalities for cancer detection, MRI was helpful for cancer staging. The review bestows a roadmap to fully utilize the potential of AI in early cancer detection and staging to enhance patient survival.

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来源期刊
CiteScore
19.80
自引率
4.10%
发文量
153
审稿时长
>12 weeks
期刊介绍: Archives of Computational Methods in Engineering Aim and Scope: Archives of Computational Methods in Engineering serves as an active forum for disseminating research and advanced practices in computational engineering, particularly focusing on mechanics and related fields. The journal emphasizes extended state-of-the-art reviews in selected areas, a unique feature of its publication. Review Format: Reviews published in the journal offer: A survey of current literature Critical exposition of topics in their full complexity By organizing the information in this manner, readers can quickly grasp the focus, coverage, and unique features of the Archives of Computational Methods in Engineering.
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