• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Infection detection revolution: Harnessing AI-powered image analysis to combat infectious diseases.感染检测革命:利用人工智能驱动的图像分析来对抗传染病。
PLoS One. 2024 Oct 9;19(10):e0307437. doi: 10.1371/journal.pone.0307437. eCollection 2024.
2
Mathematical modeling and AI based decision making for COVID-19 suspects backed by novel distance and similarity measures on plithogenic hypersoft sets.基于新型距离和相似度度量的 COVID-19 疑似病例的数学建模和人工智能决策支持,这些度量是在多亲软集合上提出的。
Artif Intell Med. 2022 Oct;132:102390. doi: 10.1016/j.artmed.2022.102390. Epub 2022 Sep 2.
3
Image analysis and artificial intelligence in infectious disease diagnostics.传染病诊断中的图像分析和人工智能。
Clin Microbiol Infect. 2020 Oct;26(10):1318-1323. doi: 10.1016/j.cmi.2020.03.012. Epub 2020 Mar 22.
4
Harnessing artificial intelligence for advancing early diagnosis in hidradenitis suppurativa.利用人工智能推进化脓性汗腺炎的早期诊断。
Ital J Dermatol Venerol. 2024 Feb;159(1):43-49. doi: 10.23736/S2784-8671.23.07829-5.
5
Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study.应用人工智能三部曲在智能检疫站加速新型冠状病毒肺炎疑似患者的处理:观察性研究
J Med Internet Res. 2020 Oct 14;22(10):e19878. doi: 10.2196/19878.
6
A deep learning approach to direct immunofluorescence pattern recognition in autoimmune bullous diseases.深度学习方法在自身免疫性大疱性疾病中的直接免疫荧光模式识别。
Br J Dermatol. 2024 Jul 16;191(2):261-266. doi: 10.1093/bjd/ljae142.
7
Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges.可解释人工智能和机器学习:应对面部传染病挑战的新方法。
Ann Med. 2023;55(2):2286336. doi: 10.1080/07853890.2023.2286336. Epub 2023 Nov 27.
8
Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing.人工智能和机器学习在传染病检测中的应用不断发展。
Clin Chem. 2021 Dec 30;68(1):125-133. doi: 10.1093/clinchem/hvab239.
9
Imaging Intelligence: AI Is Transforming Medical Imaging Across the Imaging Spectrum.影像智能:人工智能正在改变整个影像领域的医学成像。
IEEE Pulse. 2018 Sep-Oct;9(5):16-24. doi: 10.1109/MPUL.2018.2857226.
10
Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering.基于 EfficientNetB2 的均衡化和同态滤波的先进 AI 驱动方法,用于增强从 MRI 图像中检测脑肿瘤。
BMC Med Inform Decis Mak. 2024 Apr 30;24(1):113. doi: 10.1186/s12911-024-02519-x.

本文引用的文献

1
A New X-ray Medical-Image-Enhancement Method Based on Multiscale Shannon-Cosine Wavelet.一种基于多尺度香农-余弦小波的新型X射线医学图像增强方法。
Entropy (Basel). 2022 Nov 30;24(12):1754. doi: 10.3390/e24121754.
2
Multipurpose medical image watermarking for effective security solutions.用于有效安全解决方案的多功能医学图像水印技术。
Multimed Tools Appl. 2022;81(10):14045-14063. doi: 10.1007/s11042-022-12082-0. Epub 2022 Feb 25.
3
Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation.基于结构保留的双尺度多模态医学图像融合
Front Comput Neurosci. 2022 Jan 31;15:803724. doi: 10.3389/fncom.2021.803724. eCollection 2021.
4
ANN and Fuzzy Logic Based Model to Evaluate Huntington Disease Symptoms.基于 ANN 和模糊逻辑的亨廷顿病症状评估模型。
J Healthc Eng. 2018 Mar 11;2018:4581272. doi: 10.1155/2018/4581272. eCollection 2018.
5
Image Fusion of CT and MR with Sparse Representation in NSST Domain.基于非下采样剪切波变换域稀疏表示的CT与MR图像融合
Comput Math Methods Med. 2017;2017:9308745. doi: 10.1155/2017/9308745. Epub 2017 Nov 9.
6
CT and MR image fusion scheme in nonsubsampled contourlet transform domain.非下采样轮廓波变换域中的CT与MR图像融合方案
J Digit Imaging. 2014 Jun;27(3):407-18. doi: 10.1007/s10278-013-9664-x.
7
The perpetual challenge of infectious diseases.传染病的长期挑战。
N Engl J Med. 2012 Feb 2;366(5):454-61. doi: 10.1056/NEJMra1108296.
8
Image fusion using higher order singular value decomposition.基于高阶奇异值分解的图像融合。
IEEE Trans Image Process. 2012 May;21(5):2898-909. doi: 10.1109/TIP.2012.2183140. Epub 2012 Jan 9.
9
The theory of decision making.决策理论
Psychol Bull. 1954 Jul;51(4):380-417. doi: 10.1037/h0053870.

感染检测革命:利用人工智能驱动的图像分析来对抗传染病。

Infection detection revolution: Harnessing AI-powered image analysis to combat infectious diseases.

机构信息

Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania.

出版信息

PLoS One. 2024 Oct 9;19(10):e0307437. doi: 10.1371/journal.pone.0307437. eCollection 2024.

DOI:10.1371/journal.pone.0307437
PMID:39383149
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11463793/
Abstract

Infectious diseases wield significant influence on global mortality rates, largely due to the challenge of gauging their severity owing to diverse symptomatology. Each nation grapples with its unique obstacles in combatting these diseases. This study delves into three distinct decision-making methodologies for medical diagnostics employing Neutrosophic Hypersoft Set (NHSS) and Plithogenic Hypersoft Set (PHSS), extensions of the Hypersoft set. It introduces state-of-the-art AI-driven techniques to enhance the precision of medical diagnostics through the analysis of medical imagery. By transforming these images into the aforementioned sets, the analysis becomes more refined, facilitating more accurate diagnoses. The study advocates various courses of action, including isolation, home or specialized center quarantine, or hospitalization for further treatment. The novelty in this study utilizes cutting-edge AI methods to enhance medical imaging, transforming them into accurate diagnostic tools, marking a significant change in how infectious diseases are addressed. By combining machine learning and pattern recognition, it offers the potential to overhaul healthcare worldwide, facilitating accurate diagnoses and customized treatment plans, ultimately reducing the global burden of infectious diseases on mortality rates.

摘要

传染病对全球死亡率有重大影响,主要是因为由于症状的多样性,难以衡量其严重程度。每个国家在对抗这些疾病时都面临着自己独特的障碍。本研究探讨了使用 Neutrosophic Hypersoft Set (NHSS) 和 Plithogenic Hypersoft Set (PHSS) 的三种不同的医学诊断决策方法,这两种方法都是 Hypersoft 集的扩展。本研究引入了最先进的人工智能驱动技术,通过分析医学图像来提高医学诊断的精度。通过将这些图像转换为上述集合,分析变得更加精细,从而更准确地进行诊断。本研究主张采取各种行动方案,包括隔离、在家或专门中心检疫,或住院进一步治疗。本研究的新颖之处在于利用最先进的人工智能方法来增强医学图像,将其转化为准确的诊断工具,这标志着传染病处理方式的重大改变。通过结合机器学习和模式识别,它有可能彻底改变全球医疗保健,实现准确的诊断和定制的治疗方案,最终降低传染病对全球死亡率的影响。