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使用基因组学和人工智能机器学习方法诊断非结核性菌根感染:范围,进展和挑战
Madhan Kumar Murthy1, Vivek Kumar Gupta2, Anand Prakash Maurya3
1Department of Immunology, ICMR-National JALMA Institute for Leprosy and Other Mycobacterial Diseases (ICMR-NJIL&OMD), Agra, Uttar Pradesh, India.
Frontiers in microbiology
|September 19, 2025
概括
像全基因组测序这样的基因组技术已经彻底改变了非结核性菌根 (NTM) 诊断. 人工智能通过分析复杂的基因组数据来进一步增强NTM感染检测,以获得更快的结果.
科学领域:
- 医学微生物学 医学微生物学
- 基因组学就是基因组学.
- 传染性疾病 传染性疾病
背景情况:
- 非结核菌 (NTM) 感染对健康构成重大风险,尤其对免疫功能低下的人和患有肺部疾病的人来说.
- 及时准确地诊断NTM感染对于有效的疾病管理和改善患者的治疗结果至关重要.
研究的目的:
- 审查基因组学和人工智能 (AI) 在诊断NTM感染方面的进展.
- 突出这些技术对物种识别和诊断效率的影响.
- 讨论在NTM诊断中利用基因组学和人工智能的未来方向.
主要方法:
- 对包括全基因组测序 (WGS),目标下一代测序 (tNGS) 和元基因组NGS (mNGS) 在内的基因组方法的审查.
- 探索人工智能 (AI) 在分析NTM诊断的复杂基因组数据中的作用.
- 讨论这些技术如何完善物种识别和降低诊断成本.
主要成果:
- 包括WGS,tNGS和mNGS在内的基因组方法显著提高了NTM物种识别的容易性和准确性.
- 人工智能技术通过快速分析复杂的基因组数据来增强NTM诊断.
- 技术进步使NTM诊断更具成本效益,而不会影响质量.
结论:
- 基因组学已经改变了NTM感染的诊断环境,提供了精确的物种识别.
- 人工智能集成有望通过解释庞大的基因组数据集,进一步简化和加速NTM诊断.
- 基因组学和人工智能的持续开发和应用对于最大限度地提高管理NTM感染的好处至关重要.
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