对用于诊断,药物发现和莱什曼病疫苗开发的深度学习技术进行范围审查
Alireza Sadeghi1, Mahdieh Sadeghi2, Mahdi Fakhar3
1Intelligent Mobile Robot Lab (IMRL), Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
Transboundary and emerging diseases
|April 30, 2025
概括
深度学习在诊断莱什曼病和开发新疗法方面表现有前途. 本综述涵盖了人工智能在寄生虫检测,药物发现和莱什曼病疫苗开发中的应用.
科学领域:
- 医学寄生虫学 医学寄生虫学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 莱什曼尼亚寄生虫导致莱什曼尼亚病,这是一种具有重大全球健康影响的疾病,从皮肤病变到致命的内脏并发症.
- 流细胞计和分子生物学等诊断技术的进步改善了莱什曼病的检测.
- 人工智能 (AI),特别是深度学习,在医学诊断和治疗中提供了高精度和减少错误的潜力.
研究的目的:
- 进行第一个深度学习应用在莱什曼病的范围审查.
- 探索深度学习在疾病诊断,药物发现和莱什曼尼亚感染疫苗开发中的应用.
- 识别研究缺口,并为这个跨学科领域的未来研究提供指南.
主要方法:
- 在莱什曼病研究中使用深度学习方法进行研究的综合文献搜索.
- 精选文章的详细分析,重点关注方法和结果.
- 对诊断,药物发现和疫苗开发的深度学习应用程序进行系统审查.
主要成果:
- 深度学习方法已经应用于莱什曼病的各个方面,包括诊断,药物发现和疫苗开发.
- 对现有文献的分析强调了深度学习在提高莱什曼病研究的准确性和效率方面的潜力.
- 该审查确定了在Leishmania的背景下特定的深度学习技术及其报告的结果.
结论:
- 本综述提供了对莱什曼病深度学习应用的全面概述,解决了关键的知识差距.
- 深度学习为推进莱什曼病的诊断,治疗和预防提供了一个有希望的途径.
- 这些发现是研究人员的基础资源,旨在利用人工智能对抗莱什曼病.
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