通过生物灵感优化技术提高男性生育诊断的精度
Priyanka Ramdass1, Gajendran Ganesan2, Farid Selatnia3
1Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, TamilNadu, India.
Scientific reports
|October 28, 2025
概括
这项研究引入了一种针对男性不孕症的新型人工智能诊断工具,它结合了神经网络和殖民地优化. 它实现了99%的准确性,提供了对生殖健康因素的高效和可解释的见解.
科学领域:
- 生殖医学 生殖医学
- 人工智能的人工智能
- 生物启发的计算 生物启发的计算
背景情况:
- 男性不孕症影响了近一半的病例,但由于耻辱,经常被诊断不足.
- 久坐不动的行为,环境因素和压力会恶化男性的生殖健康.
- 传统的诊断方法在准确性和效率方面存在局限性.
研究的目的:
- 开发和评估用于男性不孕症的混合AI诊断框架.
- 提高预测准确度,克服传统诊断方法的局限性.
- 为早期检测和个性化治疗提供临床可解释的见解.
主要方法:
- 一个混合框架集成一个多层前神经网络与殖民地优化算法.
- 适应性参数调整灵感来自于的食行为.
- 基于100个男性生育不良病例的数据集进行评估,这些病例具有各种风险因素.
主要成果:
- 实现了99%的分类准确度和100%的灵敏度.
- 证明了超低的计算时间 (0.00006秒),表明实时适用性.
- 功能重要性分析确定了久坐不动的习惯和环境暴露作为关键因素.
结论:
- 混合人工智能系统比传统方法提供了更好的可靠性,通用性和效率.
- 该框架提供了可临床解释的结果,帮助医疗保健专业人员.
- 这种方法显示出降低诊断负担的潜力,并使早期,个性化的男性不孕症管理成为可能.
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