设计和开发物联网和基于深度集体学习的模型,用于疾病监测和预测
Mareeswari Venkatachala Appa Swamy1, Jayalakshmi Periyasamy1, Muthamilselvan Thangavel1
1School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.
Diagnostics (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种使用集体深度学习和物联网设备的AI诊断模型,用于从医疗图像中早期检测疾病. 它旨在通过提供个性化的治疗见解和准确的诊断来改善医疗保健.
科学领域:
- 医疗保健中的人工智能
- 用于医学诊断的深度学习
- 物联网 (IoT) 在医学领域
背景情况:
- 医疗保健越来越依赖于人工智能 (AI) 和物联网 (IoT) 等先进技术.
- 人工智能辅助诊断模型在早期疾病检测和治疗规划方面表现优于人类智能.
- 深度学习 (DL) 算法擅长分析复杂的医疗数据,识别隐藏的模式,并实现精确的疾病分类.
研究的目的:
- 为早期疾病检测设计和开发人工智能辅助的诊断模型.
- 利用集体深度学习算法和物联网设备来分析医疗大数据.
- 识别医疗图像中的异常,以便及时诊断和个性化治疗建议.
主要方法:
- 集体深度学习算法的部署.
- 整合物联网 (IoT) 设备用于数据采集.
- 对医疗大数据的分析,特别关注用于检测异常的医疗图像.
主要成果:
- 开发的AI模型有效地分析医疗大数据,以在早期诊断疾病.
- 该模型可以识别医疗图像中的异常,从而促进及时干预.
- 集成深度学习将基准模型的预测汇总在一起,以获得最终的,准确的诊断.
结论:
- 基于集体深度学习的AI辅助诊断模型是医疗保健系统和患者的宝贵工具.
- 通过这种模式的早期疾病诊断有助于个性化治疗策略.
- 该研究强调了人工智能和物联网在降低医疗保健成本和改善患者治疗结果方面的潜力.
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