临床实验室环境中的深度学习算法和拉曼光谱
Charlotte Delrue1, Marijn M Speeckaert1,2, Sander De Bruyne3,4
1Department of Nephrology, Ghent University Hospital, Ghent, Belgium.
Critical reviews in clinical laboratory sciences
|September 1, 2025
概括
深度学习 (DL) 通过自动化光谱分析来增强拉曼光谱用于疾病诊断. 这种整合提高了癌症和细菌感染等疾病的确切性,
科学领域:
- 生物医学光谱学
- 计算生物学
- 医疗诊断
背景情况:
- 拉曼光谱为疾病诊断提供生物样本的分子见解.
- 临床使用受到复杂的光谱解释挑战的阻碍.
研究的目的:
- 探索深度学习与拉曼光谱的医疗应用.
- 审查DL模型及其在提高生物样本的诊断准确性方面的作用.
主要方法:
- 使用关键的DL模型:卷积神经网络 (CNN),长期短期记忆 (LSTM) 和生成对抗网络 (GAN).
- 应用DL用于拉曼光谱实验中的自动光谱特征采集,无声化和分类.
主要成果:
- DL与拉曼光谱的整合在癌症诊断中显示出显著的前景.
- 在细菌识别和病毒诊断方面取得了成功.
- DL模型有效提取相关特征并增强诊断价值.
结论:
- 深度学习提供了一种强大的方法来自动化和改进用于医学诊断的拉曼光谱分析.
- DL和拉曼光谱的结合为非侵入性和可靠的疾病检测提供了一个独特的策略.
- 进一步探索DL架构可以解锁更广泛的临床应用.
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