白血病干细胞及其正常干细胞对应物通过人工智能在形态上是可区分的
Dongguang Li1, Ngoc DeSouza1, Kathy Nguyen1
1Division of Hematology/Oncology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA.
人工智能 (AI) 深度学习现在可以使用独特的形态特征将罕见白血病干细胞 (LSC) 与正常细胞区分开来. 这一突破使得精确的LSC识别能够改善癌症治疗评估和预后.
科学领域:
- 血液学 血液学 血液学
- 人工智能的人工智能
- 细胞生物学 细胞生物学
背景情况:
- 白血病干细胞 (LSCs) 驱动血液瘤的疾病发病和治疗抵抗.
- 由于形态特征不明确,目前的方法缺乏精确识别和量化LSC的能力.
- 准确的LSC识别对于指导治疗和评估预后至关重要.
研究的目的:
- 开发一种人工智能驱动的方法来区分LSC和正常干细胞.
- 为了证明LSCs具有独特的,AI可检测的形态特征.
- 在识别原始白血病细胞方面建立基于人工智能的形态学的基础.
主要方法:
- 利用人工智能 (AI) 深度学习,特别是19个卷积神经网络的组合.
- 开发了人工智能模型来分析来自多细胞真菌 (PV) 鼠标模型的单细胞图像.
- 将人工智能分析与病理学家的传统显微镜可视化进行比较.
主要成果:
- 人工智能模型准确地将LSC与正常干细胞区分开来,准确度>99%.
- 人工智能识别了人类病理学家无法察觉的LSCs独特的形态特征.
- 人工智能还可以在光伏小鼠中区分LSC与其他细胞系.
结论:
- 人工智能深度学习可以识别LSC的独特形态特征.
- 基于人工智能的形态学为识别原始白血病细胞提供了一种新的方法.
- 这项技术具有评估治疗反应和白血病疾病预后的潜力.
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