TRAITER:使用细胞核形态和DNA损伤标志物的变压器引导心力衰竭诊断和预后
Hiromu Hayashi1, Toshiyuki Ko2,3, Zhehao Dai2
1Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka 820-8502, Fukuoka, Japan.
Bioinformatics (Oxford, England)
|October 16, 2024
概括
一种新的深度学习方法,TRAITER,准确诊断心力衰竭 (HF),并使用心脏组织图像预测左心室逆转型 (LVRR),推进个性化HF医学.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 心力衰竭 (HF) 是疾病和死亡的重要原因,需要改进诊断和预后工具.
- 准确预测HF进展和治疗反应,如左心室逆向重塑 (LVRR),对于患者管理至关重要.
研究的目的:
- 引入一种新的深度学习方法,TRAITER,用于HF的诊断和预后.
- 用心脏组织细胞形态和DNA损伤标记物来评估TRAITER在预测高风险风险和LVRR潜力的表现.
主要方法:
- TRAITER使用图像分割和视觉转换器架构.
- 该模型分析心脏组织细胞核形态,用于HF预测.
- 双染色图像,包括细胞核和DNA损伤标记,用于LVRR预测.
主要成果:
- 在使用来自9名患者的31,158张图像的HF预测中,TRAITER实现了83.1%的准确性.
- 对于LVRR预测,TRAITER在46名患者的231,840张图像中获得了个体图像的84.2%准确率和个体患者的92.9%准确率.
- 与其他基于接收器运行特征和精度回忆曲线的神经网络模型相比,TRAITER表现出卓越的性能.
结论:
- 开发的TRAITER方法在诊断HF和预测LVRR方面表现出很高的准确性.
- 这种深度学习方法有可能增强心力衰竭医学中的个性化决策.
- 这项研究为人工智能驱动的心血管诊断和预后的进一步进展提供了基础.
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