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Dipesh Gyawali¹, Jonathan Bidwell¹, Sejal Shyam Bhatia¹

¹Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.

International forum of allergy & rhinology

|August 29, 2025

概括

一个深度学习模型客观地分类鼻. 这项技术有助于在鼻腔内镜检测鼻腔炎症.

科学领域:

耳鼻喉科
医疗人工智能
计算病理学

背景情况:

鼻腔内镜对于诊断鼻腔疾病至关重要.
在鼻疾病中缺乏客观的粘液评估.
目前的粘液评估依赖于主观的解释.

研究的目的:

开发和验证粘液形态分类的深度学习模型.
评估模型在细分和分类不同类型的粘液中的性能.
探索人工智能驱动的粘液分析用于鼻腔炎症检测的实用性.

主要方法:

使用预训练的深度学习模型进行图像细分和分类.
将该模型应用于鼻腔内镜检查中获取的粘液样本.
对各种粘液形态的模型性能进行了评估.

主要成果:

深度学习模型在细分和分类粘液形态方面表现出强的表现.
能够对粘液类型进行客观分类.
模型的发现与鼻腔炎的存在有关.

结论:

人工智能支持的粘液形态的客观分类是可行的.
这种方法为鼻腔炎的检测提供了一个新的工具.
这项研究开创了鼻腔环境中的客观粘液形态分类.

关键词:

人工智能深度学习诊断情况粘液鼻子内镜鼻炎

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