[Robots and embodied intelligence in endoscopic transnasal surgery: advances, challenges, and future perspectives] [0.03%]
机器人与内镜经鼻手术：进展、挑战和未来前景
Z H Shi,F X Zhong
Z H Shi
[Transfer learning-based endoscopic image recognition of nasopharyngeal carcinoma: investigating pre-trained large models in small sample settings] [0.03%]
基于迁移学习的鼻咽癌内镜图像识别：小样本下预训练大模型的应用研究
Z Li,X Chen,X L Liu et al.
Z Li et al.
Objective: To develop a nasopharyngeal carcinoma (NPC) diagnostic model based on foundation model transfer learning, aiming to address the limited generalization and diagnostic performance of existing models under small-sample conditions. M...
G X Liang,X Luo,X K Huang et al.
G X Liang et al.
Z X Hua,X Luo,Q T Yang
Z X Hua
[Predicting marker genes and postoperative outcomes in nasal polyps using an artificial intelligence model based on digital pathology and transcriptomics] [0.03%]
基于数字病理学和转录组学的人工智能模型预测鼻息肉标记物基因及术后结局
K H Wang,Y Ren,L Ma et al.
K H Wang et al.
Objective: To evaluate HE2Signature for predicting inflammatory gene expression and postoperative outcomes in chronic rhinosinusitis with nasal polyps (CRSwNP) directly from whole slide images (WSIs). Methods: In an independent external coh...
[Deep learning-based endoscopic diagnosis of nasopharyngeal carcinoma: model development and cloud deployment] [0.03%]
基于深度学习的鼻咽癌内镜诊断模型研发及云端部署
R He,Y D Long,Y H Wen et al.
R He et al.
Objective: To develop a deep learning-assisted diagnostic model based on white light imaging (WLI) and narrow band imaging (NBI) endoscopic images for nasopharyngeal carcinoma (NPC), and to further explore its potential clinical application...
[The advances of artificial intelligence in the diagnosis and treatment of nasopharyngeal carcinoma] [0.03%]
人工智能在鼻咽癌诊疗中应用进展
Z C Zhen,L Lin,Y Sun
Z C Zhen
[Numerical simulation and machine learning analysis of aerosol drug delivery efficiency following Draf Ⅱ-Ⅲ surgery in patients with chronic rhinosinusitis] [0.03%]
[慢性鼻窦炎患者Draf Ⅱ-Ⅲ手术后气溶胶药物递送效率的数值模拟与机器学习分析]
Y S Wang,C F Li,R P Ma et al.
Y S Wang et al.
Objective: To establish a predictive system for aerosol drug deposition by integrating computational fluid dynamics (CFD) and artificial intelligence (AI) modeling, and to propose optimized strategies for intranasal drug delivery in patient...
[Artificial intelligent-based whole slide digital pathology for endotype classification of chronic rhinosinusitis with nasal polyps] [0.03%]
基于人工智能的全片数字化病理在鼻息肉型慢性鼻窦炎内表型分类中的应用研究
Z Z Guo,X Luo,Z X Hua et al.
Z Z Guo et al.
Objective: To investigate the pathological inflammatory features based on artificial intelligence for whole slide image (AI-WSI), and evaluate its consistency and clinical relevance with the conventional mean of ten random high-power fields...