Lai Shixin, Kang Weipiao, Chen Yaowen, Zou Jisheng, Wang Siqi, Zhang Xuan, Zhang Xiaolei, Lin Yu
College of Engineering Shantou University, Shantou 515063, China.
Department of Otolaryngology Second Affiliated Hospital of Shantou University Medical College, Shantou 515041, China.
Int J Biomed Imaging. 2024 Jun 6;2024:4960630. doi: 10.1155/2024/4960630. eCollection 2024.
Chronic rhinosinusitis (CRS) is a global disease characterized by poor treatment outcomes and high recurrence rates, significantly affecting patients' quality of life. Due to its complex pathophysiology and diverse clinical presentations, CRS is categorized into various subtypes to facilitate more precise diagnosis, treatment, and prognosis prediction. Among these, CRS with nasal polyps (CRSwNP) is further divided into eosinophilic CRSwNP (eCRSwNP) and noneosinophilic CRSwNP (non-eCRSwNP). However, there is a lack of precise predictive diagnostic and treatment methods, making research into accurate diagnostic techniques for CRSwNP endotypes crucial for achieving precision medicine in CRSwNP. This paper proposes a method using multiangle sinus computed tomography (CT) images combined with artificial intelligence (AI) to predict CRSwNP endotypes, distinguishing between patients with eCRSwNP and non-eCRSwNP. The considered dataset comprises 22,265 CT images from 192 CRSwNP patients, including 13,203 images from non-eCRSwNP patients and 9,062 images from eCRSwNP patients. Test results from the network model demonstrate that multiangle images provide more useful information for the network, achieving an accuracy of 98.43%, precision of 98.1%, recall of 98.1%, specificity of 98.7%, and an AUC value of 0.984. Compared to the limited learning capacity of single-channel neural networks, our proposed multichannel feature adaptive fusion model captures multiscale spatial features, enhancing the model's focus on crucial sinus information within the CT images to maximize detection accuracy. This deep learning-based diagnostic model for CRSwNP endotypes offers excellent classification performance, providing a noninvasive method for accurately predicting CRSwNP endotypes before treatment and paving the way for precision medicine in the new era of CRSwNP.
慢性鼻-鼻窦炎（CRS）是一种全球性疾病，其特点是治疗效果不佳且复发率高，严重影响患者的生活质量。由于其复杂的病理生理学和多样的临床表现，CRS被分为多种亚型，以利于更精确的诊断、治疗和预后预测。其中，伴鼻息肉的CRS（CRSwNP）又进一步分为嗜酸性粒细胞性CRSwNP（eCRSwNP）和非嗜酸性粒细胞性CRSwNP（非eCRSwNP）。然而，目前缺乏精确的预测诊断和治疗方法，因此研究CRSwNP内型的准确诊断技术对于在CRSwNP中实现精准医疗至关重要。本文提出一种利用多角度鼻窦计算机断层扫描（CT）图像结合人工智能（AI）来预测CRSwNP内型的方法，以区分eCRSwNP患者和非eCRSwNP患者。所考虑的数据集包括来自192例CRSwNP患者的22265张CT图像，其中包括来自非eCRSwNP患者的13203张图像和来自eCRSwNP患者的9062张图像。网络模型的测试结果表明，多角度图像为网络提供了更多有用信息，准确率达到98.43%，精确率为98.1%，召回率为98.1%，特异性为98.7%，AUC值为0.984。与单通道神经网络有限的学习能力相比，我们提出的多通道特征自适应融合模型能够捕捉多尺度空间特征，增强模型对CT图像中关键鼻窦信息的关注，以最大限度地提高检测准确率。这种基于深度学习的CRSwNP内型诊断模型具有出色的分类性能，为治疗前准确预测CRSwNP内型提供了一种非侵入性方法，为CRSwNP新时代的精准医疗铺平了道路。
