Abstract：
OBJECTIVE: Accurate diagnosis and quantification of polyps and symptoms are pivotal for planning the therapeutic strategy of Chronic rhinosinusitis with nasal polyposis (CRSwNP). This pilot study aimed to develop an artificial intelligence (AI)-based image analysis system capable of segmenting nasal polyps from nasal endoscopy videos.
METHODS: Recorded nasal videoendoscopies from 52 patients diagnosed with CRSwNP between 2019 and 2022 were retrospectively analyzed. Images extracted were manually segmented on the web application Roboflow. A dataset of 342 images was generated and divided into training (80%), validation (10%), and testing (10%) sets. The Ultralytics YOLOv8.0.28 model was employed for automated segmentation.
RESULTS: The YOLOv8s-seg model consisted of 195 layers and required 42.4 GFLOPs for operation. When tested against the validation set, the algorithm achieved a precision of 0.91, recall of 0.839, and mean average precision at 50% IoU (mAP50) of 0.949. For the segmentation task, similar metrics were observed, including a mAP ranging from 0.675 to 0.679 for IoUs between 50% and 95%.
CONCLUSIONS: The study shows that a carefully trained AI algorithm can effectively identify and delineate nasal polyps in patients with CRSwNP. Despite certain limitations like the focus on CRSwNP-specific samples, the algorithm presents a promising complementary tool to existing diagnostic methods.
METHODS: Recorded nasal videoendoscopies from 52 patients diagnosed with CRSwNP between 2019 and 2022 were retrospectively analyzed. Images extracted were manually segmented on the web application Roboflow. A dataset of 342 images was generated and divided into training (80%), validation (10%), and testing (10%) sets. The Ultralytics YOLOv8.0.28 model was employed for automated segmentation.
RESULTS: The YOLOv8s-seg model consisted of 195 layers and required 42.4 GFLOPs for operation. When tested against the validation set, the algorithm achieved a precision of 0.91, recall of 0.839, and mean average precision at 50% IoU (mAP50) of 0.949. For the segmentation task, similar metrics were observed, including a mAP ranging from 0.675 to 0.679 for IoUs between 50% and 95%.
CONCLUSIONS: The study shows that a carefully trained AI algorithm can effectively identify and delineate nasal polyps in patients with CRSwNP. Despite certain limitations like the focus on CRSwNP-specific samples, the algorithm presents a promising complementary tool to existing diagnostic methods.
摘要：
目的：息肉和症状的准确诊断和量化对于制定慢性鼻窦炎伴鼻息肉病（CRSwNP）的治疗策略至关重要。这项初步研究旨在开发一种基于人工智能（AI）的图像分析系统，该系统能够从鼻内窥镜检查视频中分割鼻息肉。
方法：回顾性分析2019年至2022年间52例CRSwNP患者的鼻内窥镜检查记录。提取的图像在Web应用程序Roboflow上手动分割。生成了342张图像的数据集，并将其分为训练（80％），验证(10%)，和测试（10％）集。UltralyticsYOLOv8.0.28模型用于自动分割。
结果：YOLOv8s-seg模型由195层组成，需要42.4GFLOP进行操作。当针对验证集进行测试时，该算法的精度为0.91，召回率为0.839，在50％IoU时的平均精度（mAP50）为0.949。对于分段任务，观察到类似的指标，包括50%到95%的IoU的mAP范围从0.675到0.679。
结论：研究表明，经过精心训练的AI算法可以有效地识别和描绘CRSwNP患者的鼻息肉。尽管存在某些限制，例如专注于CRSwNP特定样品，该算法为现有的诊断方法提供了一个有前途的补充工具。
方法：回顾性分析2019年至2022年间52例CRSwNP患者的鼻内窥镜检查记录。提取的图像在Web应用程序Roboflow上手动分割。生成了342张图像的数据集，并将其分为训练（80％），验证(10%)，和测试（10％）集。UltralyticsYOLOv8.0.28模型用于自动分割。
结果：YOLOv8s-seg模型由195层组成，需要42.4GFLOP进行操作。当针对验证集进行测试时，该算法的精度为0.91，召回率为0.839，在50％IoU时的平均精度（mAP50）为0.949。对于分段任务，观察到类似的指标，包括50%到95%的IoU的mAP范围从0.675到0.679。
结论：研究表明，经过精心训练的AI算法可以有效地识别和描绘CRSwNP患者的鼻息肉。尽管存在某些限制，例如专注于CRSwNP特定样品，该算法为现有的诊断方法提供了一个有前途的补充工具。
