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.
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。