Zhang Ning, Ma Ruixia, Ren Hailing, Shen Xueliang, He Jiao, Zhao Yutong, Yang Fengxia, Liu Ming, Wang Le, Zhang Yuqiao, Zeng Zhiling
Yinchuan First People's Hospital，Yinchuan，750004，China.
The Second Clinical College of Ningxia Medical University.
Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2024 Jul;38(7):624-630. doi: 10.13201/j.issn.2096-7993.2024.07.013.
To build a VGG-based computer-aided diagnostic model for chronic sinusitis and evaluate its efficacy. ①A total of 5 000 frames of diagnosed sinus CT images were collected. The normal group consisted of 1 000 frames（250 frames each of maxillary sinus, frontal sinus, septal sinus, and pterygoid sinus）, while the abnormal group consisted of 4 000 frames（1 000 frames each of maxillary sinusitis, frontal sinusitis, septal sinusitis, and pterygoid sinusitis）. ②The models were trained and simulated to obtain five classification models for the normal group, the pteroid sinusitis group, the frontal sinusitis group, the septal sinusitis group and the maxillary sinusitis group, respectively. The classification efficacy of the models was evaluated objectively in six dimensions: accuracy, precision, sensitivity, specificity, interpretation time and area under the ROC curve（AUC）. ③Two hundred randomly selected images were read by the model with three groups of physicians（low, middle and high seniority） to constitute a comparative experiment. The efficacy of the model was objectively evaluated using the aforementioned evaluation indexes in conjunction with clinical analysis. ①Simulation experiment: The overall recognition accuracy of the model is 83.94%, with a precision of 89.52%, sensitivity of 83.94%, specificity of 95.99%, and the average interpretation time of each frame is 0.2 s. The AUC for sphenoid sinusitis was 0.865（95% 0.849-0.881）, for frontal sinusitis was 0.924（0.991-0.936）, for ethmoidoid sinusitis was 0.895（0.880-0.909）, and for maxillary sinusitis was 0.974（0.967-0.982）. ②Comparison experiment: In terms of recognition accuracy, the model was 84.52%, while the low-seniority physicians group was 78.50%, the middle-seniority physicians group was 80.50%, and the seniority physicians group was 83.50%; In terms of recognition accuracy, the model was 85.67%, the low seniority physicians group was 79.72%, the middle seniority physicians group was 82.67%, and the high seniority physicians group was 83.66%. In terms of recognition sensitivity, the model was 84.52%, the low seniority group was 78.50%, the middle seniority group was 80.50%, and the high seniority group was 83.50%. In terms of recognition specificity, the model was 96.58%, the low-seniority physicians group was 94.63%, the middle-seniority physicians group was 95.13%, and the seniority physicians group was 95.88%. In terms of time consumption, the average image per frame of the model is 0.20 s, the average image per frame of the low-seniority physicians group is 2.35 s, the average image per frame of the middle-seniority physicians group is 1.98 s, and the average image per frame of the senior physicians group is 2.19 s. This study demonstrates the potential of a deep learning-based artificial intelligence diagnostic model for chronic sinusitis to classify and diagnose chronic sinusitis; the deep learning-based artificial intelligence diagnosis model for chronic sinusitis has good classification performance and high diagnostic efficacy.
构建基于VGG的慢性鼻窦炎计算机辅助诊断模型并评估其疗效。①共收集5000帧已诊断的鼻窦CT图像。正常组由1000帧图像组成（上颌窦、额窦、筛窦和蝶窦各250帧），而异常组由4000帧图像组成（上颌窦炎、额窦炎、筛窦炎和蝶窦炎各1000帧）。②对模型进行训练和模拟，分别获得正常组、蝶窦炎组、额窦炎组、筛窦炎组和上颌窦炎组的五个分类模型。从准确性、精确性、敏感性、特异性、解读时间和ROC曲线下面积（AUC）六个维度对模型的分类疗效进行客观评估。③由三组医生（低年资、中年资和高年资）使用该模型读取随机选取的200幅图像，构成对比实验。结合临床分析，使用上述评估指标对模型的疗效进行客观评估。①模拟实验：模型的总体识别准确率为83.94%，精确率为89.52%，敏感性为83.94%，特异性为95.99%，每帧的平均解读时间为0.2秒。蝶窦炎的AUC为0.865（95%可信区间0.849 - 0.881），额窦炎为0.924（0.91 - 0.936），筛窦炎为0.895（0.880 - 0.909），上颌窦炎为0.974（0.967 - 0.982）。②对比实验：在识别准确率方面，模型为84.52%，低年资医生组为78.50%，中年资医生组为80.50%，高年资医生组为83.50%；在识别精确率方面，模型为85.67%，低年资医生组为79.72%，中年资医生组为82.67%，高年资医生组为83.66%。在识别敏感性方面，模型为84.52%，低年资组为78.50%，中年资组为80.50%，高年资组为83.50%。在识别特异性方面，模型为96.58%，低年资医生组为94.63%，中年资医生组为95.13%，高年资医生组为95.88%。在时间消耗方面，模型每帧图像平均用时0.20秒，低年资医生组每帧图像平均用时2.35秒，中年资医生组每帧图像平均用时1.98秒，高年资医生组每帧图像平均用时2.19秒。本研究证明了基于深度学习的人工智能诊断模型对慢性鼻窦炎进行分类和诊断的潜力；基于深度学习的慢性鼻窦炎人工智能诊断模型具有良好的分类性能和较高的诊断效能。
