State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, Sichuan, China.
J Dent Res. 2021 Nov;100(12):1337-1343. doi: 10.1177/00220345211009474. Epub 2021 Apr 29.
Adenoid hypertrophy is a pathological hyperplasia of the adenoids, which may cause snoring and apnea, as well as impede breathing during sleep. The lateral cephalogram is commonly used by dentists to screen for adenoid hypertrophy, but it is tedious and time-consuming to measure the ratio of adenoid width to nasopharyngeal width for adenoid assessment. The purpose of this study was to develop a screening tool to automatically evaluate adenoid hypertrophy from lateral cephalograms using deep learning. We proposed the deep learning model VGG-Lite, using the largest data set (1,023 X-ray images) yet described to support the automatic detection of adenoid hypertrophy. We demonstrated that our model was able to automatically evaluate adenoid hypertrophy with a sensitivity of 0.898, a specificity of 0.882, positive predictive value of 0.880, negative predictive value of 0.900, and F1 score of 0.889. The comparison of model-only and expert-only detection performance showed that the fully automatic method (0.07 min) was about 522 times faster than the human expert (36.6 min). Comparison of human experts with or without deep learning assistance showed that model-assisted human experts spent an average of 23.3 min to evaluate adenoid hypertrophy using 100 radiographs, compared to an average of 36.6 min using an entirely manual procedure. We therefore concluded that deep learning could improve the accuracy, speed, and efficiency of evaluating adenoid hypertrophy from lateral cephalograms.
腺样体肥大是腺样体的一种病理性增生，可导致打鼾和呼吸暂停，并在睡眠期间阻碍呼吸。牙医通常使用侧颅位片来筛查腺样体肥大，但测量腺样体宽度与鼻咽宽度的比值以评估腺样体比较繁琐和耗时。本研究旨在开发一种使用深度学习自动从侧颅位片中评估腺样体肥大的筛查工具。我们提出了深度学习模型 VGG-Lite，使用了迄今为止描述的最大数据集（1023 张 X 光片）来支持腺样体肥大的自动检测。我们证明，我们的模型能够以 0.898 的灵敏度、0.882 的特异性、0.880 的阳性预测值、0.900 的阴性预测值和 0.889 的 F1 评分自动评估腺样体肥大。模型仅和专家仅检测性能的比较表明，全自动方法（0.07 分钟）比人类专家（36.6 分钟）快约 522 倍。比较有或没有深度学习辅助的人类专家发现，使用 100 张射线照片，模型辅助的人类专家平均花费 23.3 分钟评估腺样体肥大，而使用完全手动程序则平均花费 36.6 分钟。因此，我们得出结论，深度学习可以提高从侧颅位片中评估腺样体肥大的准确性、速度和效率。
