Automated Radiographic Evaluation of Adenoid Hypertrophy Based on VGG-Lite. (November 2021)

• Record Type: Journal Article
• Authors: Liu, J.L.; Li, S.H.; Cai, Y.M.; Lan, D.P.; Lu, Y.F.; Liao, W.; Ying, S.C.; Zhao, Z.H.
• Is Part Of: Journal of dental research. Volume 100:Number 12(2021)
• Page Start: 1337
• Page End: 1343
• Publication Date: 2021-11
• Subjects: orthodontics -- machine learning -- artificial intelligence -- diagnostic imaging -- medical image classification -- convolutional neural networks; Dentistry -- Periodicals; Dentistry -- Social aspects -- Periodicals; Dentistry -- Periodicals; Research -- Periodicals; 617.6005
• DOI: 10.1177/00220345211009474
• Languages: English
• ISSNs: 0022-0345

Abstract

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.