Automated Radiographic Evaluation of Adenoid Hypertrophy Based on VGG-Lite
| Autor: | Y M Cai, D P Lan, Jialing Liu, S C Ying, Zhihe Zhao, Y F Lu, Wen Liao, Sheng-wei Li |
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| Rok vydání: | 2021 |
| Předmět: |
Radiography
02 engineering and technology 03 medical and health sciences 0302 clinical medicine Nasopharynx otorhinolaryngologic diseases 0202 electrical engineering electronic engineering information engineering Medical imaging Humans Medicine General Dentistry Pathological Orthodontics business.industry Lateral cephalograms Apnea Hypertrophy 030206 dentistry Hyperplasia medicine.disease stomatognathic diseases Adenoids Breathing 020201 artificial intelligence & image processing medicine.symptom business Adenoid hypertrophy |
| Zdroj: | Journal of Dental Research. 100:1337-1343 |
| ISSN: | 1544-0591 0022-0345 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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