Using Transfer Learning of Convolutional Neural Network on Neck Radiographs to Identify Acute Epiglottitis

Acute epiglottitis (AE) is a life-threatening condition and needs to be recognized timely. Diagnosis of AE with a lateral neck radiograph yields poor reliability and sensitivity. Convolutional neural networks (CNN) are powerful tools to assist the analysis of medical images. This study aimed to deve...

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Bibliographic Details
Published in:Journal of digital imaging 2023-06, Vol.36 (3), p.893-901
Main Authors: Lin, Yang-Tse, Shia, Ben-Chang, Chang, Chia-Jung, Wu, Yueh, Yang, Jheng-Dao, Kang, Jiunn-Horng
Format: Article
Language:English
Subjects:
Acute Disease
Artificial Intelligence
Artificial neural networks
Confusion
Deep Learning
Epiglottitis
Epiglottitis - diagnostic imaging
Health care facilities
Humans
Imaging
Learning
Medical imaging
Medicine
Medicine & Public Health
Neck
Neural networks
Neural Networks, Computer
Original Paper
Patients
Radiographs
Radiography
Radiology
Reproducibility of Results
Retrospective Studies
Transfer learning
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Summary:Acute epiglottitis (AE) is a life-threatening condition and needs to be recognized timely. Diagnosis of AE with a lateral neck radiograph yields poor reliability and sensitivity. Convolutional neural networks (CNN) are powerful tools to assist the analysis of medical images. This study aimed to develop an artificial intelligence model using CNN-based transfer learning to identify AE in lateral neck radiographs. All cases in this study are from two hospitals, a medical center, and a local teaching hospital in Taiwan. In this retrospective study, we collected 251 lateral neck radiographs of patients with AE and 936 individuals without AE. Neck radiographs obtained from patients without and with AE were used as the input for model transfer learning in a pre-trained CNN including Inception V3, Densenet201, Resnet101, VGG19, and Inception V2 to select the optimal model. We used five-fold cross-validation to estimate the performance of the selected model. The confusion matrix of the final model was analyzed. We found that Inception V3 yielded the best results as the optimal model among all pre-train models. Based on the average value of the fivefold cross-validation, the confusion metrics were obtained: accuracy = 0.92, precision = 0.94, recall = 0.90, and area under the curve (AUC) = 0.96. Using the Inception V3-based model can provide an excellent performance to identify AE based on radiographic images. We suggest using the CNN-based model which can offer a non-invasive, accurate, and fast diagnostic method for AE in the future.
ISSN:1618-727X
0897-1889
1618-727X
DOI:10.1007/s10278-023-00774-4