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...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 893 - 902 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164473101&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473101 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473101 161385506 164473101 164473101 10.1007/s10278-023-00774-4 164473101 ppf: 893 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Using Transfer Learning of Convolutional Neural Network on Neck Radiographs to Identify Acute Epiglottitis. aug: au: Lin, Yang-Tse Shia, Ben-Chang Chang, Chia-Jung Wu, Yueh Yang, Jheng-Dao Kang, Jiunn-Horng affil: Department of Emergency Medicine, Hsinchu Cathay General Hospital, 30060, Hsinchu City, Taiwan sug: subj: Epiglottitis Diagnosis Acute Disease Diagnosis Convolutional Neural Networks Utilization Artificial Intelligence Neck Radiography Human Retrospective Design Descriptive Statistics Data Analysis Software Emergency Medicine Male Female Young Adult Adult Middle Age Aged Aged, 80 and Over Algorithms Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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