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

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Abstract

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.

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Data Availability

Due to privacy and ethical concerns, neither the data nor the source of the data can be made available.

Abbreviations

AE:

Acute epiglottitis

CNN:

Convolutional neural networks

AI:

Artificial intelligence

HE:

Histogram equalization

ROI:

Region of interest (ROI)

ACF:

Aggregate channel feature

CAM:

Class activation mapping

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Author information

Authors and Affiliations

  1. Department of Emergency Medicine, Hsinchu Cathay General Hospital, 30060, Hsinchu City, Taiwan

    Yang-Tse Lin

  2. Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, No. 510, Zhongzheng Road, Xinzhuang District, New Taipei City, 242062, Taiwan

    Ben-Chang Shia

  3. Division of Pediatric Emergency, Department of Pediatrics, MacKay Children’s Hospital and Mackay Memorial Hospital, No. 92, Sec.2, Zhongshan N Road, Taipei City, 10449, Taiwan

    Chia-Jung Chang

  4. Department of Orthopedics, Taipei Municipal Wanfang Hospital, No.111, Sec. 3, Xinglong Rd., Wenshan Dist., Taipei City, 116081, Taiwan

    Yueh Wu

  5. Department of Physical Medicine and Rehabiliation, Taipei Medical University Hospital, No. 252 WuHsing Street, 110, Taipei City, Taiwan

    Jheng-Dao Yang & Jiunn-Horng Kang

  6. Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, No. 250, Wuxing StXinyi Dist., Taipei City, 110301, Taiwan

    Jiunn-Horng Kang

  7. Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, No. 250, Wuxing StXinyi Dist., 110301, Taipei City, Taiwan

    Jiunn-Horng Kang

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Contributions

Yang-Tse Lin, MD: conception and design, acquisition of data, or analysis and interpretation of data.

Ben-Chang Shia, Ph.D.: development of the theoretical formulation

Chia-Jung Chang, MD: conception and design, acquisition of data, or analysis and interpretation of data

Yueh Wu, MD: conception and design, acquisition of data, or analysis and interpretation of data

Jheng-Dao Yang, MD: concept and design, acquisition of data, or analysis and interpretation of data

Jiunn-Horng Kang, MD Ph.D.: development of the theoretical formulation, performed the analytic calculations and performed the numerical simulations, contributed to the final version of the manuscript

All authors provided critical feedback and helped shape the research, analysis, and manuscript.

Corresponding author

Correspondence to Jiunn-Horng Kang.

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Cite this article

Lin, YT., Shia, BC., Chang, CJ. et al. Using Transfer Learning of Convolutional Neural Network on Neck Radiographs to Identify Acute Epiglottitis. J Digit Imaging 36, 893–901 (2023). https://doi.org/10.1007/s10278-023-00774-4

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