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Application of Artificial Neural Network Models to Differentiate Between Complicated and Uncomplicated Acute Appendicitis

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Abstract

Preoperative prediction of complicated appendicitis is challenging, and many clinical tools are developed to predict complicated appendicitis. This study evaluated whether a supervised learning method can recognize complicated appendicitis in emergency department (ED). Consecutive patients with acute appendicitis presenting to the ED were enrolled and included into training and testing datasets at a ratio of 70:30. The multilayer perceptron artificial neural network (ANN) models were trained to perform binary outcome classification between uncomplicated and complicated acute appendicitis. Measures of sensitivity, specificity, positive and negative likelihood ratio (LR + and LR-), and a c statistic of a receiver of operating characteristic curve were used to evaluate an ANN model. The simplest ANN model by Bröker et al. including the C-reactive protein (CRP) and symptom duration as variables achieved a c statistic value of 0.894. The ANN models developed by Avanesov et al. including symptom duration, appendiceal diameter, periappendiceal fluid, extraluminal air, and abscess as variables attained a high diagnostic performance (a c statistic value of 0.949) and good efficiency (sensitivity of 78.6%, specificity of 94.5%, LR + of 14.29, LR- of 0.23 in the testing dataset); and our own model by H.A. Lin et al. including the CRP level, neutrophil-to-lymphocyte ratio, fat-stranding sign, appendicolith, and ascites exhibited high accuracy (c statistic of 0.950) and outstanding efficiency (sensitivity of 85.7%, specificity of 91.7%, LR + of 10.36, LR- of 0.16 in the testing dataset). The ANN models developed by Avanesov et al. and H.A. Lin et al. developed model exhibited a high diagnostic performance.

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Data Availability

The identified and anonymous dataset was used, and data cannot be shared publicly due to legal restrictions imposed by the government of Taiwan on the distribution of the “Personal Information Protection Act.” Data are only available from the formal proposal to Department of Emergency, Taipei Medical University Hospital, Taipei, Taiwan. The contact information was as follows: No. 252, Wuxing St, Xinyi District, Taipei City, 110, Taiwan (R.O.C.)

Abbreviations

ANN:

artificial neural network

AUC:

area under curve

BMI:

body-mass index

CT:

computed tomography

CRP:

C-reactive protein

ED:

emergency department

IDI:

integrated discrimination improvement

MLP:

multilayer perceptron

NLR:

neutrophil-to-lymphocyte ratio

NRI:

net reclassification improvement

OR:

odds ratio

ROC:

Receiver operating characteristic

WBC:

white blood cell

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Acknowledgements

Our work is supported by the staffs in the Department of Emergency Medicine, Taipei Medical University Hospital, Taipei, Taiwan.

Funding

All authors declared no financial or non-financial interests. This study was funded by the Taipei Medical University (reference number: TMU111-AE1-B07). The sponsor had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Author information

Authors and Affiliations

  1. Department of Emergency Medicine, Taipei Medical University Hospital, 250 Wu-Hsing Street, Taipei, Taiwan

    Hui-An Lin & Sheng-Feng Lin

  2. Department of Emergency Medicine, School of Medicine, College of Medicine , Taipei Medical University, Taipei, Taiwan

    Hui-An Lin

  3. Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA

    Li-Tsung Lin

  4. Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, 250 Wu-Hsing Street, Taipei, Taiwan

    Sheng-Feng Lin

  5. School of Public Health, College of Public Health, Taipei Medical University, 250 Wu-Hsing Street, Taipei, Taiwan

    Sheng-Feng Lin

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Contributions

Data curation, investigation, formal analysis were performed by Hui-An Lin and Sheng-Feng Lin. Hui-An Lin, Li-Tsung Lin, and Sheng-Feng Lin wrote the original draft. Hui-An Lin and Sheng-Feng Lin performed the final review and editing of the draft. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sheng-Feng Lin.

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The authors declare that they have no competing interests.

Ethical Approval and Consent to Participate

This study was approved by the Joint Institutional Review Board (IRB) of Taipei Medical University (reference number: N201905057). The requirement of informed consent was waived by the IRB because the data used were anonymous and deidentified. This study was performed in accordance with the Declaration of Helsinki.

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Lin, HA., Lin, LT. & Lin, SF. Application of Artificial Neural Network Models to Differentiate Between Complicated and Uncomplicated Acute Appendicitis. J Med Syst 47, 38 (2023). https://doi.org/10.1007/s10916-023-01932-5

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