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Prediction of pouchitis after ileal pouch–anal anastomosis in patients with ulcerative colitis using artificial intelligence and deep learning

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Abstract

Background

Pouchitis is one of the major postoperative complications of ulcerative colitis (UC), and it is still difficult to predict the development of pouchitis after ileal pouch–anal anastomosis (IPAA) in UC patients. In this study, we examined whether a deep learning (DL) model could predict the development of pouchitis.

Methods

UC patients who underwent two-stage restorative proctocolectomy with IPAA at Keio University Hospital were included in this retrospective analysis. The modified pouchitis disease activity index (mPDAI) was evaluated by the clinical and endoscopic findings. Pouchitis was defined as an mPDAI ≥ 5.860; endoscopic pouch images before ileostomy closure were collected. A convolutional neural network was used as the DL model, and the prediction rates of pouchitis after ileostomy closure were evaluated by fivefold cross-validation.

Results

A total of 43 patients were included (24 males and 19 females, mean age 39.2 ± 13.2 years). Pouchitis occurred in 14 (33%) patients after ileostomy closure. In less than half of the patients, mPDAI scores matched before and after ileostomy closure. Most of patients whose mPDAI scores did not match before and after ileostomy closure had worse mPDAI scores after than before. The prediction rate of pouchitis calculated by the area under the curve using the DL model was 84%. Conversely, the prediction rate of pouchitis using mPDAI before ileostomy closure was 62%.

Conclusion

The prediction rate of pouchitis using the DL model was more than 20% higher than that using mPDAI, suggesting the utility of the DL model as a prediction model for the development of pouchitis. It could also be used to determine early interventions for pouchitis.

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Availability of data and material

Due to the sensitive nature of the questions asked in this study, survey respondents were assured raw data would remain confidential and would not be shared.

Code availability

Not applicable.

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Acknowledgements

The English in this document has been checked by a professional editor who is a native speaker of English.

Funding

The authors state no grant support or financial relationships.

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Authors and Affiliations

  1. Department of Surgery, Keio University School of Medicine, 35 Shinano-machi, Shinjuku-ku, Tokyo, 160-8582, Japan

    S. Mizuno, K. Okabayashi, S. Matsui, R. Seishima, K. Shigeta & Y. Kitagawa

  2. Department of Surgery, Saitama Medical Center, Saitama, Japan

    A. Ikebata

Authors
  1. S. Mizuno
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  2. K. Okabayashi
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  3. A. Ikebata
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  4. S. Matsui
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  5. R. Seishima
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  6. K. Shigeta
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  7. Y. Kitagawa
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Contributions

SM: conceptualization, methodology, writing-original draft. KO: conceptualization, methodology, data curation, supervision, writing, review and editing. AI: data curation, supervision. SM: data curation, supervision. RS: data curation, supervision. KS: data curation, supervision. YK: project administration. All authors read and approved the final manuscript.

Corresponding author

Correspondence to K. Okabayashi.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

The study was approved by Keio University’s Ethics Committee (20150051).

Informed consent

All patients signed the institution informed consent for colorectal surgery. No specific consent for this type of study is required.

Consent for publication

Consent to submit the present paper has been received explicitly from all co-authors, as well as from Keio University’s Ethics Committee.

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Mizuno, S., Okabayashi, K., Ikebata, A. et al. Prediction of pouchitis after ileal pouch–anal anastomosis in patients with ulcerative colitis using artificial intelligence and deep learning. Tech Coloproctol 26, 471–478 (2022). https://doi.org/10.1007/s10151-022-02602-3

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  • DOI: https://doi.org/10.1007/s10151-022-02602-3

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