This paper is in the following e-collection/theme issue:

Machine Learning (1473) Artificial Intelligence in Diabetes Care and Prevention (29) Decision Support for Health Professionals (1191) Clinical Information and Decision Making (1413) Longitudinal and Cohort Studies in Public Health (310) Artificial Intelligence in Surgery and Perioperative Medicine (27)

Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2020.11.03.20224956v1, first published .
Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register

Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register

Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register

Authors of this article:

Yang Cao1, 2 Author Orcid Image ;   Ingmar Näslund3 Author Orcid Image ;   Erik Näslund4 Author Orcid Image ;   Johan Ottosson3 Author Orcid Image ;   Scott Montgomery1, 5, 6 Author Orcid Image ;   Erik Stenberg3 Author Orcid Image
Article Authors Cited by (10) Tweetations (2) Metrics

Journals

  1. Wise E, Leslie D, Amateau S, Hocking K, Scott A, Dutta N, Ikramuddin S. Prediction of thirty-day morbidity and mortality after duodenal switch using an artificial neural network. Surgical Endoscopy 2023;37(2):1440 View
  2. Sundbom M, Näslund I, Ottosson J, Stenberg E, Näslund E. Results from the Scandinavian Obesity Surgery Registry: A narrative review. Obesity Reviews 2024;25(2) View
  3. Al Sadi K, Balachandran W. Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman. Bioengineering 2023;10(12):1420 View
  4. Şişik A, Dalkılıç M, Gençtürk M, Yılmaz M, Erdem H. Individualized Bariatric Surgery Utilizing Artificial Intelligence: A Call to Colleagues and New Year’s Aspiration. Obesity Surgery 2024;34(4):1380 View
  5. Scott A, Amateau S, Leslie D, Ikramuddin S, Wise E. Prediction of 30-Day Morbidity and Mortality After Conversion of Sleeve Gastrectomy to Roux-en-Y Gastric Bypass: Use of an Artificial Neural Network. The American Surgeon™ 2024;90(6):1202 View
  6. Byrne C, Targher G. MASLD, MAFLD, or NAFLD criteria: have we re-created the confusion and acrimony surrounding metabolic syndrome?. Metabolism and Target Organ Damage 2024;4(2) View
  7. Brydges G, Uppal A, Gottumukkala V. Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians. Current Oncology 2024;31(5):2727 View
  8. Punthakee Z, Hall S, McInnes N, Sherifali D, Tsiplova K, Kirabo F, Ransom T, Harris S, Lochnan H, Sigal R, Ghosh M, Spaic T, Gerstein H. Evaluating remission of type 2 diabetes using a metabolic intervention including fixed‐ratio insulin degludec and liraglutide: A randomized controlled trial. Diabetes, Obesity and Metabolism 2024;26(12):5600 View
  9. Nopour R. Comparison of machine learning models to predict complications of bariatric surgery: A systematic review. Health Informatics Journal 2024;30(3) View
  10. Cacciari I, Ranfagni A. Hands-On Fundamentals of 1D Convolutional Neural Networks—A Tutorial for Beginner Users. Applied Sciences 2024;14(18):8500 View