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

Yang Cao,Scott Montgomery,Erik Stenberg,Ingmar Näslund,Johan Ottosson,Erik Näslund

doi:10.2196/25612

Abstract

BackgroundPrediction of diabetes remission is an important topic in the evaluation of patients with type 2 diabetes (T2D) before bariatric surgery. Several high-quality predictive indices are available, but artificial intelligence algorithms offer the potential for higher predictive capability.ObjectiveThis study aimed to construct and validate an artificial intelligence prediction model for diabetes remission after Roux-en-Y gastric bypass surgery.MethodsPatients who underwent surgery from 2007 to 2017 were included in the study, with collection of individual data from the Scandinavian Obesity Surgery Registry (SOReg), the Swedish National Patients Register, the Swedish Prescribed Drugs Register, and Statistics Sweden. A 7-layer convolution neural network (CNN) model was developed using 80% (6446/8057) of patients randomly selected from SOReg and 20% (1611/8057) of patients for external testing. The predictive capability of the CNN model and currently used scores (DiaRem, Ad-DiaRem, DiaBetter, and individualized metabolic surgery) were compared.ResultsIn total, 8057 patients with T2D were included in the study. At 2 years after surgery, 77.09% achieved pharmacological remission (n=6211), while 63.07% (4004/6348) achieved complete remission. The CNN model showed high accuracy for cessation of antidiabetic drugs and complete remission of T2D after gastric bypass surgery. The area under the receiver operating characteristic curve (AUC) for the CNN model for pharmacological remission was 0.85 (95% CI 0.83-0.86) during validation and 0.83 for the final test, which was 9%-12% better than the traditional predictive indices. The AUC for complete remission was 0.83 (95% CI 0.81-0.85) during validation and 0.82 for the final test, which was 9%-11% better than the traditional predictive indices.ConclusionsThe CNN method had better predictive capability compared to traditional indices for diabetes remission. However, further validation is needed in other countries to evaluate its external generalizability.

Highlights

Bariatric surgery is an efficient and safe treatment for patients with morbid obesity and type 2 diabetes (T2D) [1,2]
The convolution neural network (CNN) method had better predictive capability compared to traditional indices for diabetes remission
The inclusion criteria for patients registered in the Scandinavian Obesity Surgery Registry T2D (SOReg) were included those operated on with a primary Roux-en-Y gastric bypass procedure between 2007 and 2015 and those diagnosed with T2D preoperatively, as defined by the American Diabetes Association [21]

Summary

Introduction

Bariatric surgery is an efficient and safe treatment for patients with morbid obesity and type 2 diabetes (T2D) [1,2]. Many of the models based on the scores have high predictive capability and may already provide clinical guidance [11]. These tools might be helpful for personalized management of morbidly obese individuals with diabetes when considering bariatric surgery in routine care, contributing to precision medicine [12]. Previous prediction models were either limited by small sample sizes or were not validated using external data that were not seen by the models during model construction Both the performance and validity of the models or scores need to be further evaluated and improved using a larger bariatric surgery database. Several high-quality predictive indices are available, but artificial intelligence algorithms offer the potential for higher predictive capability

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