Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register
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 pre...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2021-08-01
|
| Series: | JMIR Medical Informatics |
| Online Access: | https://medinform.jmir.org/2021/8/e25612 |
| _version_ | 1827859090182766592 |
|---|---|
| author | Yang Cao Ingmar Näslund Erik Näslund Johan Ottosson Scott Montgomery Erik Stenberg |
| author_facet | Yang Cao Ingmar Näslund Erik Näslund Johan Ottosson Scott Montgomery Erik Stenberg |
| author_sort | Yang Cao |
| collection | DOAJ |
| description |
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. |
| first_indexed | 2024-03-12T13:03:22Z |
| format | Article |
| id | doaj.art-55273572d2624ebe8c9b7ce19eae566e |
| institution | Directory Open Access Journal |
| issn | 2291-9694 |
| language | English |
| last_indexed | 2024-03-12T13:03:22Z |
| publishDate | 2021-08-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Medical Informatics |
| spelling | doaj.art-55273572d2624ebe8c9b7ce19eae566e2023-08-28T18:32:43ZengJMIR PublicationsJMIR Medical Informatics2291-96942021-08-0198e2561210.2196/25612Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery RegisterYang Caohttps://orcid.org/0000-0002-3552-9153Ingmar Näslundhttps://orcid.org/0000-0002-3644-3319Erik Näslundhttps://orcid.org/0000-0002-0166-6344Johan Ottossonhttps://orcid.org/0000-0002-9243-2390Scott Montgomeryhttps://orcid.org/0000-0001-6328-5494Erik Stenberghttps://orcid.org/0000-0001-9189-0093 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.https://medinform.jmir.org/2021/8/e25612 |
| spellingShingle | Yang Cao Ingmar Näslund Erik Näslund Johan Ottosson Scott Montgomery Erik Stenberg Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register JMIR Medical Informatics |
| title | Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register |
| title_full | Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register |
| title_fullStr | Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register |
| title_full_unstemmed | Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register |
| title_short | Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register |
| title_sort | using a convolutional neural network to predict remission of diabetes after gastric bypass surgery machine learning study from the scandinavian obesity surgery register |
| url | https://medinform.jmir.org/2021/8/e25612 |
| work_keys_str_mv | AT yangcao usingaconvolutionalneuralnetworktopredictremissionofdiabetesaftergastricbypasssurgerymachinelearningstudyfromthescandinavianobesitysurgeryregister AT ingmarnaslund usingaconvolutionalneuralnetworktopredictremissionofdiabetesaftergastricbypasssurgerymachinelearningstudyfromthescandinavianobesitysurgeryregister AT eriknaslund usingaconvolutionalneuralnetworktopredictremissionofdiabetesaftergastricbypasssurgerymachinelearningstudyfromthescandinavianobesitysurgeryregister AT johanottosson usingaconvolutionalneuralnetworktopredictremissionofdiabetesaftergastricbypasssurgerymachinelearningstudyfromthescandinavianobesitysurgeryregister AT scottmontgomery usingaconvolutionalneuralnetworktopredictremissionofdiabetesaftergastricbypasssurgerymachinelearningstudyfromthescandinavianobesitysurgeryregister AT erikstenberg usingaconvolutionalneuralnetworktopredictremissionofdiabetesaftergastricbypasssurgerymachinelearningstudyfromthescandinavianobesitysurgeryregister |