A multimodal neural network that distinguishes between type 1 and type 2 diabetes in young persons using MRI and clinical data
Notice bibliographique
Résumé
Early diabetic kidney disease (DKD) is common in young persons with type 1 (T1D) and type 2 diabetes (T2D) and accentuates their lifetime risk of kidney failure, requiring dialysis or a kidney transplant. Although clinical manifestations of DKD are similar in T1D and T2D, the structural lesions may differ, and it remains unclear whether DKD in T1D and T2D represent distinct diseases. Accordingly, the objective of this study is to build machine learning (ML) models using clinical and kidney MRI data to classify diabetes status, as well as structural and functional kidney differences of individuals with T1D versus T2D. We hypothesize that a highly accurate multimodal neural network can be constructed that integrates clinical and functional kidney MRI images.Data were obtained from several studies at University of Colorado in youth with T1D (n=102), T2D (n=91) as well as non-diabetic controls (n=60). A total of 253 participants were included in the analyses. First, we applied to clinical data (CASPER, IMPROVE-T2D, CROCODILE, and RENAL-HEIR trials) logistic regression and 7 ML models: extreme gradient boosting machine (XGBoost), XGBoost with grid search, k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, and a 3-layer neural network (NN-EHR). When a small subset of the clinical data was used as features, the NN-EHR yielded the highest accuracy of 84%. Next, we considered the MRI images (27,000 images from the 253 individuals). We applied three convoluted NN to perform the classification: AlexNet, VGG16, and a 4-layer Neural Network for Diabetes Detection (NN4DD). Considering the images alone, VGG16 and NN4DD both achieved an accuracy of > 80%. Additionally, by integrating the clinical data and the MRI images, the fusion neural network achieved an accuracy of almost 100%. Finally, an interpretability analysis of NN4DD indicated notable differences in kidney structure and function among the three groups.Although the cost of MRI is prohibitive and thus impractical for diabetes diagnosis, these results provide a proof-of-concept that fused neural networks that integrate multimodal data can be a valuable diagnostic tool, and provide structural and functional insight on kidney differences between T1D and T2D. This research is sponsored in part by the Natural Sciences and Engineering Council (Canada) and the National Institutes of Health (USA). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».