scholarly journals A Deep Learning Model for Screening Type 2 Diabetes from Retinal Photographs

2022 ◽  
Author(s):  
Jae-Seung Yun ◽  
Jaesik Kim ◽  
Sang-Hyuk Jung ◽  
Seon-Ah Cha ◽  
Seung-Hyun Ko ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Learning Model ◽  
Retinal Photographs ◽  
Deep Learning Model

scholarly journals A Deep Learning Model for Screening Type 2 Diabetes from Retinal Photographs

2021 ◽  
Author(s):  
Jae-Seung Yun ◽  
Jaesik Kim ◽  
Sang-Hyuk Jung ◽  
Seon-Ah Cha ◽  
Seung-Hyun Ko ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Model ◽  
Retinal Images ◽  
Uk Biobank ◽  
Non Invasive ◽  
Deep Learning Algorithm ◽  
Deep Learning Model

Objective: We aimed to develop and evaluate a non-invasive deep learning algorithm for screening type 2 diabetes in UK Biobank participants using retinal images. Research Design and Methods: The deep learning model for prediction of type 2 diabetes was trained on retinal images from 50,077 UK Biobank participants and tested on 12,185 participants. We evaluated its performance in terms of predicting traditional risk factors (TRFs) and genetic risk for diabetes. Next, we compared the performance of three models in predicting type 2 diabetes using 1) an image-only deep learning algorithm, 2) TRFs, 3) the combination of the algorithm and TRFs. Assessing net reclassification improvement (NRI) allowed quantification of the improvement afforded by adding the algorithm to the TRF model. Results: When predicting TRFs with the deep learning algorithm, the areas under the curve (AUCs) obtained with the validation set for age, sex, and HbA1c status were 0.931 (0.928-0.934), 0.933 (0.929-0.936), and 0.734 (0.715-0.752), respectively. When predicting type 2 diabetes, the AUC of the composite logistic model using non-invasive TRFs was 0.810 (0.790-0.830), and that for the deep learning model using only fundus images was 0.731 (0.707-0.756). Upon addition of TRFs to the deep learning algorithm, discriminative performance was improved to 0.844 (0.826-0.861). The addition of the algorithm to the TRFs model improved risk stratification with an overall NRI of 50.8%. Conclusions: Our results demonstrate that this deep learning algorithm can be a useful tool for stratifying individuals at high risk of type 2 diabetes in the general population.

309-OR: Deep Learning Model of Pancreatic Islet Epigenome Refines Association Signals at Type 2 Diabetes Susceptibility Loci

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 309-OR
Author(s):  
AGATA WESOLOWSKA-ANDERSEN ◽  
MATTHIAS THURNER ◽  
ANUBHA MAHAJAN ◽  
FERNANDO ABAITUA ◽  
JASON TORRES ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Pancreatic Islet ◽  
Learning Model ◽  
Susceptibility Loci ◽  
Diabetes Susceptibility ◽  
Deep Learning Model

352-P: Assessment of a Deep Learning Model Based on Three-Year Longitudinal Electronic Health Record Data for Predicting Severe Hypoglycemia in Patients with Type 2 Diabetes

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 352-P
Author(s):  
GARAM LEE ◽  
SEUNG-HYUN KO ◽  
YU-BAE AHN ◽  
EUN YOUNG LEE ◽  
SEON-AH CHA ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Electronic Health Record ◽  
Severe Hypoglycemia ◽  
Learning Model ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Deep Learning Model

scholarly journals Estimating body fat distribution - a driver of cardiometabolic health - from silhouette images

2022 ◽  
Author(s):  
Marcus D.R. Klarqvist ◽  
Saaket Agrawal ◽  
Nathaniel Diamant ◽  
Patrick T. Ellinor ◽  
Anthony Philippakis ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Fat Distribution ◽  
Accurate Estimation ◽  
Comparator Model ◽  
Artery Disease ◽  
Deep Learning Model

Background: Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice because quantification requires medical imaging. Objectives: We hypothesized that a deep learning model trained on an individual's body shape outline - or silhouette - would enable accurate estimation of specific fat depots, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT/ASAT ratio. We additionally set out to study whether silhouette-estimated VAT/ASAT ratio may stratify risk of cardiometabolic diseases independent of body mass index (BMI) and waist circumference. Methods: Two-dimensional coronal and sagittal silhouettes were constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used to train a convolutional neural network to predict VAT, ASAT, and GFAT volumes, and VAT/ASAT ratio. Logistic and Cox regressions were used to determine the independent association of silhouette-predicted VAT/ASAT ratio with type 2 diabetes and coronary artery disease. Results: Mean age of the study participants was 65 years and 51% were female. A deep learning model trained on silhouettes enabled accurate estimation of VAT, ASAT, and GFAT volumes (R2: 0.88, 0.93, and 0.93, respectively), outperforming a comparator model combining anthropometric and bioimpedance measures (ΔR2 = 0.05-0.13). Next, we studied VAT/ASAT ratio, a nearly BMI- and waist circumference-independent marker of unhealthy fat distribution. While the comparator model poorly predicted VAT/ASAT ratio (R2: 0.17-0.26), a silhouette-based model enabled significant improvement (R2: 0.50-0.55). Silhouette-predicted VAT/ASAT ratio was associated with increased prevalence of type 2 diabetes and coronary artery disease. Conclusions: Body silhouette images can estimate important measures of fat distribution, laying the scientific foundation for population-based assessment.

scholarly journals Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial (Preprint)

2019 ◽  
Author(s):  
Syed Hasib Akhter Faruqui ◽  
Yan Du ◽  
Rajitha Meka ◽  
Adel Alaeddini ◽  
Chengdong Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Glucose ◽  
Glucose Level ◽  
Mobile Health ◽  
Learning Model ◽  
Health Data ◽  
Glucose Levels ◽  
Health Lifestyle ◽  
Deep Learning Model

BACKGROUND Type 2 diabetes mellitus (T2DM) is a major public health burden. Self-management of diabetes including maintaining a healthy lifestyle is essential for glycemic control and to prevent diabetes complications. Mobile-based health data can play an important role in the forecasting of blood glucose levels for lifestyle management and control of T2DM. OBJECTIVE The objective of this work was to dynamically forecast daily glucose levels in patients with T2DM based on their daily mobile health lifestyle data including diet, physical activity, weight, and glucose level from the day before. METHODS We used data from 10 T2DM patients who were overweight or obese in a behavioral lifestyle intervention using mobile tools for daily monitoring of diet, physical activity, weight, and blood glucose over 6 months. We developed a deep learning model based on long short-term memory–based recurrent neural networks to forecast the next-day glucose levels in individual patients. The neural network used several layers of computational nodes to model how mobile health data (food intake including consumed calories, fat, and carbohydrates; exercise; and weight) were progressing from one day to another from noisy data. RESULTS The model was validated based on a data set of 10 patients who had been monitored daily for over 6 months. The proposed deep learning model demonstrated considerable accuracy in predicting the next day glucose level based on Clark Error Grid and ±10% range of the actual values. CONCLUSIONS Using machine learning methodologies may leverage mobile health lifestyle data to develop effective individualized prediction plans for T2DM management. However, predicting future glucose levels is challenging as glucose level is determined by multiple factors. Future study with more rigorous study design is warranted to better predict future glucose levels for T2DM management.

scholarly journals Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial

10.2196/14452 ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. e14452 ◽  
Author(s):  
Syed Hasib Akhter Faruqui ◽  
Yan Du ◽  
Rajitha Meka ◽  
Adel Alaeddini ◽  
Chengdong Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Glucose ◽  
Glucose Level ◽  
Mobile Health ◽  
Learning Model ◽  
Health Data ◽  
Glucose Levels ◽  
Health Lifestyle ◽  
Deep Learning Model

Background Type 2 diabetes mellitus (T2DM) is a major public health burden. Self-management of diabetes including maintaining a healthy lifestyle is essential for glycemic control and to prevent diabetes complications. Mobile-based health data can play an important role in the forecasting of blood glucose levels for lifestyle management and control of T2DM. Objective The objective of this work was to dynamically forecast daily glucose levels in patients with T2DM based on their daily mobile health lifestyle data including diet, physical activity, weight, and glucose level from the day before. Methods We used data from 10 T2DM patients who were overweight or obese in a behavioral lifestyle intervention using mobile tools for daily monitoring of diet, physical activity, weight, and blood glucose over 6 months. We developed a deep learning model based on long short-term memory–based recurrent neural networks to forecast the next-day glucose levels in individual patients. The neural network used several layers of computational nodes to model how mobile health data (food intake including consumed calories, fat, and carbohydrates; exercise; and weight) were progressing from one day to another from noisy data. Results The model was validated based on a data set of 10 patients who had been monitored daily for over 6 months. The proposed deep learning model demonstrated considerable accuracy in predicting the next day glucose level based on Clark Error Grid and ±10% range of the actual values. Conclusions Using machine learning methodologies may leverage mobile health lifestyle data to develop effective individualized prediction plans for T2DM management. However, predicting future glucose levels is challenging as glucose level is determined by multiple factors. Future study with more rigorous study design is warranted to better predict future glucose levels for T2DM management.

scholarly journals Identifying diabetes from conjunctival images using a novel hierarchical multi-task network

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Xinyue Li ◽  
Chenjie Xia ◽  
Xin Li ◽  
Shuangqing Wei ◽  
Sujun Zhou ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Pathological Changes ◽  
Automated Identification ◽  
Rapid Discrimination ◽  
Sensitivity Specificity ◽  
The Relationship ◽  
Deep Learning Model ◽  
Better Than

AbstractDiabetes can cause microvessel impairment. However, these conjunctival pathological changes are not easily recognized, limiting their potential as independent diagnostic indicators. Therefore, we designed a deep learning model to explore the relationship between conjunctival features and diabetes, and to advance automated identification of diabetes through conjunctival images. Images were collected from patients with type 2 diabetes and healthy volunteers. A hierarchical multi-tasking network model (HMT-Net) was developed using conjunctival images, and the model was systematically evaluated and compared with other algorithms. The sensitivity, specificity, and accuracy of the HMT-Net model to identify diabetes were 78.70%, 69.08%, and 75.15%, respectively. The performance of the HMT-Net model was significantly better than that of ophthalmologists. The model allowed sensitive and rapid discrimination by assessment of conjunctival images and can be potentially useful for identifying diabetes.

Deep Learning Model Selection of Suboptimal Complexity

2018 ◽  
pp. 129-147
Author(s):  
Oleg Bakhteev ◽  
◽  
Vadim Strijov ◽  
Keyword(s):  
Deep Learning ◽  
Model Selection ◽  
Learning Model ◽  
Deep Learning Model ◽  
Selection Of

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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