diabetestalk.net

Development And Validation Diabetic Retinopathy

Rhodopsin In Plasma From Patients With Diabetic Retinopathy - Development And Validation Of Digital Elisa By Single Molecule Array (simoa) Technology.

Rhodopsin In Plasma From Patients With Diabetic Retinopathy - Development And Validation Of Digital Elisa By Single Molecule Array (simoa) Technology.

Diabetic retinopathy (DR) is the most frequent cause of blindness among younger adults in the western world. No blood biomarkers exist to detect DR. Hypothetically, Rhodopsin concentrations in blood has been suggested as an early marker for retinal damage. The aim of this study was therefore to develop and validate a Rhodopsin assay by employing digital ELISA technology, and to investigate whether Rhodopsin concentrations in diabetes patients with DR are elevated compared with diabetes patients without DR.A digital ELISA assay using a Simoa HD-1 Analyzer (Quanterix, Lexington, MA 02421, USA) was developed and validated and applied on a cohort of diabetes patients characterised with (n=466) and without (n=144) DR.The Rhodopsin assay demonstrated a LOD of 0.26ng/l, a LLOQ of 3ng/l and a linear measuring range from 3 to 2500ng/l. Total CV% was 32%, 23%, 19% and 17% respectively at the following Rhodopsin concentrations: 1, 3, 5 and 13ng/l. Recovery was 17%, 34%, 51% and 55% respectively at Rhodopsin concentrations of 2, 10, 50 and 250ng/l. There was no statistically significant difference in the plasma concentration of Rhodopsin between the diabetes patients with or without DR, but significantly increased number of DR patients having concentrations above the LOD.We developed and validated a digital ELISA method for quantification of Rhodopsin in plasma but found no statistically significant difference in the plasma concentration of Rhodopsin between diabetes patients with DR compared to diabetes patients without DR, though significantly more DR patients had values above the LOD. Continue reading >>

Development And Validation Of A Diabetic Retinopathy Referral Algorithm Based On Single-field Fundus Photography

Development And Validation Of A Diabetic Retinopathy Referral Algorithm Based On Single-field Fundus Photography

Development and Validation of a Diabetic Retinopathy Referral Algorithm Based on Single-Field Fundus Photography Affiliation Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India Affiliation Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India Affiliation Department of Preventive Ophthalmology, Sankara Nethralaya, Chennai-600 006, Tamil Nadu, India Affiliation Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India Affiliation Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India Development and Validation of a Diabetic Retinopathy Referral Algorithm Based on Single-Field Fundus Photography To develop a simplified algorithm to identify and refer diabetic retinopathy (DR) from single-field retinal images specifically for sight-threatening diabetic retinopathy for appropriate care (ii) to determine the agreement and diagnostic accuracy of the algorithm as a pilot study among optometrists versus gold standard (retinal specialist grading). The severity of DR was scored based on colour photo using a colour coded algorithm, which included the lesions of DR and number of quadrants involved. A total of 99 participants underwent training followed by evaluation. Data of the 99 participants were analyzed. Fifty posterior pole 45 degree retinal images with all stages of DR were presented. Kappa scores (), areas under the receiver operating characteristic curves (AUCs), sensitivity and specificity were determined, with further comparison between working optometrists and optometry students. Mean age of the participants was 22 years (range: 1943 years), 87% being women. Participants correctly identified 91.5% images that required immed Continue reading >>

Artificial Intelligence Eyed As Diabetic Retinopathy Screen

Artificial Intelligence Eyed As Diabetic Retinopathy Screen

Artificial Intelligence Eyed as Diabetic Retinopathy Screen A computing system using artificial intelligence is highly accurate in identifying people with diabetes who have diabetic retinopathy and related eye diseases and need to be referred for further care, a new study finds. Results for the development and validation of a "deep learning system" using retinal images of a large, multiethnic study population with diabetes were published in the December 12 issue of the Journal of the American Medical Association by Daniel Shu Wei Ting, MD, PhD, of the Singapore National Eye Center, with an international group of colleagues. The deep learning system (DLS) is a new artificial intelligence (AI) technology that processes large amounts of data and extracts meaningful patterns from them. Such systems have achieved promising results compared with previous "pattern-recognition" type image analysis, principal investigator Tien Y Wong, MD, PhD, professor andmedical director, Singapore National Eye Center, and chair of ophthalmology at the National University of Singapore, told Medscape Medical News. "The entire DLS approach does not involve any objective judgment and the feature extraction process is entirely automatic. Numerous unconventional featuresare assessed. Thus, DLS can help clinicians detect subtle changes, patterns, and abnormalities that may be overlooked or disregarded by humans," Dr Wong said. Indeed, the ultimate goal is to incorporate the DLS into retinal cameras that can be used in a variety of locations, including primary care, pharmacies, or even retail settings to screen people with diabetes in order to detect who needs to see an ophthalmologist. The approach would be expected to save a considerable amount of money and healthcare resources, according to study Continue reading >>

Development And Validation Of A Deep Learning Algorithm For Detection Of Diabetic Retinopathy In Retinal Fundus Photographs

Development And Validation Of A Deep Learning Algorithm For Detection Of Diabetic Retinopathy In Retinal Fundus Photographs

EyePACS-1 and Messidor-2 Clinical Validation Sets for Detection of Diabetic Retinopathy and All-Cause Referable Diabetic Retinopathy A, Referable diabetic retinopathy, defined as moderate or worse diabetic retinopathy or referable diabetic macular edema. B, All-cause referable cases, defined as moderate or worse diabetic retinopathy, referable diabetic macular edema, or ungradable image quality. Validation Set Performance for Referable Diabetic Retinopathy Performance of the algorithm (black curve) and ophthalmologists (colored circles) for the presence of referable diabetic retinopathy (moderate or worse diabetic retinopathy or referable diabetic macular edema) on A, EyePACS-1 (8788 fully gradable images) and B, Messidor-2 (1745 fully gradable images). The black diamonds on the graph correspond to the sensitivity and specificity of the algorithm at the high-sensitivity and high-specificity operating points. In A, for the high-sensitivity operating point, specificity was 93.4% (95% CI, 92.8%-94.0%) and sensitivity was 97.5% (95% CI, 95.8%-98.7%); for the high-specificity operating point, specificity was 98.1% (95% CI, 97.8%-98.5%) and sensitivity was 90.3% (95% CI, 87.5%-92.7%). In B, for the high-sensitivity operating point, specificity was 93.9% (95% CI, 92.4%-95.3%) and sensitivity was 96.1% (95% CI, 92.4%-98.3%); for the high-specificity operating point, specificity was 98.5% (95% CI, 97.7%-99.1%) and sensitivity was 87.0% (95% CI, 81.1%-91.0%). There were 8 ophthalmologists who graded EyePACS-1 and 7 ophthalmologists who graded Messidor-2. AUC indicates area under the receiver operating characteristic curve. Validation Set Performance for All-Cause Referable Diabetic Retinopathy in the EyePACS-1 Data Set (9946 Images) Performance of the algorithm (black curve) and Continue reading >>

Development And Validation Of A Deep Learning System For Diabetic Retinopathy And Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

Development And Validation Of A Deep Learning System For Diabetic Retinopathy And Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. @article{Ting2017DevelopmentAV, title={Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.}, author={Daniel Shu Wei Ting and Carol Yim-lui Cheung and Gilbert Lim and Gavin S. W. Tan and Nguyen Dang Quang and Alfred Tau Liang Gan and Haslina H Hamzah and Renata Garc{\'i}a-Franco and Ian Yew San Yeo and Shu Yen Lee and Edmund Yick Mun Wong and Charumathi Sabanayagam and Mani Baskaran and Farah Nadia Ibrahim and Ngiap Chuan Tan and Eric Andrew Finkelstein and Ecosse L Lamoureux and Ian Y. H. Wong and Neil M. Bressler and Sobha Sivaprasad and Rohit Varma and Jost Bruno Jonas and Ming Guang He and Ching-Yu Cheng and Gemmy Chui Ming Cheung and Tin Aung and Wynne Hsu and Mong Li Lee and Tien Yin Wong}, journal={JAMA}, year={2017}, volume={318 22}, pages={ 2211-2223 }} Continue reading >>

Replication Study: Development And Validation Of Deep Learning Algorithm For Detection Of Diabetic Retinopathy In Retinal Fundus Photographs

Replication Study: Development And Validation Of Deep Learning Algorithm For Detection Of Diabetic Retinopathy In Retinal Fundus Photographs

. This is to our knowledge the first attempt to replicate their results. The paper describes an algorithm for detection of diabetic retinopathy in retinal fundus photographs. The algorithm is trained using 128 725 fundus images retrieved from EyePACS and from three eye hospitals in India, and the algorithms performance was evaluated on 2 test sets. The algorithms area under the receiver operating curve for detecting referable diabetic retinopathy was 0.991 for EyePACS-1 and 0.990 for Messidor-2, and two operating points were selected for high sensitivity and specificity. The operating point for high specificity had 90.3% and 87.0% sensitivity and 98.1% and 98.5% specificity for the EyePACS-1 and Messidor-2 test sets, whereas the operating point for high sensitivity had 97.5% and 96.1% sensitivity and 93.4% and 93.9% specificity, respectively. All fundus images were re-graded by a team of 54 US-licensed ophthalmologists. The source code is not published. To re-implement their algorithm for detection of diabetic retinopathy, we used similar images from a publicly available EyePACS data set for training, and we used a subset from the EyePACS data set and images from the public Messidor-Original data set for testing. In addition, we attempted to find optimal hyper-parameters for the algorithms training and validation procedure that the paper did not describe. Our objective is to compare the results of their referable diabetic retinopathy detection algorithm, and our replica algorithm, taking into account potential deviations in the data sets, having fewer grades, and potential differences in hyper-parameter settings. In comparison, our algorithms area under the receiver operating curve for detecting referable diabetic retinopathy for our EyePACS and Messidor-Original test Continue reading >>

Spotlight On Artificial Intelligence And Diabetic Retinopathy

Spotlight On Artificial Intelligence And Diabetic Retinopathy

ACP Diabetes Monthly | Keeping tabs | October 12, 2018 Spotlight on artificial intelligence and diabetic retinopathy One recent study reported on the development and validation of an artificial intelligence algorithm for detecting diabetic retinopathy, while another tested such technology in primary care. Two recent studies from Australia looked at how well artificial intelligence systems diagnose diabetic retinopathy. The first study, published online by Diabetes Care on Oct. 1, described the development and validation of an artificial intelligencebased, deep learning algorithm for the detection of vision-threatening referable diabetic retinopathy (defined as at least preproliferative diabetic retinopathy or diabetic macular edema). The study included separate training and validation sets of retinal photographs, 71,043 in total, 12,329 with what clinicians had judged to be referable diabetic retinopathy. In the internal validation set, the algorithm's area under the curve (AUC) was 0.989, the sensitivity was 97.0%, and the specificity was 91.4%. Testing against an independent, multiethnic (Malay, Caucasian Australians, and Indigenous Australians) data set found results of 0.955%, 92.5%, and 98.5%, respectively. Most false positives (85.6%) were due to misclassification of mild or moderate diabetic retinopathy, and undetected intraretinal microvascular abnormalities caused 77.3% of false negatives. The study authors concluded that the algorithm could be used with high accuracy to detect cases that should be referred to ophthalmology. Thus it offers great potential as an efficient, low-cost solution for [diabetic retinopathy] screening, they wrote. This study differed from previous similar analyses by its use of less strict, more real-world criteria for referral and a m Continue reading >>

Automated Diabetic Retinopathy Detection In Smartphone-based Fundus Photography Using Artificial Intelligence

Automated Diabetic Retinopathy Detection In Smartphone-based Fundus Photography Using Artificial Intelligence

Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence To assess the role of artificial intelligence (AI)-based automated software for detection of diabetic retinopathy (DR) and sight-threatening DR (STDR) by fundus photography taken using a smartphone-based device and validate it against ophthalmologists grading. Three hundred and one patients with type 2 diabetes underwent retinal photography with Remidio Fundus on phone (FOP), a smartphone-based device, at a tertiary care diabetes centre in India. Grading of DR was performed by the ophthalmologists using International Clinical DR (ICDR) classification scale. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The retinal photographs were graded using a validated AI DR screening software (EyeArtTM) designed to identify DR, referable DR (moderate non-proliferative DR or worse and/or DME) or STDR. The sensitivity and specificity of automated grading were assessed and validated against the ophthalmologists grading. Retinal images of 296 patients were graded. DR was detected by the ophthalmologists in 191 (64.5%) and by the AI software in 203 (68.6%) patients while STDR was detected in 112 (37.8%) and 146 (49.3%) patients, respectively. The AI software showed 95.8% (95% CI 92.998.7) sensitivity and 80.2% (95% CI 72.687.8) specificity for detecting any DR and 99.1% (95% CI 95.199.9) sensitivity and 80.4% (95% CI 73.985.9) specificity in detecting STDR with a kappa agreement of k = 0.78 (p < 0.001) and k = 0.75 (p < 0.001), respectively. Automated AI analysis of FOP smartphone retinal imaging has very high sensitivity for detecting DR and STDR and thus can be an initial tool for mass retinal screening in p Continue reading >>

[1803.04337] Replication Study: Development And Validation Of Deep Learning Algorithm For Detection Of Diabetic Retinopathy In Retinal Fundus Photographs

[1803.04337] Replication Study: Development And Validation Of Deep Learning Algorithm For Detection Of Diabetic Retinopathy In Retinal Fundus Photographs

Computer Science > Computer Vision and Pattern Recognition Title:Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs Authors: Mike Voets , Kajsa Mllersen , Lars Ailo Bongo Abstract: We have replicated some experiments in 'Development and validation of a deeplearning algorithm for detection of diabetic retinopathy in retinal fundusphotographs' that was published in JAMA 2016; 316(22). We re-implemented themethods since the source code is not available. The original study used fundus images from EyePACS and three hospitals inIndia for training their detection algorithm. We used a different EyePACS dataset that was made available in a Kaggle competition. For evaluating thealgorithm's performance the benchmark data set Messidor-2 was used. We used thesimilar Messidor-Original data set to evaluate our algorithm's performance. Inthe original study licensed ophthalmologists re-graded all their obtainedimages for diabetic retinopathy, macular edema, and image gradability. Ourchallenge was to re-implement the methods with publicly available data sets andone diabetic retinopathy grade per image, find the hyper-parameter settings fortraining and validation that were not described in the original study, and makean assessment on the impact of training with ungradable images. We were not able to reproduce the performance as reported in the originalstudy. We believe our model did not learn to recognize lesions in fundusimages, since we only had a singular grade for diabetic retinopathy per image,instead of multiple grades per images. Furthermore, the original study misseddetails regarding hyper-parameter settings for training and validation. Theoriginal study may also have used image quality grad Continue reading >>

Development And Validation Of A Deep Learning System For Diabetic Retinopathy And Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

Development And Validation Of A Deep Learning System For Diabetic Retinopathy And Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

Ting, D. S. W., Cheung, C. Y. L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., ... Wong, T. Y. (2017). Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes . JAMA - Journal of the American Medical Association , 318(22), 2211-2223. DOI: 10.1001/jama.2017.18152 Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. / Ting, Daniel Shu Wei; Cheung, Carol Yim Lui; Lim, Gilbert; Tan, Gavin Siew Wei; Quang, Nguyen D.; Gan, Alfred; Hamzah, Haslina; Garcia-Franco, Renata; Yeo, Ian Yew San; Lee, Shu Yen; Wong, Edmund Yick Mun; Sabanayagam, Charumathi; Baskaran, Mani; Ibrahim, Farah; Tan, Ngiap Chuan; Finkelstein, Eric A.; Lamoureux, Ecosse L.; Wong, Ian Y. ; Bressler, Neil M. ; Sivaprasad, Sobha; Varma, Rohit; Jonas, Jost B.; He, Ming Guang; Cheng, Ching Yu; Cheung, Gemmy Chui Ming; Aung, Tin; Hsu, Wynne; Lee, Mong Li; Wong, Tien Yin. In: JAMA - Journal of the American Medical Association , Vol. 318, No. 22, 12.12.2017, p. 2211-2223. Research output: Contribution to journal Article Ting DSW, Cheung CYL, Lim G, Tan GSW, Quang ND, Gan A et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes . JAMA - Journal of the American Medical Association . 2017 Dec 12;318(22):2211-2223. Available from, DOI: 10.1001/jama.2017.18152 Ting, Daniel Shu Wei ; Cheung, Carol Yim Lui ; Lim, Gilbert ; Tan, Gavin Siew Wei ; Quang, Nguyen D. ; Gan, Alfred ; Hamzah, Haslina ; Garcia-Franco, Renata ; Yeo, Ian Yew San ; Lee, Shu Yen ; Wong, Edmund Yick Mun ; Sabanay Continue reading >>

Development And Validation Of Risk Assessment Models For Diabetes-related Complications Based On The Dcct/edic Data

Development And Validation Of Risk Assessment Models For Diabetes-related Complications Based On The Dcct/edic Data

Volume 29, Issue 4 , MayJune 2015, Pages 479-487 Development and validation of risk assessment models for diabetes-related complications based on the DCCT/EDIC data Author links open overlay panel VincenzoLagania To derive and validate a set of computational models able to assess the risk of developing complications and experiencing adverse events for patients with diabetes. The models are developed on data from the Diabetes Control and Complications Trial (DCCT) and the Epidemiology of Diabetes Interventions and Complications (EDIC) studies, and are validated on an external, retrospectively collected cohort. We selected fifty-one clinical parameters measured at baseline during the DCCT as potential risk factors for the following adverse outcomes: Cardiovascular Diseases (CVD), Hypoglycemia, Ketoacidosis, Microalbuminuria, Proteinuria, Neuropathy and Retinopathy. For each outcome we applied a data-mining analysis protocol in order to identify the best-performing signature, i.e., the smallest set of clinical parameters that, considered jointly, are maximally predictive for the selected outcome. The predictive models built on the selected signatures underwent both an interval validation on the DCCT/EDIC data and an external validation on a retrospective cohort of 393 diabetes patients (49 Type I and 344 Type II) from the Chorleywood Medical Center, UK. The selected predictive signatures contain five to fifteen risk factors, depending on the specific outcome. Internal validation performances, as measured by the Concordance Index (CI), range from 0.62 to 0.83, indicating good predictive power. The models achieved comparable performances for the Type I and, quite surprisingly, Type II external cohort. Data-mining analyses of the DCCT/EDIC data allow the identification of ac Continue reading >>

Development And Validation Of A Diabetes Risk Score For Screening Undiagnosed Diabetes In Sri Lanka (sldrisk)

Development And Validation Of A Diabetes Risk Score For Screening Undiagnosed Diabetes In Sri Lanka (sldrisk)

Development and validation of a Diabetes Risk Score for screening undiagnosed diabetes in Sri Lanka (SLDRISK) Screening for undiagnosed diabetes is not widely undertaken due to the high costs and invasiveness of blood sampling. Simple non-invasive tools to identify high risk individuals can facilitate screening. The main objectives of this study are to develop and validate a risk score for screening undiagnosed diabetes among Sri Lankan adults and to compare its performance with the Cambridge Risk Score (CRS), the Indian Diabetes Risk Score (IDRS) and three other Asian risk scores. Data were available from a representative sample of 4276 adults without diagnosed diabetes. In a jack-knife approach two thirds of the sample was used for the development of the risk score and the remainder for the validation. Age, waist circumference, BMI, hypertension, balanitis or vulvitis, family history of diabetes, gestational diabetes, physical activity and osmotic symptoms were significantly associated with undiagnosed diabetes (age most to osmotic symptoms least). Individual scores were generated for these factors using the beta coefficient values obtained in multiple logistic regression. A cut-off value of sum = 31 was determined by ROC curve analysis. The area under the ROC curve of the risk score for prevalent diabetes was 0.78 (CI 0.730.82). In the sample 36.3% were above the cut-off of 31. A risk score above 31 gave a sensitivity, specificity, positive predictive value and negative predictive value of 77.9, 65.6, 9.4 and 98.3% respectively. For Sri Lankans the AUC for the CRS and IDRS were 0.72 and 0.66 repectively. This simple non-invasive screening tool can identify 80% of undiagnosed diabetes by selecting 40% of Sri Lankan adults for confirmatory blood investigations. Diabet Continue reading >>

Development And Validation Of A Deep Learning System For Diabetic Retinopathy And Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

Development And Validation Of A Deep Learning System For Diabetic Retinopathy And Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yatsen University, Guangzhou, China. JAMA. 2017 Dec 12;318(22):2211-2223. doi: 10.1001/jama.2017.18152. Importance: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective: To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants: Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76370 images), possible glaucoma (125189 images), and AMD (72610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112648 images), possible glaucoma (71896 images), and AMD (35948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures: Use of a deep learning system. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Resu Continue reading >>

Development And Validation Of A Health Policy Model Of Type 2 Diabetes In Chinese Setting

Development And Validation Of A Health Policy Model Of Type 2 Diabetes In Chinese Setting

Development and validation of a Health Policy Model of Type 2 diabetes in Chinese setting Aim: Due to the difference in epidemiology and outcomes between eastern and western populations with Type 2 diabetes mellitus (T2DM), an important challenge is determining how useful the outcomes from diabetes models based on western populations are for eastern patients. Consequently, the principal aim of this study was to develop and validate a Health Policy Model (Chinese Outcomes Model for T2DM [COMT]) for supporting Chinese medical and health economic studies. Methods: The model is created to simulate a series of important complications of T2DM diabetes based on the latest Risk Equations for Complications of Type 2 Diabetes, which was adjusted by adding the adjustment regulator to the linear predictor within the risk equation. The validity of the model was conducted by using a total of 171 validation outcomes from seven studies in eastern populations and ten studies in western populations. The simulation cohorts in the COMT model were generated by copying each validation study's baseline characteristics. Concordance was tested by assessing the difference between the identity (45) line and the best-fitting regression of the scatterplots for the predicted versus observed outcomes. Results: The slope coefficients of the best-fitting regression line between the predicted and corresponding observed actual outcomes was 0.9631 and the R2 was 0.8701. There were major differences between western and eastern populations. The slope and R2 of predictions were 0.9473 and 0.9272 in the eastern population and 1.0566 and 0.8863 in the western population, which showed more perfect agreement with the observed values in the eastern population than the western populations. The subset of macro-vas Continue reading >>

Development And Validation Of A Deep Learning System For Diabetic Retinopathy And Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

Development And Validation Of A Deep Learning System For Diabetic Retinopathy And Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes 1Singapore Eye Research Institute, Singapore National Eye Center, Singapore 2Duke-NUS Medical School, National University of Singapore, Singapore 3Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong SAR, China 4School of Computing, National University of Singapore 5Instituto Mexicano De Oftalmologia, IAP, Queretaro, Mexico. 6SingHealth Polyclinic, Singapore Health Service, Singapore 7Lien Center for Palliative Care, Health Services and Systems Research Program, Duke-NUS Graduate Medical School, Singapore 8Department of Ophthalmology, The University of Hong Kong, Hong Kong SAR, China 9Johns Hopkins Wilmer Eye Institute, Baltimore, Maryland 10Moorfields Eye Hospital National Health Service Foundation Trust, London, United Kingdom 11University of Southern California Gayle and Edward Roski Eye Institute, Los Angeles, California 12Department of Ophthalmology, Ruprecht-Karls University of Heidelberg, Heidelberg, Germany 13State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yatsen University, Guangzhou, China JAMA. 2017;318(22):2211-2223. doi:10.1001/jama.2017.18152 Continue reading >>

More in diabetes