scholarly journals Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development (Preprint)

2018 ◽  
Author(s):  
Josep Vidal-Alaball ◽  
Dídac Royo Fibla ◽  
Miguel A Zapata ◽  
Francesc X Marin-Gomez ◽  
Oscar Solans Fernandez
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy ◽  
Prospective Study ◽  
Confusion Matrix ◽  
False Negative ◽  
Superior Performance ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Care Protocol

BACKGROUND Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities. OBJECTIVE The objective of this is paper was to develop an artificial intelligence (AI) algorithm for the detection of signs of DR in diabetic patients and to scientifically validate the algorithm to be used as a screening tool in primary care. METHODS Under this project, 2 studies will be conducted in a concomitant way: (1) Development of an algorithm with AI to detect signs of DR in patients with diabetes and (2) A prospective study comparing the diagnostic capacity of the AI algorithm with respect to the actual system of family physicians evaluating the images. The standard reference to compare with will be a blinded double reading conducted by retina specialists. For the development of the AI algorithm, different iterations and workouts will be performed on the same set of data. Before starting each new workout, the strategy of dividing the set date into 2 groups will be used randomly. A group with 80% of the images will be used during the training (training dataset), and the remaining 20% images will be used to validate the results (validation dataset) of each cycle (epoch). During the prospective study, true-positive, true-negative, false-positive, and false-negative values will be calculated again. From here, we will obtain the resulting confusion matrix and other indicators to measure the performance of the algorithm. RESULTS Cession of the images began at the end of 2018. The development of the AI algorithm is calculated to last about 3 to 4 months. Inclusion of patients in the cohort will start in early 2019 and is expected to last 3 to 4 months. Preliminary results are expected to be published by the end of 2019. CONCLUSIONS The study will allow the development of an algorithm based on AI that can demonstrate an equal or superior performance, and that constitutes a complement or an alternative, to the current screening of DR in diabetic patients. INTERNATIONAL REGISTERED REPOR PRR1-10.2196/12539

scholarly journals Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development

10.2196/12539 ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. e12539 ◽  
Author(s):  
Josep Vidal-Alaball ◽  
Dídac Royo Fibla ◽  
Miguel A Zapata ◽  
Francesc X Marin-Gomez ◽  
Oscar Solans Fernandez
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy ◽  
Algorithm Development ◽  
Care Protocol

602-P: Explaining an Artificial Intelligence (AI) System for Diabetic Retinopathy (DR) Screening in Primary Care

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 602-P
Author(s):  
NISHIT UMESH PAREKH ◽  
MALAVIKA BHASKARANAND ◽  
CHAITHANYA RAMACHANDRA ◽  
SANDEEP BHAT ◽  
KAUSHAL SOLANKI
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy

scholarly journals Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reza Mirshahi ◽  
Pasha Anvari ◽  
Hamid Riazi-Esfahani ◽  
Mahsa Sardarinia ◽  
Masood Naseripour ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Healthy Subjects ◽  
Optical Coherence Tomography Angiography ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

scholarly journals Screening for Diabetic Retinopathy in Endocrinology Clinics by Using Handheld Cameras and Applying Artificial Intelligence Algorithms

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A419-A420
Author(s):  
Zack Dvey-Aharon ◽  
Petri Huhtinen
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Ground Truth ◽  
World Health ◽  
Diabetic Patients ◽  
Early Screening ◽  
Exclusion Criteria ◽  
Fundus Camera ◽  
The World

Abstract According to estimations of the World Health Organization (WHO), there are almost 500M people in the world that suffer from diabetes. Projections suggest this number will surpass 700M by 2045 with global prevalence surpassing 7%. This huge population, alongside people with pre-diabetics, is prone to develop diabetic retinopathy, the leading cause of vision loss in the working age. While early screening can help prevent most cases of vision loss caused by diabetic retinopathy, the vast majority of patients are not being screened periodically as the guidelines instruct. The challenge is to find a reliable and convenient method to screen patients so that efficacy in detection of referral diabetic retinopathy is sufficient while integration with the flow of care is smooth, easy, simple, and cost-efficient. In this research, we described a screening process for more-than-mild retinopathy through the application of artificial intelligence (AI) algorithms on images obtained by a portable, handheld fundus camera. 156 patients were screened for mtmDR indication. Four images were taken per patient, two macula centered and two optic disc centered. The 624 images were taken using the Optomed Aurora fundus camera and were uploaded using Optomed Direct-Upload. Fully blinded and independently, a certified, experienced ophthalmologist (contracted by Optomed and based in Finland) reviewed each patient to determine ground truth. Indications that are different than mtmDR were also documented by the ophthalmologist to meet exclusion criteria. Data was obtained from anonymized images uploaded to the cloud-based AEYE-DS system and analysis results from the AI algorithm were promptly returned to the users. Of the 156 patients, a certified ophthalmologist determined 100% reached sufficient quality of images for grading, and 36 had existing retinal diseases that fall under exclusion criteria, thus, 77% of the participants met the participation criteria. Of the remaining 120 patients, the AEYE-DS system determined that 2 patients had at least one insufficient quality image. AEYE-DS provided readings for each of the 118 remaining patients (98.3% of all patients). These were statistically compared to the output of the ground truth arm. The patient ground truth was defined as the most severe diagnosis from the four patient images; the ophthalmologist diagnosed 54 patients as mtmDR+ (45% prevalence). Of the 54 patients with referable DR, 50 were diagnosed and of the 64 mtmDR- patients, 61 were correctly diagnosed by the AI. In summary, the results of the study in terms of sensitivity and specificity were 92.6% and 95.3%, respectively. The results indicated accurate classification of diabetic patients that required referral to the ophthalmologist and those who did not. The results also demonstrated the potential of efficient screening and easy workflow integration into points of care such as endocrinology clinics.

scholarly journals A 15 Month Experience with a Primary Care-based Telemedicine Screening Program for Diabetic Retinopathy.

2020 ◽  
Author(s):  
James Benjamin ◽  
Justin Sun ◽  
Devon Cohen ◽  
Joseph Matz ◽  
Angela Barbera ◽  
...  
Keyword(s):  
Primary Care ◽  
Diabetic Retinopathy ◽  
Screening Program ◽  
Primary Care Clinic ◽  
University Hospital ◽  
Eye Disease ◽  
Diabetic Patients ◽  
Close Monitoring ◽  
Care Clinic ◽  
Retinal Screening

Abstract Background: Using telemedicine for diabetic retinal screening is becoming popular especially amongst at-risk urban communities with poor access to care. The goal of the diabetic telemedicine project at Temple University Hospital is to improve cost-effective access to appropriate retinal care to those in need of close monitoring and/or treatment.Methods: This will be a retrospective review of 15 months of data from March 2016 to May 2017. We will investigate how many patients were screened, how interpretable the photographs were, how often the photographs generated a diagnosis of diabetic retinopathy (DR) based on the screening photo, and how many patients followed-up for an exam in the office, if indicated.Results: Six-hundred eighty-nine (689) digital retinal screening exams on 1377 eyes of diabetic patients were conducted in Temple’s primary care clinic. The majority of the photographs were read to have no retinopathy (755, 54.8%). Among all of the screening exams, 357 (51.8%) triggered a request for a referral to ophthalmology. Four-hundred forty-nine (449, 32.6%) of the photos were felt to be uninterpretable by the clinician. Referrals were meant to be requested for DR found in one or both eyes, inability to assess presence of retinopathy in one or both eyes, or for suspicion of a different ophthalmic diagnosis. Sixty-seven patients (9.7%) were suspected to have another ophthalmic condition based on other findings in the retinal photographs. Among the 34 patients that were successfully completed a referral visit to Temple ophthalmology, there was good concordance between the level of DR detected by their screening fundus photographs and visit diagnosis.Conclusions: Although a little more than half of the patients did not have diabetic eye disease, about half needed a referral to ophthalmology. However, only 9.5% of the referral-warranted exams actually received an eye exam. Mere identification of referral-warranted diabetic retinopathy or other eye disease is not enough. A successful telemedicine screening program must close the communication gap between screening and diagnosis by reviewer to provide timely follow-up by eye care specialists.

scholarly journals Prevalence of Diabetic Retinopathy and its Associated Factors among Diabetic Patients in Primary Care Clinics, Kuantan, Pahang

2020 ◽  
Vol 17 (2) ◽  
Author(s):  
Mohd Aznan MA ◽  
Khairidzan MK ◽  
Razman MR ◽  
Fa’iza A
Keyword(s):  
Primary Care ◽  
Diabetic Retinopathy ◽  
Cross Sectional Study ◽  
Diabetic Patients ◽  
Cross Sectional ◽  
Uncontrolled Blood Pressure ◽  
Primary Care Clinics ◽  
High Hba1c ◽  
Retinal Examination ◽  
Disease Severity Scale

Introduction: Diabetic retinopathy (DR) is one of the commonest complications of diabetes mellitus. This study was to determine the prevalence of DR and its association with chronic kidney disease (CKD), high HbA1c and dyslipidemia among diabetic patients in government primary care clinics. Materials and Methods: A cross sectional study was carried out. The respondents were selected from diabetic registry at two government primary care clinics in Kuantan, Pahang via stratified random sampling method during the study period from May 2010 to April 2011. The respondents were interviewed and assessed clinically using a structured questionnaire. Retinal examination was performed by accredited staff using non-mydratic retinal imaging and DR was classified according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Results: Out of 400 respondents, 58.8% were diagnosed with diabetes less than 5 years and 51.0% had uncontrolled blood pressure (>130/80 mmHg). The prevalence of DR and maculopathy were 33.5% and of 17.8% respectively. Most of these patients (22.3%) had mild non-proliferative DR. DR patients had higher percentages CKD (17.9% vs. 6.8%; p<0.001) and a higher mean of HbA1C (8.69 vs. 8.11; p=0.015) compared to non-DR patients. The study revealed that DR was independently associated with CKD {OR: 3.46, 95% CI (1.76, 6.80)} and high HbA1c {OR: 1.12, 95% CI (1.02, 1.23)}. Those with dyslipidemia however, has 39% less risk of DR {OR: 0.61, 95% CI (0.39, 0.94)}. Conclusion: This study showed that diabetic patients with CKD and high HbA1c have greater risks to develop DR but has protective risk with dyslipidemia.

scholarly journals Evaluation of Artificial Intelligence–Based Grading of Diabetic Retinopathy in Primary Care

2018 ◽  
Vol 1 (5) ◽  
pp. e182665 ◽  
Author(s):  
Yogesan Kanagasingam ◽  
Di Xiao ◽  
Janardhan Vignarajan ◽  
Amita Preetham ◽  
Mei-Ling Tay-Kearney ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy

scholarly journals Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera

2021 ◽  
pp. 193229682098556
Author(s):  
Fernando Korn Malerbi ◽  
Rafael Ernane Andrade ◽  
Paulo Henrique Morales ◽  
José Augusto Stuchi ◽  
Diego Lencione ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Diagnostic Accuracy ◽  
Real World ◽  
False Negative ◽  
Portable Devices ◽  
High Burden ◽  
Screening Programs

Background: Portable retinal cameras and deep learning (DL) algorithms are novel tools adopted by diabetic retinopathy (DR) screening programs. Our objective is to evaluate the diagnostic accuracy of a DL algorithm and the performance of portable handheld retinal cameras in the detection of DR in a large and heterogenous type 2 diabetes population in a real-world, high burden setting. Method: Participants underwent fundus photographs of both eyes with a portable retinal camera (Phelcom Eyer). Classification of DR was performed by human reading and a DL algorithm (PhelcomNet), consisting of a convolutional neural network trained on a dataset of fundus images captured exclusively with the portable device; both methods were compared. We calculated the area under the curve (AUC), sensitivity, and specificity for more than mild DR. Results: A total of 824 individuals with type 2 diabetes were enrolled at Itabuna Diabetes Campaign, a subset of 679 (82.4%) of whom could be fully assessed. The algorithm sensitivity/specificity was 97.8 % (95% CI 96.7-98.9)/61.4 % (95% CI 57.7-65.1); AUC was 0·89. All false negative cases were classified as moderate non-proliferative diabetic retinopathy (NPDR) by human grading. Conclusions: The DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs.

A Comparison of Artificial Intelligence and Human Diabetic Retinal Image Interpretation in an Urban Health System

2021 ◽  
pp. 193229682199937
Author(s):  
Nikita Mokhashi ◽  
Julia Grachevskaya ◽  
Lorrie Cheng ◽  
Daohai Yu ◽  
Xiaoning Lu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Predictive Value ◽  
Image Interpretation ◽  
High Sensitivity ◽  
Diabetic Patients ◽  
Screening Algorithm ◽  
Significant Difference ◽  
Internal Medicine Clinic ◽  
Sensitivity Specificity

Introduction: Artificial intelligence (AI) diabetic retinopathy (DR) software has the potential to decrease time spent by clinicians on image interpretation and expand the scope of DR screening. We performed a retrospective review to compare Eyenuk’s EyeArt software (Woodland Hills, CA) to Temple Ophthalmology optometry grading using the International Classification of Diabetic Retinopathy scale. Methods: Two hundred and sixty consecutive diabetic patients from the Temple Faculty Practice Internal Medicine clinic underwent 2-field retinal imaging. Classifications of the images by the software and optometrist were analyzed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and McNemar’s test. Ungradable images were analyzed to identify relationships with HbA1c, age, and ethnicity. Disagreements and a sample of 20% of agreements were adjudicated by a retina specialist. Results: On patient level comparison, sensitivity for the software was 100%, while specificity was 77.78%. PPV was 19.15%, and NPV was 100%. The 38 disagreements between software and optometrist occurred when the optometrist classified a patient’s images as non-referable while the software classified them as referable. Of these disagreements, a retina specialist agreed with the optometrist 57.9% the time (22/38). Of the agreements, the retina specialist agreed with both the program and the optometrist 96.7% of the time (28/29). There was a significant difference in numbers of ungradable photos in older patients (≥60) vs younger patients (<60) (p=0.003). Conclusions: The AI program showed high sensitivity with acceptable specificity for a screening algorithm. The high NPV indicates that the software is unlikely to miss DR but may refer patients unnecessarily.

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