The Utility of Smartphone-Based Artificial Intelligence Approaches for Diabetic Retinopathy

Sheikh, A; Bhatti, Ahsan; Adeyemi, Oluwaseun; Raja, Muhammad; Sheikh, Ijaz

doi:10.4103/2452-2325.329064

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…18 Smartphone-based AI technologies permit cost-effective screening of medical conditions, health data analysis, efficient laboratory tests, timely diagnosis of diseases, improved treatment outcomes, analysis of vital signs, etc., using sensors and machine learning algorithms, microprocessors, and high-quality cameras. 17,30 As Sheikh et al have seen 31 smartphone-based AI reached an accuracy of 89.5% and a specificity of 92.4% in diagnosing diabetic retinopathy. Two cross-sectional studies (50%) in 2020 in the United States and Saudi Arabia used Python, Keras, and TensorFlow to predict, detect, or diagnose COVID-19.…”

Section: Discussionmentioning

confidence: 94%

Artificial Intelligence Functionalities During the COVID-19 Pandemic

Marzaleh

Peyravi

Mousavi

et al. 2023

Disaster med. public health prep.

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Background: The coronavirus disease 2019 (COVID-19) pandemic has led us to use virtual solutions and emerging technologies such as artificial intelligence (AI). Recent studies have clearly demonstrated the role of AI in health care and medical practice; however, a comprehensive review can identify potential yet not fulfilled functionalities of such technologies in pandemics. Therefore, this scoping review study aims at assessing AI functionalities in the COVID-19 pandemic in 2022. Methods: A systematic search was carried out in PubMed, Cochran Library, Scopus, Science Direct, ProQuest, and Web of Science from 2019 to May 9, 2022. Researchers selected the articles according to the search keywords. Finally, the articles mentioning the functionalities of AI in the COVID-19 pandemic were evaluated. Two investigators performed this process. Results: Initial search resulted in 9123 articles. After reviewing the title, abstract, and full text of these articles, and applying the inclusion and exclusion criteria, 4 articles were selectd for the final analysis. All 4 were cross-sectional studies. Two studies (50%) were performed in the United States, 1 (25%) in Israel, and 1 (25%) in Saudi Arabia. They covered the functionalities of AI in the prediction, detection, and diagnosis of COVID-19. Conclusions: To the extent of the researchers’ knowledge, this study is the first scoping review that assesses the AI functionalities in the COVID-19 pandemic. Health-care organizations need decision support technologies and evidence-based apparatuses that can perceive, think, and reason not dissimilar to human beings. Potential functionalities of such technologies can be used to predict mortality, detect, screen, and trace current and former patients, analyze health data, prioritize high-risk patients, and better allocate hospital resources in pandemics, and generally in health-care settings.

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Section: Discussionmentioning

confidence: 94%

Artificial Intelligence Functionalities During the COVID-19 Pandemic

Marzaleh

Peyravi

Mousavi

et al. 2023

Disaster med. public health prep.

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“…Artificial intelligence (AI) has the potential to address this issue. In a systematic review of four studies on AI technology by Sheikh et al, the pooled sensitivity and specificity for detecting referable diabetic retinopathy (moderate non-PDR or worse, with or without DME) were 97.9% and 85.9%, respectively [44]. In fact, these values were higher than the corresponding values for detecting any diabetic retinopathy (89.5% and 92.4%, respectively).…”

Section: Handheld Mobile Devicesmentioning

confidence: 87%

Diabetic retinopathy screening in the emerging era of artificial intelligence

Grauslund

2022

Diabetologia

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Diabetic retinopathy is a frequent complication in diabetes and a leading cause of visual impairment. Regular eye screening is imperative to detect sight-threatening stages of diabetic retinopathy such as proliferative diabetic retinopathy and diabetic macular oedema in order to treat these before irreversible visual loss occurs. Screening is cost-effective and has been implemented in various countries in Europe and elsewhere. Along with optimised diabetes care, this has substantially reduced the risk of visual loss. Nevertheless, the growing number of patients with diabetes poses an increasing burden on healthcare systems and automated solutions are needed to alleviate the task of screening and improve diagnostic accuracy. Deep learning by convolutional neural networks is an optimised branch of artificial intelligence that is particularly well suited to automated image analysis. Pivotal studies have demonstrated high sensitivity and specificity for classifying advanced stages of diabetic retinopathy and identifying diabetic macular oedema in optical coherence tomography scans. Based on this, different algorithms have obtained regulatory approval for clinical use and have recently been implemented to some extent in a few countries. Handheld mobile devices are another promising option for self-monitoring, but so far they have not demonstrated comparable image quality to that of fundus photography using non-portable retinal cameras, which is the gold standard for diabetic retinopathy screening. Such technology has the potential to be integrated in telemedicine-based screening programmes, enabling self-captured retinal images to be transferred virtually to reading centres for analysis and planning of further steps. While emerging technologies have shown a lot of promise, clinical implementation has been sparse. Legal obstacles and difficulties in software integration may partly explain this, but it may also indicate that existing algorithms may not necessarily integrate well with national screening initiatives, which often differ substantially between countries.

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“…There are limited papers about usability of Apps for individual risk assessment for progression of DR compared with those publications on DR prediction by digital retinal imaging [27][28][29] . We developed a smartphone App which specialized in risk factors that could be perceived by patients in their daily life to assess the progression of DR. By fulfilling the questionnaire in the App, those patients could self-manage their personalized DR risk and update such risk after altering their risk factors, and further consult eye specialist if they were at high risk.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a risk prediction model for diabetic retinopathy in type 2 diabetic patients

Zhu

Wang

et al. 2023

Sci Rep

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To establish a risk prediction model and make individualized assessment for the susceptible diabetic retinopathy (DR) population in type 2 diabetic mellitus (T2DM) patients. According to the retrieval strategy, inclusion and exclusion criteria, the relevant meta-analyses on DR risk factors were searched and evaluated. The pooled odds ratio (OR) or relative risk (RR) of each risk factor was obtained and calculated for β coefficients using logistic regression (LR) model. Besides, an electronic patient-reported outcome questionnaire was developed and 60 cases of DR and non-DR T2DM patients were investigated to validate the developed model. Receiver operating characteristic curve (ROC) was drawn to verify the prediction accuracy of the model. After retrieving, eight meta-analyses with a total of 15,654 cases and 12 risk factors associated with the onset of DR in T2DM, including weight loss surgery, myopia, lipid-lowing drugs, intensive glucose control, course of T2DM, glycated hemoglobin (HbA1c), fasting plasma glucose, hypertension, gender, insulin treatment, residence, and smoking were included for LR modeling. These factors, followed by the respective β coefficient was bariatric surgery (− 0.942), myopia (− 0.357), lipid-lowering drug follow-up < 3y (− 0.994), lipid-lowering drug follow-up > 3y (− 0.223), course of T2DM (0.174), HbA1c (0.372), fasting plasma glucose (0.223), insulin therapy (0.688), rural residence (0.199), smoking (− 0.083), hypertension (0.405), male (0.548), intensive glycemic control (− 0.400) with constant term α (− 0.949) in the constructed model. The area under receiver operating characteristic curve (AUC) of the model in the external validation was 0.912. An application was presented as an example of use. In conclusion, the risk prediction model of DR is developed, which makes individualized assessment for the susceptible DR population feasible and needs to be further verified with large sample size application.

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The Utility of Smartphone-Based Artificial Intelligence Approaches for Diabetic Retinopathy

Cited by 13 publications

References 20 publications

Artificial Intelligence Functionalities During the COVID-19 Pandemic

Artificial Intelligence Functionalities During the COVID-19 Pandemic

Diabetic retinopathy screening in the emerging era of artificial intelligence

Development and validation of a risk prediction model for diabetic retinopathy in type 2 diabetic patients

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