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BACKGROUND: Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. MAIN TEXT: This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care. CONCLUSION: AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.
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Aims: This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD. Methods and results: We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively. Conclusion: The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.
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INTRODUCTION: Healthcare professionals (HCPs) often recommend their patients to use a specific mHealth app as part of health promotion, disease prevention and patient self-management. There has been a significant growth in the number of HCPs downloading and using mobile health (mHealth) apps. Most mHealth apps that are available in app stores employ a 'star rating' system. This is based on user feedback on an app, but is highly subjective. Thus, the identification of quality mHealth apps which are deemed fit for purpose can be a difficult task for HCPs. Currently, there is no unified, validated standard guidelines for assessment of mHealth apps for patient safety, which can be used by HCPs. The Modified Enlight Suite (MES) is a quality assessment framework designed to provide a means for HCPs to evaluate mHealth apps before they are recommended to patients. MES was adapted from the original Enlight Suite for international use through a Delphi method, followed by preliminary validation process among a population predominantly consisting of medical students. This study aims to evaluate the applicability and validity of the MES, by HCPs, in low, middle and high income country settings. METHODS AND ANALYSIS: MES will be evaluated through a mixed-method study, consisting of qualitative (focus group) and quantitative (survey instruments) research, in three target countries: Malawi (low income), South Africa (middle income) and Ireland (high income). The focus groups will be conducted through Microsoft Teams (Microsoft, Redmond, Washington, USA) and surveys will be conducted online using Qualtrics (Qualtrics International, Seattle, Washington, USA). Participants will be recruited through the help of national representatives in Malawi (Mzuzu University), South Africa (University of Fort Hare) and Ireland (University College Cork) by email invitation. Data analysis for the focus group will be by the means of thematic analysis. Data analysis for the survey will use descriptive statistics and use Cronbach alpha as an indicator of internal consistency of the MES. The construct validity of the mHealth app will be assessed by computing the confirmatory factor analysis using Amos. ETHICS AND DISSEMINATION: The study has received ethical approval from the Social Research Ethics Committee (SREC) SREC/SOM/03092021/1 at University College Cork, Ireland, Malawi Research Ethics Committee (MREC), Malawi MZUNIREC/DOR/21/59 and Inter-Faculty Research Ethics Committee (IFREC) of University of Fort Hare (REC-2 70 710-028-RA). The results of the study will be disseminated through the internet, peer-reviewed journals and conference presentations.