Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank.

BACKGROUND: Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5-10% (termed as borderline-QRISK3 group) using the UK Biobank. METHODS: Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups. RESULTS: Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30-1.52) with a 13.1% (95% CI, 11.7-14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010-0.017) in non-statin cohort, 0.013 (0.007-0.019) in stage 1 hypertension cohort, and 0.023 (0.018-0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3. CONCLUSIONS: Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD.

Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans.

Aims: Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications. Methods: The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs. non-fluid optical coherence tomography (OCT) scans. Both networks are validated on external datasets. Results: Internal testing for our segmentation network produced an IoU score of 83.0% (95% CI = 76.7-89.3%) and a DICE score of 90.4% (86.3-94.4%); for external testing, we obtained an IoU score of 66.7% (63.5-70.0%) and a DICE score of 78.7% (76.0-81.4%). Internal testing of our classification network produced an area under the receiver operating characteristics curve (AUC) of 99.18%, and a Youden index threshold of 0.3806; for external testing, we obtained an AUC of 94.55%, and an accuracy of 94.98% and an F1 score of 85.73% with Youden index. Conclusion: We have developed an AI-based app with an alternative transformer-based segmentation algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring, and could allow for the generation of retrospective data to research into the varied use of treatments for AMD and DR. The modular system of our app can be scaled to add more iterative features based on user feedback for more efficient monitoring. Further study and scaling up of the algorithm dataset could potentially boost its usability in a real-world clinical setting. Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00301-5.

Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review.

PURPOSE: Despite the huge investment in health care, there is still a lack of precise and easily accessible screening systems. With proven associations to many systemic diseases, the eye could potentially provide a credible perspective as a novel screening tool. This systematic review aims to summarize the current applications of ocular image-based artificial intelligence on the detection of systemic diseases and suggest future trends for systemic disease screening. METHODS: A systematic search was conducted on September 1, 2021, using 3 databases-PubMed, Google Scholar, and Web of Science library. Date restrictions were not imposed and search terms covering ocular images, systemic diseases, and artificial intelligence aspects were used. RESULTS: Thirty-three papers were included in this systematic review. A spectrum of target diseases was observed, and this included but was not limited to cardio-cerebrovascular diseases, central nervous system diseases, renal dysfunctions, and hepatological diseases. Additionally, one- third of the papers included risk factor predictions for the respective systemic diseases. CONCLUSIONS: Ocular image - based artificial intelligence possesses potential diagnostic power to screen various systemic diseases and has also demonstrated the ability to detect Alzheimer and chronic kidney diseases at early stages. Further research is needed to validate these models for real-world implementation.

Considerations for Artificial Intelligence Real-World Implementation in Ophthalmology: Providers' and Patients' Perspectives.

ABSTRACT: Artificial Intelligence (AI), in particular deep learning, has made waves in the health care industry, with several prominent examples shown in ophthalmology. Despite the burgeoning reports on the development of new AI algorithms for detection and management of various eye diseases, few have reached the stage of regulatory approval for real-world implementation. To better enable real-world translation of AI systems, it is important to understand the demands, needs, and concerns of both health care professionals and patients, as providers and recipients of clinical care are impacted by these solutions. This review outlines the advantages and concerns of incorporating AI in ophthalmology care delivery, from both the providers' and patients' perspectives, and the key enablers for seamless transition to real-world implementation.

Emergence of non-artificial intelligence digital health innovations in ophthalmology: A systematic review.

The prominent rise of digital health in ophthalmology is evident in the current age of Industry 4.0. Despite the many facets of digital health, there has been a greater slant in interest and focus on artificial intelligence recently. Other major elements of digital health like wearables could also substantially impact patient-focused outcomes but have been relatively less explored and discussed. In this review, we comprehensively evaluate the use of non-artificial intelligence digital health tools in ophthalmology. 53 papers were included in this systematic review - 25 papers discuss virtual or augmented reality, 14 discuss mobile applications and 14 discuss wearables. Most papers focused on the use of technologies to detect or rehabilitate visual impairment, glaucoma and age-related macular degeneration. Overall, the findings on patient-focused outcomes with the adoption of these technologies are encouraging. Further validation, large-scale studies and earlier consideration of real-world barriers are warranted to enable better real-world implementation.

COVID-19 awareness, knowledge and perception towards digital health in an urban multi-ethnic Asian population.

This study aimed to determine COVID-19-related awareness, knowledge, impact and preparedness among elderly Asians; and to evaluate their acceptance towards digital health services amidst the pandemic. 523 participants (177 Malays, 171 Indians, 175 Chinese) were recruited and underwent standardised phone interview during Singapore's lockdown period (07 April till 01 June 2020). Multivariable logistic regression models were performed to evaluate the associations between demographic, socio-economic, lifestyle, and systemic factors, with COVID-19 awareness, knowledge, preparedness, well-being and digital health service acceptance. The average perception score on the seriousness of COVID-19 was 7.6 ± 2.4 (out of 10). 75.5% of participants were aware that COVID-19 carriers can be asymptomatic. Nearly all (≥ 90%) were aware of major prevention methods for COVID-19 (i.e. wearing of mask, social distancing). 66.2% felt prepared for the pandemic, and 86.8% felt confident with government's handling and measures. 78.4% felt their daily routine was impacted. 98.1% reported no prior experience in using digital health services, but 52.2% felt these services would be helpful to reduce non-essential contact. 77.8% were uncomfortable with artificial intelligence software interpreting their medical results. In multivariable analyses, Chinese participants felt less prepared, and more likely felt impacted by COVID-19. Older and lower income participants were less likely to use digital health services. In conclusion, we observed a high level of awareness and knowledge on COVID-19. However, acceptance towards digital health service was low. These findings are valuable for examining the effectiveness of COVID-19 communication in Singapore, and the remaining gaps in digital health adoption among elderly.

Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies.

The coronavirus disease 2019 (COVID-19) pandemic has overwhelmed healthcare services, faced with the twin challenges in acutely meeting the medical needs of patients with COVID-19 while continuing essential services for non-COVID-19 illnesses. The need to re-invent, re-organize and transform healthcare and co-ordinate clinical services at a population level is urgent as countries that controlled initial outbreaks start to experience resurgences. A wide range of digital health solutions have been proposed, although the extent of successful real-world applications of these technologies is unclear. This study aims to review applications of artificial intelligence (AI), telehealth, and other relevant digital health solutions for public health responses in the healthcare operating environment amidst the COVID-19 pandemic. A systematic scoping review was performed to identify potentially relevant reports. Key findings include a large body of evidence for various clinical and operational applications of telehealth (40.1%, n = 99/247). Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n = 111/247) and big data analytics (36.0%, n = 89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations. There were also few descriptions of applications for the internet of things (IoT) (2.0%, n = 5/247), digital platforms for communication (DC) (10.9%, 27/247), digital solutions for data management (DM) (1.6%, n = 4/247), and digital structural screening (DS) (8.9%, n = 22/247); representing gaps and opportunities for digital public health. Finally, the performance of digital health technology for operational applications related to population surveillance and points of entry have not been adequately evaluated.