Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke

Xie, Li; Yang, Song; Squirrell, David; Vaghefi, Ehsan

doi:10.1371/journal.pone.0225015

Cited by 19 publications

(15 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previously we have demonstrated that it is possible to train an artificial intelligence (AI) deep learning (DL) algorithm on retinal images to grade diabetic retinopathy and maculopathy for diagnostic, screening and risk assessment purposes [30,[36][37][38][39][40][41][42][43][44][45]. In this study we used 110,272 fundus images from a database of 55,118 patients from the UK Biobank and AREDS 1 datasets to train and subsequently test a novel AI platform (CVD-AI) to calculate a 10-year CVD risk score for these individuals.…”

Section: Discussionmentioning

confidence: 99%

“…However, due to the prevalence of low-quality images, a deep learning image quality screening system was used to separate the images into high quality, medium quality, and low-quality images. The training dataset for the image quality screening system was in a similar manner to our prior THEIA system [30]. After the screening process there were 95,992 images from 51,956 patients (Figure 1).…”

Section: Data Preparationmentioning

confidence: 99%

See 1 more Smart Citation

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

Vaghefi

Squirrell

Yang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Purpose: To create and evaluate the accuracy of an artificial intelligence platform capable of using only retinal fundus images to predict both an individual overall 10 year Cardiovascular Disease (CVD) risk and the relative contribution of the component risk factors that comprise this risk (CVD-AI). Methods: The UK Biobank and the US-based AREDS 1 datasets were obtained and used for this study. The UK Biobank data was used for training, validation and testing, while the AREDS 1 dataset was used as an external testing dataset. Overall, we used 110,272 fundus images from 55,118 patient visits. A series of models were trained to predict the risk of CVD against available labels in the UK Biobank dataset. Results: In both the UK Biobank testing dataset and the external validation dataset (AREDS 1), the 10-year CV risk scores generated by CVD-AI were significantly higher for patients who had suffered an actual CVD event when compared to patients who did not experience a CVD event. In the UK Biobank dataset the median 10-year CVD risk for those individuals who experienced a CVD was higher than those who did not (4.9% [ICR 2.9-8%] v 2.3% [IQR 4.3- 1.3%] P<0.01.]. Similar results were observed in the AREDS 1 dataset The median 10-year CVD risk for those individuals who experienced a CVD event was higher than those who did not (6.2% [ICR 3.2%-12.9%] v 2.2% [IQR 3.9- 1.3%] P<0.01 Conclusion: Retinal photography is inexpensive and as fully automated, inexpensive camera systems are now widely available, minimal training is required to acquire them. As such, AI enabled retinal image-based CVD risk algorithms like CVD-AI will make cardiovascular health screening more affordable and accessible. CVD-AI unique ability to assess the relative contribution of the components that comprise an individual overall risk could inform personalized treatment decisions based on the specific needs of an individual, thereby increasing the likelihood of positive health outcomes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Data Preparationmentioning

confidence: 99%

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

Vaghefi

Squirrell

Yang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to subtract local mean color from image, image was processed through equation1. 8 and saturated. With splitting the color channels, the green channel could be used for CLAHE processing or the image could be converted to a gray-scale/binary image.…”

Section: Image Processing Methodsmentioning

confidence: 99%

Untitled

2022

AOWMC

View full text Add to dashboard Cite

Diabetes mellitus is common disease nowadays which could cause blindness. Earlier detection of diabetes signs from retina fundus images could help predicting and preventing the damages. Image processing methods could process the matrix data of pictures as blood vessel segmentation and exudate detection. In this research, the CLAHE algorithm with morphological transformations are used to blood vessel segmentation and determination of the Hessian matrix of images are utilized to detect the exudate blobs.

show abstract

“…National attendance at screening usually falls below recommended rates, particularly for Māori, Pacific and remote communities. 135 Artificial intelligence technology offers ever improving methods to analyse fundus images faster, with greater accuracy and at lower cost than clinicians or technicians.…”

Section: Dovepressmentioning

confidence: 99%

A Systematic Review of Current Teleophthalmology Services in New Zealand Compared to the Four Comparable Countries of the United Kingdom, Australia, United States of America (USA) and Canada

Walsh¹,

Hong²,

Chalakkal³

et al. 2021

OPTH

View full text Add to dashboard Cite

Background: Over 700,000 New Zealanders (NZ), particularly elderly and Māori, live without timely access to specialist ophthalmology services. Teleophthalmology is a widely recognised tool that can assist in overcoming resource and distance barriers. Teleophthalmology gained unprecedented traction in NZ during the COVID-19 pandemic and subsequent lockdown. However, its provision is still limited and there are equity issues. The aim of this study was to conduct a systematic review identifying, describing and contrasting teleophthalmology services in NZ with the comparable countries of Australia, USA, Canada and the United Kingdom. Methods: The electronic databases Embase, PubMed, Web of Science, Google Scholar and Google were systemically searched using the keywords: telemedicine, ophthalmology, teleophthalmology/teleophthalmology. The searches were filtered to the countries above, with no time constraints. An integrative approach was used to synthesise findings. Results: One hundred and thirty-two studies were identified describing 90 discrete teleophthalmology services. Articles spanned from 1997 to 2020. Models were categorised into general eye care (n=21; 16%); emergency/trauma (n=6; 4.5%); school screening (n=25; 19%); artificial intelligence (AI) (n=23; 18%); and disease-specific models of care (MOC) (n=57; 43%). The most common diseases addressed were diabetic retinopathy (n=23; 17%); retinopathy of prematurity (n=9; 7%); and glaucoma (n=8; 6%). Programs were mainly centred in the US (n=72; 54.5%), followed by the UK (n=29; 22%), then Canada (n=16; 12%), Australia (n=13; 10%), with the fewest identified in NZ (n=3; 2%). Models generally involved an ophthalmologist consultative service, remote supervision and triaging. Most models involved local clinicians transmitting fed-forward or live images. Conclusion: Teleophthalmology will likely play a crucial role in the future of eye care. COVID-19 has offered a unique opportunity to observe the use of teleophthalmology services globally. Feed-forward and, increasingly, live-based teleophthalmology services have demonstrated feasibility and cost-effectiveness in similar countries internationally. New Zealand's teleophthalmology services, however, are currently limited. Investing in strategic partnerships and technology at a national level can advance health equities in ophthalmic care.

show abstract

Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke

Cited by 19 publications

References 58 publications

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

Untitled

A Systematic Review of Current Teleophthalmology Services in New Zealand Compared to the Four Comparable Countries of the United Kingdom, Australia, United States of America (USA) and Canada

Contact Info

Product

Resources

About