Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.

Background: Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an ARIAS using true-colour, wide-field confocal scanning images and standard fundus images in the English National Diabetic Eye Screening Programme (NDESP) against human grading. Methods: Cross-sectional study with consecutive recruitment of patients attending annual diabetic eye screening. Imaging with mydriasis was performed (two-field protocol) with the EIDON platform (CenterVue, Padua, Italy) and standard NDESP cameras. Human grading was carried out according to NDESP protocol. Images were processed by EyeArt V.2.1.0 (Eyenuk Inc, Woodland Hills, California). The reference standard for analysis was the human grade of standard NDESP images. Results: We included 1257 patients. Sensitivity estimates for retinopathy grades were: EIDON images; 92.27% (95% CI: 88.43% to 94.69%) for any retinopathy, 99% (95% CI: 95.35% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. For NDESP images: 92.26% (95% CI: 88.37% to 94.69%) for any retinopathy, 100% (95% CI: 99.53% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. One case of vision-threatening retinopathy (R1M1) was missed by the EyeArt when analysing the EIDON images, but identified by the human graders. The EyeArt identified all cases of vision-threatening retinopathy in the standard images. Conclusion: EyeArt identified diabetic retinopathy in EIDON images with similar sensitivity to standard images in a large-scale screening programme, exceeding the sensitivity threshold recommended for a screening test. Further work to optimise the identification of ‘no retinopathy’ and to understand the differential lesion detection in the two imaging systems would enhance the use of these two innovative technologies in a diabetic retinopathy screening setting

3D virtual worlds as environments for literacy learning

Background: Although much has been written about the ways in which new technology might transform educational practice, particularly in the area of literacy learning, there is relatively little empirical work that explores the possibilities and problems - or even what such a transformation might look like in the classroom. 3D virtual worlds offer a range of opportunities for children to use digital literacies in school, and suggest one way in which we might explore changing literacy practices in a playful, yet meaningful context. Purpose: This paper identifies some of the key issues that emerged in designing and implementing virtual world work in a small number of primary schools in the UK. It examines the tensions between different discourses about literacy and literacy learning and shows how these were played out by teachers and pupils in classroom settings.Sources of evidence: Case study data are used as a basis for exploring and illustrating key aspects of design and implementation. The case study material includes views from a number of perspectives including classroom observations, chatlogs, in-world avatar interviews with teachers and also pupils, as well as the author’s field notes of the planning process with accompanying minutes and meeting documents.Main argument: From a Foucauldian perspective, the article suggests that social control of pedagogical practice through the regulation of curriculum time, the normalisation of teaching routines and the regimes of individual assessment restricts teachers’ and pupils’ conceptions of what constitutes literacy. The counternarrative, found in recent work in new litearcies (Lankshear & Knobel, 2006) provides an attractive alternative, but a movement in this direction requires a fundamental shift of emphasis and a re-conceptualisation of what counts as learning.Conclusions: This work on 3D virtual worlds questions the notion of how transformative practice can be achieved with the use of new technologies. It suggests that changes in teacher preparation, continuing professional development as well as wider educational reform may be needed

Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients.

BACKGROUND/AIMS: Human grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard. METHODS: Retinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard. RESULTS: Sensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy. CONCLUSION: The algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed

'Foreigners are stealing our birth right': Moral panics and the discursive construction of Zimbabwean immigrants in South African media

We examine 575 randomly selected articles on Zimbabwean immigrants from the South African Media (SAM) database to expose discourses of exclusion and the production of the psycho-social condition - moral panic. We use critical discourse analysis, notions of remediation and immediacy to scrutinize discourse structures and other discursive strategies designed to conceal mediation and authorial prejudices, and to make the reader 'experience' the actual content. In addition to making the anti-immigrant rhetoric appear legitimate, and the danger immediate and real, we argue that the apparent seamless content is often biased by selection and structured in such a way as to deny voice to immigrants and their advocates. Among other things, we conclude that since the readers' interpretations are filtered through lenses of subjectivities defined by communicative contexts characterized by job scarcity, poverty, crime and wanting healthcare, the news content heightens anxiety and miseducates more than it enlightens readers on migration issues. Hence there is a danger of SAM becoming unwitting conveyors of the same vices they preach against.IS

Digital archives, e-books and narrative space

In this paper we are concerned with the capacity of digital media to enable publics to tell their own environmental stories using digital broadcast archives (DBAs). We consider how digital media afford different ways of telling stories in relation to digital media archives. Central to this discussion is our experience of writing e‐books as part of the AHRC‐funded project “Earth in Vision: BBC coverage of environmental change 1960–2010”. The e‐book format has been adopted in order to explore some of the possibilities for writing environmental history and politics using DBAs

Accentuating institutional brands: A multimodal analysis of the homepages of selected South African universities

In seeking to disentangle themselves from the constraints of apartheid, South African universities have immersed themselves in an identity modification process in which they not only seek to redress the past, but also to reposition their identities as equal opportunity and non-racial institutions. In this paper, we investigate how the University of the Western Cape, the University of Cape Town and Stellenbosch University have used visual and verbal semiotics to re-design their identities on their homepages to appeal to diverse national and international clients. Using Multimodal Discourse Analysis (MDA), we show how the multi-semiotic choices work together on the homepages to give the universities differentiated, competitive, powerful and attractive brands. We conclude that the homepages blended cultural semiotic artefacts, historical, global and transformational discourses, and architectural landscapes to construct different brand identities that, in turn, rebrand the universities from edifices of apartheid education to equal opportunity institutions

Two-year recall for people with no diabetic retinopathy: A multi-ethnic population-based retrospective cohort study using real-world data to quantify the effect

BACKGROUND/AIMS: The English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual screening. Less frequent screening has been advocated among PLD without diabetic retinopathy (DR), but evidence for each ethnic group is limited. We examined the potential effect of biennial versus annual screening on the detection of sight-threatening diabetic retinopathy (STDR) and proliferative diabetic retinopathy (PDR) among PLD without DR from a large urban multi-ethnic English DESP. METHODS: PLD in North-East London DESP (January 2012 to December 2021) with no DR on two prior consecutive screening visits with up to 8 years of follow-up were examined. Annual STDR and PDR incidence rates, overall and by ethnicity, were quantified. Delays in identification of STDR and PDR events had 2-year screening intervals been used were determined. FINDINGS: Among 82 782 PLD (37% white, 36% South Asian, and 16% black people), there were 1788 incident STDR cases over mean (SD) 4.3 (2.4) years (STDR rate 0.51, 95% CI 0.47 to 0.55 per 100-person-years). STDR incidence rates per 100-person-years by ethnicity were 0.55 (95% CI 0.48 to 0.62) for South Asian, 0.34 (95% CI 0.29 to 0.40) for white, and 0.77 (95% CI 0.65 to 0.90) for black people. Biennial screening would have delayed diagnosis by 1 year for 56.3% (1007/1788) with STDR and 43.6% (45/103) with PDR. Standardised cumulative rates of delayed STDR per 100 000 persons for each ethnic group were 1904 (95% CI 1683 to 2154) for black people, 1276 (95% CI 1153 to 1412) for South Asian people, and 844 (95% CI 745 to 955) for white people. INTERPRETATION: Biennial screening would have delayed detection of some STDR and PDR by 1 year, especially among those of black ethnic origin, leading to healthcare inequalities

Nonhuman humanitarianism: when ‘AI for good’ can be harmful

Artificial intelligence (AI) applications have been introduced in humanitarian operations in order to help with the significant challenges the sector is facing. This article focuses on chatbots which have been proposed as an efficient method to improve communication with, and accountability to affected communities. Chatbots, together with other humanitarian AI applications such as biometrics, satellite imaging, predictive modelling and data visualisations, are often understood as part of the wider phenomenon of ‘AI for social good’. The article develops a decolonial critique of humanitarianism and critical algorithm studies which focuses on the power asymmetries underpinning both humanitarianism and AI. The article asks whether chatbots, as exemplars of ‘AI for good’, reproduce inequalities in the global context. Drawing on a mixed methods study that includes interviews with seven groups of stakeholders, the analysis observes that humanitarian chatbots do not fulfil claims such as ‘intelligence’. Yet AI applications still have powerful consequences. Apart from the risks associated with misinformation and data safeguarding, chatbots reduce communication to its barest instrumental forms which creates disconnects between affected communities and aid agencies. This disconnect is compounded by the extraction of value from data and experimentation with untested technologies. By reflecting the values of their designers and by asserting Eurocentric values in their programmed interactions, chatbots reproduce the coloniality of power. The article concludes that ‘AI for good’ is an ‘enchantment of technology’ that reworks the colonial legacies of humanitarianism whilst also occluding the power dynamics at play

"An infinitude of Possible Worlds": towards a research method for hypertext fiction

While the investigation of creative writing as a research method is gathering apace, little work has been done into the specific case of hypertext fiction (fiction written through a digital medium). This paper argues that, while there remain certain similarities between paper-based and digital texts, fundamental differences in design and construction remain. If hypertext fictions are to be successfully understood, then the role and purpose of the digital writer needs to be more fully analysed as part of the creative process. This paper argues that Possible Worlds Theory offers a way forward. With its focus on the ontological structures created by hypertext fiction, Possible World Theory actively embraces narrative indeterminacy and ontological changeability. In this sense the method provides a structured means by which the creative manipulation of the unique affordances of a digital medium by a writer can be theorised

Ethnic disparities in progression rates for sight-threatening diabetic retinopathy in diabetic eye screening: a population-based retrospective cohort study

INTRODUCTION: The English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual eye screening. We examined incidence and determinants of sight-threatening diabetic retinopathy (STDR) in a sociodemographically diverse multi-ethnic population. RESEARCH DESIGN AND METHODS: North East London DESP cohort data (January 2012 to December 2021) with 137 591 PLD with no retinopathy, or non-STDR at baseline in one/both eyes, were used to calculate STDR incidence rates by sociodemographic factors, diabetes type, and duration. HR from Cox models examined associations with STDR. RESULTS: There were 16 388 incident STDR cases over a median of 5.4 years (IQR 2.8-8.2; STDR rate 2.214, 95% CI 2.214 to 2.215 per 100 person-years). People with no retinopathy at baseline had a lower risk of sight-threatening diabetic retinopathy (STDR) compared with those with non-STDR in one eye (HR 3.03, 95% CI 2.91 to 3.15, p<0.001) and both eyes (HR 7.88, 95% CI 7.59 to 8.18, p<0.001). Black and South Asian individuals had higher STDR hazards than white individuals (HR 1.57, 95% CI 1.50 to 1.64 and HR 1.36, 95% CI 1.31 to 1.42, respectively). Additionally, every 5-year increase in age at inclusion was associated with an 8% reduction in STDR hazards (p<0.001). CONCLUSIONS: Ethnic disparities exist in a health system limited by capacity rather than patient economic circumstances. Diabetic retinopathy at first screen is a strong determinant of STDR development. By using basic demographic characteristics, screening programmes or clinical practices can stratify risk for sight-threatening diabetic retinopathy development