Evaluation of AI-Enhanced Non-Mydriatic Fundus Photography for Diabetic Retinopathy Screening.

OBJECTIVE: To assess the feasibility of using non-mydriatic fundus photography in conjunction with an artificial intelligence (AI) reading platform for large-scale screening of diabetic retinopathy (DR). METHODS: In this study, we selected 120 patients with diabetes hospitalized in our institution from December 2019 to April 2021. Retinal imaging of 240 eyes was obtained using non-mydriatic fundus photography. The fundus images of these patients were divided into two groups based on different interpretation methods. In Experiment Group 1, the images were analyzed and graded for DR diagnosis using an AI reading platform. In Experiment Group 2, the images were analyzed and graded for DR diagnosis by an associate chief physician in ophthalmology, specializing in fundus diseases. Concurrently, all patients underwent the gold standard for DR diagnosis and grading-fundus fluorescein angiography (FFA)-with the outcomes serving as the Control Group. The diagnostic value of the two methods was assessed by comparing the results of Experiment Groups 1 and 2 with those of the Control Group. RESULTS: Keeping the control group (FFA results) as the gold standard, no significant differences were observed between the two experimental groups regarding diagnostic sensitivity, specificity, false positive rate, false negative rate, positive predictive value, negative predictive value, Youden's index, Kappa value, and diagnostic accuracy (X2 = 0.371, P > 0.05). CONCLUSION: Compared with the manual reading group, the AI reading group revealed no significant differences across all diagnostic indicators, exhibiting high sensitivity and specificity, as well as a relatively high positive predictive value. Additionally, it demonstrated a high level of diagnostic consistency with the gold standard. This technology holds potential for suitability in large-scale screening of DR.

Outdated and re-configured: Challenging linear conceptualizations of ageing through the case of revived obsolete technologies.

Taking as a starting point the conventional view of ageing as a linear process beginning in a youthful and productive stage but gradually deteriorating, this paper shifts the usual anthropocentric focal point towards technological artifacts which do not conform to this typical view. More specifically, three examples of technologies previously considered obsolete, but which have seen a revival in the last decade, are presented: the so-called dumbphones, analogue cameras, and vinyl players. Although very different at first glance, the three cases of these revived technologies show a similar evolution trajectory which breaks from the typical view of ageing in technological artifacts. Instead, they indicate how their revival does not simply entail a reconsideration of their initial value (such as it is often the case with antiques or heirlooms), but a transformation, hybridisation, and re-envisioned purpose. To this effect, the agential realism theory is applied to show how the revival of technological artifacts and practices once considered outdated attempts to dissolve binaries such as old/new, young/old, or slow/fast. Furthermore, such artifacts reveal trajectories of ageing that are unlike their human counterparts, but which can make way for new manners of articulating issues pertaining to ageing as a process in humans as well. The contribution of the paper lies in illustrating how adopting a non-linear view of ageing and fundamentally questioning its inherent binaries has the capacity to produce a much-needed nuanced view of ageing in humans, non-humans, and their sociomaterial entanglements.

ETDRS grading with CLARUS ultra-widefield images shows agreement with 7-fields colour fundus photography.

BACKGROUND: To analyse and compare the grading of diabetic retinopathy (DR) severity level using standard 35° ETDRS 7-fields photography and CLARUS™ 500 ultra-widefield imaging system. METHODS: A cross-sectional analysis of retinal images of patients with type 2 diabetes (n = 160 eyes) was performed for this study. All patients underwent 7-fields colour fundus photography (CFP) at 35° on a standard Topcon TRC-50DX® camera, and ultra-widefield (UWF) imaging at 200° on a CLARUS™ 500 (ZEISS, Dublin, CA, USA) by an automatic montage of two 133° images (nasal and temporal). 35° 7-fields photographs were graded by two graders, according to the Early Treatment Diabetic Retinopathy Study (ETDRS). For CLARUS UWF images, a prototype 7-fields grid was applied using the CLARUS review software, and the same ETDRS grading procedures were performed inside that area only. Grading of DR severity level was compared between these two methods to evaluate the agreement between both imaging techniques. RESULTS: Images of 160 eyes from 83 diabetic patients were considered for analysis. According to the 35° ETDRS 7-fields images, 22 eyes were evaluated as DR severity level 10-20, 64 eyes were evaluated as DR level 35, 41 eyes level 43, 21 eyes level 47, 7 eyes level 53, and 5 eyes level 61. The same DR severity level was achieved with CLARUS 500 UWF images in 92 eyes (57%), showing a perfect agreement (k > 0.80) with the 7-fields 35° technique. Fifty-seven eyes (36%) showed a higher DR level with CLARUS UWF images, mostly due to a better visualization of haemorrhages and a higher detection rate of intraretinal microvascular abnormalities (IRMA). Only 11 eyes (7%) showed a lower severity level with the CLARUS UWF system, due to the presence of artifacts or media opacities that precluded the correct evaluation of DR lesions. CONCLUSIONS: UWF CLARUS 500 device showed nearly perfect agreement with standard 35° 7-fields images in all ETDRS severity levels. Moreover, CLARUS images showed an increased ability to detect haemorrhages and IRMA helping with finer evaluation of lesions, thus demonstrating that a UWF photograph can be used to grade ETDRS severity level with a better visualization than the standard 7-fields images. TRIAL REGISTRATION: Approved by the AIBILI - Association for Innovation and Biomedical Research on Light and Image Ethics Committee for Health with number CEC/009/17- EYEMARKER.

Implementing a Photodocumentation Program.

The widespread availability of smart devices has facilitated the use of medical photography, yet photodocumentation workflows are seldom implemented in healthcare organizations due to integration challenges with electronic health records (EHR) and standard clinical workflows. This manuscript details the implementation of a comprehensive photodocumentation workflow across all phases of care at a large healthcare organization, emphasizing efficiency and patient safety. From November 2018 to December 2023, healthcare workers at our institution uploaded nearly 32,000 photodocuments spanning 54 medical specialties. The photodocumentation process requires as few as 11 mouse clicks and keystrokes within the EHR and on smart devices. Automation played a crucial role in driving workflow efficiency and patient safety. For example, body part rules were used to automate the application of a sensitive label to photos of the face, chest, external genitalia, and buttocks. This automation was successful, with over 50% of the uploaded photodocuments being labeled as sensitive. Our implementation highlights the potential for standardizing photodocumentation workflows, thereby enhancing clinical documentation, improving patient care, and ensuring the secure handling of sensitive images.

Global health photography behind the façade of empowerment and decolonisation.

Global health photography has historically been commissioned and, therefore, dominated by the gaze of Western photographers on assignments in the Global South. This is changing as part of international calls to decolonise global health and stimulate 'empowerment', spawning a growing initiative to hire local photographers. This article, based on interviews with global health photographers, reflects on this paradigm shift. It highlights how behind the laudable aim of 'empowerment' of local global health photography there is a simultaneous exploitation of precarious photographer labour and the emergence of 'glocal' photography elites. The paper argues that empowerment of local photographers can become a euphemism for reducing image production costs and maintaining control over the image content, while extending the scope of mainstream global health visual culture without challenging it. Finally, the article amplifies the growing concern that uncritical engagement with institutionalised empowerment becomes a warrant for the reproduction of local inequalities behind the fashionable façade of cooperation and care.

Varying views of maxillary and mandibular aspects of teeth: A dataset.

Real teeth or dental image datasets are a valuable resource that is transforming the field of dentistry by enabling automation, improving diagnostics and accelerating research and development.This article presents a comprehensive dataset containing 9,562 images of healthy teeth (noncarious) from children aged 1 to 14 years. The images capture different views of the teeth, including maxillary (upper) and mandibular (lower) arches, front, right, left, and occlusal (biting surface) views. These images are stored under eight subcategories in the Mendeley repository, a platform for research data. The potential application of this dataset involves using machine learning to analyze the dental condition. This could provide a faster analysis and facilitate remote assessment of dental conditions in underserved areas. Overall, this dataset seems like a promising tool for advancing dental care through the power of machine learning.

A Case of Accidental Autoerotic Death by Hanging Linked to Erotic Photography.

Autoerotic death, as a subtype of mechanical asphyxia, refers to a person's fatal outcome while engaging in solitary sexual activity using various devices and methods to reduce oxygen supply and induce cerebral hypoxia, leading to increased sexual gratification. These asphyxial deaths are accidental and sporadic. In cases of sexual asphyxia, especially when strangulation methods such as hanging or ligature are used, thorough crime scene investigation is crucial to determine the type of asphyxia and the manner of death. Inadequate information about specific crime scene findings can lead to significant errors in determining the manner of death and the type of strangulation, potentially leading to cases being mistaken for ligature strangulation in a homicidal manner or hanging in a suicidal manner.

Evaluating the potential of retinal photography in chronic kidney disease detection: a review.

Background: Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. Objectives: The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. Survey Methodology: A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Results: Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. Conclusion: In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.

Enhancing the ophthalmic AI assessment with a fundus image quality classifier using local and global attention mechanisms.

Background: The assessment of image quality (IQA) plays a pivotal role in the realm of image-based computer-aided diagnosis techniques, with fundus imaging standing as the primary method for the screening and diagnosis of ophthalmic diseases. Conventional studies on fundus IQA tend to rely on simplistic datasets for evaluation, predominantly focusing on either local or global information, rather than a synthesis of both. Moreover, the interpretability of these studies often lacks compelling evidence. In order to address these issues, this study introduces the Local and Global Attention Aggregated Deep Neural Network (LGAANet), an innovative approach that integrates both local and global information for enhanced analysis. Methods: The LGAANet was developed and validated using a Multi-Source Heterogeneous Fundus (MSHF) database, encompassing a diverse collection of images. This dataset includes 802 color fundus photography (CFP) images (302 from portable cameras), and 500 ultrawide-field (UWF) images from 904 patients with diabetic retinopathy (DR) and glaucoma, as well as healthy individuals. The assessment of image quality was meticulously carried out by a trio of ophthalmologists, leveraging the human visual system as a benchmark. Furthermore, the model employs attention mechanisms and saliency maps to bolster its interpretability. Results: In testing with the CFP dataset, LGAANet demonstrated remarkable accuracy in three critical dimensions of image quality (illumination, clarity and contrast based on the characteristics of human visual system, and indicates the potential aspects to improve the image quality), recording scores of 0.947, 0.924, and 0.947, respectively. Similarly, when applied to the UWF dataset, the model achieved accuracies of 0.889, 0.913, and 0.923, respectively. These results underscore the efficacy of LGAANet in distinguishing between varying degrees of image quality with high precision. Conclusion: To our knowledge, LGAANet represents the inaugural algorithm trained on an MSHF dataset specifically for fundus IQA, marking a significant milestone in the advancement of computer-aided diagnosis in ophthalmology. This research significantly contributes to the field, offering a novel methodology for the assessment and interpretation of fundus images in the detection and diagnosis of ocular diseases.

Standardization of Oral Photography in Dogs and Cats.

Oral photography is one of the essential methods of maintaining dental records. The primary purpose of photography is to record an image. Images of the oral cavity should have sufficient detail for discerning the features of the hard and soft tissues; in addition, they should accurately reproduce the real colors as they appear in the mouth. Photographs are of value during the repeated monitoring of patients and while making comparisons among historical cohort studies. This article describes the equipment and techniques for obtaining high-quality images of the oral cavities of dogs and cats in a standardized way.

The use and ethics of dental photography and social media at an oral healthcare training centre in South Africa.

Background: The ethics surrounding the use and sharing of photographs on social media has come under the spotlight as the Protection of Personal Information Act (POPI Act) has come into play. Aim: The aim is to determine the use, ethical practice and sharing of dental photography on social media among qualified and undergraduate oral health practitioners at a dental school in South Africa. Methods: A cross-sectional study design was used on staff and students at the University of the Western Cape's Dental Faculty in 2022. Chi-squared and Fisher's exact tests were used to determine associations between the different graduation statuses and various demographic factors. Results: From the 80 undergraduate students and 46 qualified oral healthcare practitioners, the majority were aware that photography could be used in dentistry, and 87.3% (n = 110) took photographs of the dental treatments performed on their patients. Only 60.3% of the participants attended an ethical course that addressed issues with social media and digital photography. Almost 80% (n = 100) of the participants did not feel that they needed to mention all the social media platforms that they would use with their patients' photographs before sharing. Conclusion: Dental photography is being used and sometimes shared on social media platforms by some students and staff at university level. Not all participants have attended an ethical course on clinical photography. Dental training needs to include an ethical course on dental photography and the use of sharing photographs on social media. Contribution: Good ethical practice regarding clinical photographs in all undergraduate and postgraduate curriculums, to eliminate any uncertainty.

From Photography to Radiology: How Physicians Leveraged Early Hospital X-ray Machines to Supplant Photographers.

At the end of the nineteenth century, the advent of x-ray machines fueled American medicine's reliance on technology, transforming hospitals and the medical profession. X-ray manufacturers pursued the nascent hospital market as competition and patent feuds accelerated x-ray machine modifications. Hospitals incorporated clunky new machines and employed x-ray photographers, but as the unruly apparatus stabilized, physicians joining the new specialty of radiology discounted the toils of machine troubleshooting and promoted their medically qualified x-ray interpretations. This article frames early medical radiography in terms of boundary work, highlighting how discourse among physicians, x-ray photographers, and hospital administrators vied to establish a privileged demarcation between radiological science and photographic craft. Ultimately, radiologists supplanted x-ray photographers by leveraging the automation of x-ray machines and capitalizing on the epistemic shift from photographic objectivity to qualified interpretations. By focusing on this overlooked aspect of x-ray incorporation into hospitals, this work provides a unique perspective on how harnessing mechanization and authoritative medical interpretations can shift professional boundaries.

Combining functional and morphological retinal vascular characteristics achieves high-precision diagnosis of mild non-proliferative diabetic retinopathy.

BACKGROUND: To explore the functional and morphological variations of retinal vessels in diabetes with no clinically detectable retinopathy (NDR) and mild non-proliferative diabetic retinopathy (NPDR) and to establish a high-performance mild NPDR diagnostic model. METHODS: Normal subjects and type 2 diabetes patients with NDR and mild NPDR were recruited. Oxygen-saturation-related functional parameter (optical density ratio ODR) and morphological characteristics (fractal dimension Df, vessel area rate VAR, mean vascular diameter Dm, vessel tortuosity τ) of different vascular areas were extracted with single fundus photography and comprehensively analyzed among groups. An interpretable model combining marine predator algorithm (MPA) and support vector machine (SVM) based on characteristic selection was proposed for mild NPDR diagnosis. RESULTS: A total of 91 NDR subjects, 75 mild NPDR subjects, and 111 sex- and age-matched normal controls were analyzed. Increased main vessels ODR, while lower VAR of all areas except outer ring macula, lower Dm of all vessels and decreased τ of all areas were associate with NDR (e.g. main vessels ODR: OR [95%CI] 1.42[1.07-1.89], full macula τ:0.53[0.38-0.74]). Increased ODR of all areas, higher Dm of all areas except inner ring macula, increased inner ring macula τ, while decreased Df of full and inner ring macula, lower VAR of all areas were associate with mild NPDR (e.g. main vessels ODR:5.68[3.03-10.65], inner ring macula VAR: 0.48[0.33-0.69]). The MPA-SVM model with selected characteristics obtained the best diagnosis performance (AUC:0.940 ± 0.014; Accuracy:90.4 ± 3.9%; Sensitivity:89.2 ± 6.4%; Specificity:91.3 ± 6.4%). CONCLUSIONS: More significant retinal vascular variations are associate with the incidence of mild NPDR than NDR. High-precision mild NPDR diagnosis is achieved combining the morphological and functional vascular characteristics based on characteristic selection.

Enhancing diabetic retinopathy screening: Non-mydriatic fundus photography-assisted telemedicine for improved clinical management.

The utilization of non-mydriatic fundus photography-assisted telemedicine to screen patients with diabetes mellitus for diabetic retinopathy provides an accurate, efficient, and cost-effective method to improve early detection of disease. It has also been shown to correlate with increased participation of patients in other aspects of diabetes care. In particular, patients who undergo teleretinal imaging are more likely to meet Comprehensive Diabetes Care Healthcare Effectiveness Data and Information Set metrics, which are linked to preservation of quality-adjusted life years and additional downstream healthcare savings.

Evaluation of sensitivity and specificity of the slit-light method in the diagnosis of cyclotorsion.

PURPOSE: The purpose of this study was to evaluate the amount of sensitivity and specificity of the slit-light (SL) method in the diagnosis of ocular cyclotorsion. MATERIALS AND METHODS: One hundred and twenty eyes of 60 individuals (10-60 years old), with mean visual acuity of 0.08 ± 0.14 LogMAR, were divided into two groups (normal and torsion groups). Individuals without ocular motility disorder were selected as normal and patients with extraocular motility disorders and oblique muscle dysfunctions as the torsion group. The sensitivity and specificity of SL in the diagnosis of ocular torsion were measured by masked investigators and compared to fundus photography (FP). Inter- and intraobserver variability of these techniques was also determined. RESULTS: The amounts of sensitivity and specificity of SL, measured by the first examiner, were 60% and 92% for intorsion and 50% and 96% for extorsion assessment, respectively. These amounts were 53% and 95% for intorsion, and 54% and 97% for extorsion by the second examiner. The contingency coefficient between the two examiners was 68.6% for SL. This amount was 61% between FP and SL for the first examiner and 63% for the second. The contingency coefficient for the repeatability of SL was 72.2% for the first examiner and 75.7% for the second. This amount was 71.2% between the two examiners. CONCLUSION: SL can be considered a useful method for the diagnosis of cyclotorsion.

Deep learning for predicting circular retinal nerve fiber layer thickness from fundus photographs and diagnosing glaucoma.

Purpose: This study aimed to propose a new deep learning (DL) approach to automatically predict the retinal nerve fiber layer thickness (RNFLT) around optic disc regions in fundus photography trained by optical coherence tomography (OCT) and diagnose glaucoma based on the predicted comprehensive information about RNFLT. Methods: A total of 1403 pairs of fundus photographs and OCT RNFLT scans from 1403 eyes of 1196 participants were included. A residual deep neural network was trained to predict the RNFLT for each local image in a fundus photograph, and then a RNFLT report was generated based on the local images. Two indicators were designed based on the generated report. The support vector machines (SVM) algorithm was used to diagnose glaucoma based on the two indicators. Results: A strong correlation was found between the predicted and actual RNFLT values on local images. On three testing datasets, we found the Pearson r to be 0.893, 0.850, and 0.831, respectively, and the mean absolute error of the prediction to be 14.345, 17.780, and 19.250 µm, respectively. The area under the receiver operating characteristic curves for discriminating glaucomatous from healthy eyes was 0.860 (95 % confidence interval, 0.799-0.921). Conclusions: We established a novel local image-based DL approach to provide comprehensive quantitative information on RNFLT in fundus photographs, which was used to diagnose glaucoma. In addition, training a deep neural network based on local images to predict objective detail information in fundus photographs provided a new paradigm for the diagnosis of ophthalmic diseases.

Low-cost, high-volume imaging for entomological digitization.

Large-scale digitization of natural history collections requires automation of image acquisition and processing. Reflecting this fact, various approaches, some highly sophisticated, have been developed to support imaging of museum specimens. However, most of these systems are complex and expensive, restricting their deployment. Here we describe a simple, inexpensive technique for imaging arthropods larger than 5 mm. By mounting a digital SLR camera on a CNC (computer numerical control) motor-drive rig, we created a system that captures high-resolution z-axis stacked images (6960 × 4640 pixels) of 95 specimens in 30 minutes. This system can be assembled inexpensively ($1000 USD without a camera) and it is easy to set-up and maintain. By coupling low cost with high production capacity, it represents a solution for digitizing any natural history collection.