Multimodal optical imaging of iris flocculi in three consecutive generations: a case report.

Background: Multiple pigmented epithelial cysts at the edge of pupils, that is, iris flocculi, in both eyes, are rare ocular diseases. It has been demonstrated that this disease can be attributed to mutations in the smooth muscle α-actin 2 (ACTA2) gene, which mainly affects the function of smooth muscle cells (SMCs). SMCs are components of the iris, aorta, and several other systemic organs. In addition, iris flocculi are strongly correlated with familial thoracic aortic aneurysm and dissection (TAAD), which is caused by the mutation of amino acid 149 in the ACTA2 gene. Case description: A 6-month-old Chinese boy was found to have iris flocculi during ocular fundus screening for premature infants. His mother, a 30-year-old Chinese woman with a history of aortic dissection, underwent an ophthalmic examination and was found to have iris flocculi. Whole exome sequencing revealed a heterozygous c.445C > T (p. Arg149Cys) mutation in ACTA2 in both the boy and his mother. After his family history was traced, the boy's grandfather was diagnosed with similar iris flocculi. Due to the absence of any ocular complications caused by iris flocculi in the cases, no special treatment was given, and regular follow-up was recommended. Conclusion: We reported one case of familial iris flocculi caused by a heterozygous missense mutation in ACTA2 (p. Arg149Cys) and presented multimodal optical images of both the iris and fundus in three consecutive generations. This case report enriched the clinical features of retinal vasculature and macula associated with the mutation in the amino acid 149 of the ACTA2 gene.

Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy.

Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is relatively underexplored. In this study, using retinal photographs, we evaluated the comparative performances of ViTs and CNNs on detection of referable DR. Design: Retrospective study. Participants: A total of 48 269 retinal images from the open-source Kaggle DR detection dataset, the Messidor-1 dataset and the Singapore Epidemiology of Eye Diseases (SEED) study were included. Methods: Using 41 614 retinal photographs from the Kaggle dataset, we developed 5 CNN (Visual Geometry Group 19, ResNet50, InceptionV3, DenseNet201, and EfficientNetV2S) and 4 ViTs models (VAN_small, CrossViT_small, ViT_small, and Hierarchical Vision transformer using Shifted Windows [SWIN]_tiny) for the detection of referable DR. We defined the presence of referable DR as eyes with moderate or worse DR. The comparative performance of all 9 models was evaluated in the Kaggle internal test dataset (with 1045 study eyes), and in 2 external test sets, the SEED study (5455 study eyes) and the Messidor-1 (1200 study eyes). Main Outcome Measures: Area under operating characteristics curve (AUC), specificity, and sensitivity. Results: Among all models, the SWIN transformer displayed the highest AUC of 95.7% on the internal test set, significantly outperforming the CNN models (all P < 0.001). The same observation was confirmed in the external test sets, with the SWIN transformer achieving AUC of 97.3% in SEED and 96.3% in Messidor-1. When specificity level was fixed at 80% for the internal test, the SWIN transformer achieved the highest sensitivity of 94.4%, significantly better than all the CNN models (sensitivity levels ranging between 76.3% and 83.8%; all P < 0.001). This trend was also consistently observed in both external test sets. Conclusions: Our findings demonstrate that ViTs provide superior performance over CNNs in detecting referable DR from retinal photographs. These results point to the potential of utilizing ViT models to improve and optimize retinal photo-based deep learning for referable DR detection. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes.

OBJECTIVE: In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling peripheral blood from the patient, which is an invasive procedure and not conducive to routine monitoring. Therefore, we developed deep learning algorithm to detect noninvasively hyperuricemia from retina photographs and metadata of patients with diabetes and evaluated performance in multiethnic populations and different subgroups. MATERIALS AND METHODS: To achieve the task of non-invasive detection of hyperuricemia in diabetic patients, given that blood uric acid metabolism is directly related to estimated glomerular filtration rate(eGFR), we first performed a regression task for eGFR value before the classification task for hyperuricemia and reintroduced the eGFR regression values into the baseline information. We trained 3 deep learning models: (1) metadata model adjusted for sex, age, body mass index, duration of diabetes, HbA1c, systolic blood pressure, diastolic blood pressure; (2) image model based on fundus photographs; (3)hybrid model combining image and metadata model. Data from the Shanghai General Hospital Diabetes Management Center (ShDMC) were used to develop (6091 participants with diabetes) and internally validated (using 5-fold cross-validation) the models. External testing was performed on an independent dataset (UK Biobank dataset) consisting of 9327 participants with diabetes. RESULTS: For the regression task of eGFR, in ShDMC dataset, the coefficient of determination (R2) was 0.684±0.07 (95 % CI) for image model, 0.501±0.04 for metadata model, and 0.727±0.002 for hybrid model. In external UK Biobank dataset, a coefficient of determination (R2) was 0.647±0.06 for image model, 0.627±0.03 for metadata model, and 0.697±0.07 for hybrid model. Our method was demonstrably superior to previous methods. For the classification of hyperuricemia, in ShDMC validation, the area, under the curve (AUC) was 0.86±0.013for image model, 0.86±0.013 for metadata model, and 0.92±0.026 for hybrid model. Estimates with UK biobank were 0.82±0.017 for image model, 0.79±0.024 for metadata model, and 0.89±0.032 for hybrid model. CONCLUSION: There is a potential deep learning algorithm using fundus photographs as a noninvasively screening adjunct for hyperuricemia among individuals with diabetes. Meanwhile, combining patient's metadata enables higher screening accuracy. After applying the visualization tool, it found that the deep learning network for the identification of hyperuricemia mainly focuses on the fundus optic disc region.

Occupational therapists' experiences of photo-supported conversations - An intervention in primary health care.

BACKGROUND: The health-promoting intervention BeWell™, which includes photo-supported conversations, is intended for people with stress-related illnesses. Its focus is on improving the individual's health and well-being by addressing what contributes to well-being from the patient's own perspective. There is no current knowledge of the experiences of occupational therapists of using BeWell™ in primary health care. It is thus important to gain knowledge of their experiences of using this intervention as part of investigating its feasibility. AIM: To describe the occupational therapists' experiences of photo-supported conversations about well-being (BeWell™) with patients diagnosed with stress-related illnesses. MATERIAL AND METHODS: Six occupational therapists, working in primary health care, who had conducted the photo-supported conversations about well-being (BeWell™), were interviewed individually, and one focus group discussion was also conducted. Systematic text condensation was used as the analysis method. RESULTS: Three main themes with two to three subgroups in each were identified; Discovering well-being through images, Enhancing patient's own efforts towards well-being, and Contributing to one's own well-being. CONCLUSIONS AND SIGNIFICANCE: The results provide important knowledge for the continued research work with BeWell™ by investigating how the users of the intervention experienced it.

American Society of Retina Specialists Artificial Intelligence Task Force Report.

Since the Artificial Intelligence Committee of the American Society of Retina Specialists developed the initial task force report in 2020, the artificial intelligence (AI) field has seen further adoption of US Food and Drug Administration-approved AI platforms and significant development of AI for various retinal conditions. With expansion of this technology comes further areas of challenges, including the data sources used in AI, the democracy of AI, commercialization, bias, and the need for provider education on the technology of AI. The overall focus of this committee report is to explore these recent issues as they relate to the continued development of AI and its integration into ophthalmology and retinal practice.

Relationship between fundus sex index obtained using color fundus parameters and body height or axial length in the Kumejima population.

PURPOSE: To investigate the relationship between the fundus sex index obtained from fundus photographs and body height or axial length in the Kumejima population. STUDY DESIGN: Prospective cross-sectional observational population study. METHODS: Using color fundus photographs obtained from the Kumejima population, 1,653 healthy right eyes with reliable fundus parameter measurements were included in this study. The tessellation fundus index was calculated as R/(R + G + B) using the mean value of the red-green-blue intensity in the eight locations around the optic disc and foveal region. The optic disc ovality ratio, papillomacular angle, and retinal vessel angle were quantified as previously described. The masculine or feminine fundus was quantified using machine learning (L2 regularized binominal logistic regression and leave one out cross validation), with the range of 0-1 as the predictive value, and defined as the fundus sex index. The relationship between the fundus sex index and body height or axial length was investigated using Spearman's correlation. RESULTS: The mean age of the 838 men and 815 women included in this study was 52.8 and 54.0 years, respectively. The correlation coefficient between fundus sex index and body height was - 0.40 (p < 0.001) in all, 0.01 (p = 0.89) in men, and - 0.04 (p = 0.30) in women, and that between fundus sex index and axial length was - 0.23 (p < 0.001) in all, - 0.12 (p < 0.001) in men, and - 0.13 (p < 0.001) in women. CONCLUSION: This study shows that a larger number of masculine fundi tend to have longer axial lengths in each sex group. However, sex index was not significantly related with body height either in men or in women.

Spectroscopic Benchmarks by Machine Learning as Discriminant Analysis for Unconventional Italian Pictorialism Photography.

Up to the 1930s, the Italian pictorialism movement dominated photography, and many handcrafted procedures started appearing. Each operator had his own working method and his own secrets to create special effects that moved away from the standard processes. Here, a methodology that combines X-ray fluorescence and infrared analysis spectroscopy with unsupervised learning techniques was developed on an unconventional Italian photographic print collection (the Piero Vanni Collection, 1889-1939) to unveil the artistic technique by the extraction of spectroscopic benchmarks. The methodology allowed the distinction of hidden elements, such as iodine and manganese in silver halide printing, or highlighted slight differences in the same printing technique and unveiled the stylistic practice. Spectroscopic benchmarks were extracted to identify the elemental and molecular fingerprint layers, as the oil-based prints were obscured by the proteinaceous binder. It was identified that the pigments used were silicates or iron oxide introduced into the solution or that they retraced the practice of reusing materials to produce completely different printing techniques. In general, four main groups were extracted, in this way recreating the 'artistic palette' of the unconventional photography of the artist. The four groups were the following: (1) Cr, Fe, K, potassium dichromate, and gum arabic bands characterized the dichromate salts; (2) Ag, Ba, Sr, Mn, Fe, S, Ba, gelatin, and albumen characterized the silver halide emulsions on the baryta layer; (3) the carbon prints were benchmarked by K, Cr, dichromate salts, and pigmented gelatin; and (4) the heterogeneous class of bromoil prints was characterized by Ba, Fe, Cr, Ca, K, Ag, Si, dichromate salts, and iron-based pigments. Some exceptions were found, such as the baryta layer being divided into gum bichromate groups or the use of albumen in silver particles suspended in gelatin, to underline the unconventional photography at the end of the 10th century.

Deep Learning Evaluation of Glaucoma Detection Using Fundus Photographs in Highly Myopic Populations.

OBJECTIVES: This study aimed to use deep learning to identify glaucoma and normal eyes in groups with high myopia using fundus photographs. METHODS: Patients who visited Tri-Services General Hospital from 1 November 2018 to 31 October 2022 were retrospectively reviewed. Patients with high myopia (spherical equivalent refraction of ≤-6.0 D) were included in the current analysis. Meanwhile, patients with pathological myopia were excluded. The participants were then divided into the high myopia group and high myopia glaucoma group. We used two classification models with the convolutional block attention module (CBAM), an attention mechanism module that enhances the performance of convolutional neural networks (CNNs), to investigate glaucoma cases. The learning data of this experiment were evaluated through fivefold cross-validation. The images were categorized into training, validation, and test sets in a ratio of 6:2:2. Grad-CAM visual visualization improved the interpretability of the CNN results. The performance indicators for evaluating the model include the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: A total of 3088 fundus photographs were used for the deep-learning model, including 1540 and 1548 fundus photographs for the high myopia glaucoma and high myopia groups, respectively. The average refractive power of the high myopia glaucoma group and the high myopia group were -8.83 ± 2.9 D and -8.73 ± 2.6 D, respectively (p = 0.30). Based on a fivefold cross-validation assessment, the ConvNeXt_Base+CBAM architecture had the best performance, with an AUC of 0.894, accuracy of 82.16%, sensitivity of 81.04%, specificity of 83.27%, and F1 score of 81.92%. CONCLUSIONS: Glaucoma in individuals with high myopia was identified from their fundus photographs.

A Deep Learning-Based Rotten Food Recognition App for Older Adults: Development and Usability Study.

BACKGROUND: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items. OBJECTIVE: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app. METHODS: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants' perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses. RESULTS: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants' perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory. CONCLUSIONS: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

Facial morphology analysis of children with non-syndromic cleft lip and palate in a local population.

Objectives: To study the facial morphology in children with non-syndromic cleft lip and palate by applying numerical facial analysis on photographs for planning and evaluating treatment outcomes. Methods: This descriptive study was conducted from March 2020 to July 2020 in the Department of Oral Pathology, University of Health Sciences and Cleft Lip and Palate Hospital, Lahore Pakistan. A total of 104 patients of both genders with an age range from three months to thirteen years were included. Photographs of the participants were taken to measure facial anthropometrical landmarks including facial height, nose width, mouth width and inter canthal distance. The association between facial measurements with gender and phenotype and across age groups were computed keeping the confidence level at 95%. Results: Mean age of the children was 72.43±44.2 months with slight male predominance. Thirty-one percent presented with bilateral cleft lip and palate followed by unilateral cleft lip and plate. Total mean facial height, nose width and mouth width were found to be 143.46±21.52mm, 32.24±5.03mm and 33.71±4.38mm respectively. Intercanthal distance was measured to be 31.04±5.99mm. Statistically significant association was observed between gender and facial height, nose width, mouth width and Intercanthal distance. Conclusion: Facial anthropometric measures done on frontal photographs can be used to identify the facial landmarks in children with non-syndromic cleft lip and palate in low resource stings that may help surgeons in getting better aesthetic outcomes. These landmarks vary between ethnic groups therefore these should be specific to a particular race and ethnicity so as to ensure proper aesthetics and improved quality of life for the children of all nations.

Influence of the Rater's Gender on Assessing Facial Beauty in Adult Patients With Vertical and Horizontal Growth Patterns.

Background The evaluation of attractiveness varies from one civilization, culture, and environment to another and between individuals. Gender can also play a role in determining the standards of attractiveness. The purpose of this study was to evaluate the effect of the rater's gender on the assessment of adult facial attractiveness with a vertical and horizontal growth pattern in patients with skeletal Class I malocclusion. Methodology The study sample comprised extraoral photos taken before the treatment of 120 patients (30 males and 30 females in each group) with skeletal Class I malocclusion and vertical and horizontal growth patterns according to the Bjork sum aged between 18 and 25 years. A panel of 30 laypersons (aged 19-25 years with an average age of 23 ± 0.53 years), including raters from both genders, were selected equally using a disproportionate stratified sampling method through a computer-generated list. The raters used the visual analog scale (VAS) to provide a score for each photograph's aesthetic quality. The most attractive group, which received the greatest aesthetic score, and the least attractive group, which received the lowest aesthetic score, were the two groups formed based on each photograph's mean aesthetic scores. Overall, 13 patients were chosen for each group. Subsequently, the average assessment score for every patient photo set was determined. Independent-sample t-tests were employed to ascertain if the raters' gender made a statistically significant difference in assessing patients with vertical and horizontal growth patterns. Results There were statistically significant differences between the gender of raters in evaluating female patients with vertical growth patterns (p < 0.001), where the average rating of the female raters was significantly greater than that of the male raters in evaluating female patients. In addition, there were statistically significant differences between the gender of raters in evaluating female patients with horizontal growth patterns (p = 0.009), where the average rating of the male raters was significantly greater than that of the female raters in evaluating female patients. Conclusions There is a limited effect of the rater's gender in evaluating facial aesthetics. However, the facial features of female patients with long faces are preferred by females more than males, and males are more critical in evaluating these patients. On the other hand, males favor the facial features of female patients with short faces more than females, and females are more critical in evaluating these patients. These results suggest considering patients' personal characteristics with vertical and horizontal growth patterns during diagnosis and treatment planning.

Análise da performance de graduandos ingressantes e concluintes do curso de odontologia na identificação humana pela análise de fotografias do sorriso / Analysis of the performance of incoming and outgoing dental students in human identification through the analysis of smile photographs

Introdução: Os dentes são órgãos de valor inestimável para a identificação humana, uma vez que são as estruturas mais estáveis, duras e resistentes do corpo. Com a existência progressiva para o uso de câmeras digitais para captar fotografias sociais, como as "selfie", em que o foco principal está direcionado no rosto do indivíduo, estas fotografias são capazes de prover material com capacidade para resolver casos de identificação humana. Objetivo: Demonstrar a importância das fotografias do sorriso no processo de identificação humana e comparar e verificar o desempenho de graduandos em Odontologia (ingressantes e concluintes) na identificação humana por meio da análise do sorriso. Material e Métodos: Este estudo utilizou a metodologia e banco de imagens do trabalho de Silva et al. (2012), e o público alvo que avaliou as imagens, modificada. Cada aluno participante da pesquisa analisou um grupo de 4 imagens projetadas, sendo 2 de sorriso (A e B) e 2 intrabucais (C e D). Dentre as 10 fornecidas para cada grupo de imagem, somente uma é compatível com cada fotografia projetada. Resultado: Após análise dos dados, foi possível verificar que 84,6% dos alunos ingressantes (número de 26 alunos) acertaram os testes A e B, e 95,6% dos concluintes (número de 23) acertaram os testes A e B. No teste C podemos observar que 61,5% dos alunos ingressantes acertaram o teste, e 69,6% dos concluintes obtiveram acertos. E, por fim, o teste D mostrou que os ingressantes obtiveram um total de 80,8% de acertos, enquanto todos concluintes (100%) acertaram o teste D. Os parâmetros mais citados foram a coloração dos dentes, a inclinação dental, a morfologia das incisais e o alinhamento incisal. Conclusão: Nesta pesquisa, tanto os alunos ingressantes como os concluintes demostraram capacidade para analisar as fotografias do sorriso na identificação humana. Os alunos ingressantes obtiveram um número maior de erros pelo fato de ainda não terem conhecimento teórico e técnico mais aprofundado em anatomia, tornando isso uma diferença entre ingressantes e concluintes

Introduction: Teeth are invaluable organs for human identification, as they are the most stable, hard and resistant structures in the body. With the progressive use of digital cameras to capture social photographs, such as the "selfie", in which the main focus is on the individual's face, these photographs are able to provide material capable of solving human identification cases. Objective: To demonstrate the importance of smile photographs in the process of human identification and to compare and verify the performance of undergraduate dental students (beginners and graduates) in human identification using smile analysis. Material and Methods: This study used the methodology and image bank of the work by SILVA R.F. et al. (2012), and the target audience that evaluated the images, modified. Each student participating in the study analyzed a group of 4 projected images, 2 of which were smiles (A and B) and 2 intraoral (C and D). Of the 10 provided for each image group, only one was compatible with each projected photograph. Results: After analyzing the data, it was possible to see that 84.6% of the incoming students (26 students) got tests A and B right, and 95.6% of the outgoing students (23) got tests A and B right. In test C we can see that 61.5% of incoming students got the test right, and 69.6% of outgoing students got it right. And finally, test D showed that the entrants got a total of 80.8% right, while all the graduates (100%) got test D right. The most cited parameters were tooth color, tooth inclination, incisal morphology and incisal alignment. Conclusion: In this study, both new students and graduates demonstrated their ability to analyze smile photographs in human identification. The new students had a higher number of errors, due to the fact that they did not yet have in-depth theoretical and technical knowledge of anatomy, making this a difference between new students and graduates

Finding Medical Photographs of Patients Online: Randomized, Cross-Sectional Study.

BACKGROUND: Photographs from medical case reports published in academic journals have previously been found in online image search results. This means that patient photographs circulate beyond the original journal website and can be freely accessed online. While this raises ethical and legal concerns, no systematic study has documented how often this occurs. OBJECTIVE: The aim of this cross-sectional study was to provide systematic evidence that patient photographs from case reports published in medical journals appear in Google Images search results. Research questions included the following: (1) what percentage of patient medical photographs published in case reports were found in Google Images search results? (2) what was the relationship between open access publication status and image availability? and (3) did the odds of finding patient photographs on third-party websites differ between searches conducted in 2020 and 2022? METHODS: The main outcome measure assessed whether at least 1 photograph from each case report was found on Google Images when using a structured search. Secondary outcome variables included the image source and the availability of images on third-party websites over time. The characteristics of medical images were described using summary statistics. The association between the source of full-text availability and image availability on Google Images was tested using logistic regressions. Finally, we examined the trend of finding patient photographs using generalized estimating equations. RESULTS: From a random sample of 585 case reports indexed in PubMed, 186 contained patient photographs, for a total of 598 distinct images. For 142 (76.3%) out of 186 case reports, at least 1 photograph was found in Google Images search results. A total of 18.3% (110/598) of photographs included eye, face, or full body, including 10.9% (65/598) that could potentially identify the patient. The odds of finding an image from the case report online were higher if the full-text paper was available on ResearchGate (odds ratio [OR] 9.16, 95% CI 2.71-31.02), PubMed Central (OR 7.90, 95% CI 2.33-26.77), or Google Scholar (OR 6.07, 95% CI 2.77-13.29) than if the full-text was available solely through an open access journal (OR 5.33, 95% CI 2.31-12.28). However, all factors contributed to an increased risk of locating patient images online. Compared with the search in 2020, patient photographs were less likely to be found on third-party websites based on the 2022 search results (OR 0.61, 95% Cl 0.43-0.87). CONCLUSIONS: A high proportion of medical photographs from case reports was found on Google Images, raising ethical concerns with policy and practice implications. Journal publishers and corporations such as Google are best positioned to develop an effective remedy. Until then, it is crucial that patients are adequately informed about the potential risks and benefits of providing consent for clinicians to publish their images in medical journals.

Drivers of autochthonous malaria cases over time: could the Central European present the African future?

BACKGROUND: Results of spatial and temporal comparison of malaria hotspots and coldspots could improve the health measures of malaria control and eradication strategies. The study aimed to reveal the spatially and temporally independent correlations between the potentially most effective background variables and the number of autochthonous malaria cases. METHODS: Relationships between malaria cases and background variables were studied in 2 km × 2 km sized quadrates (10 Central European and 10 African). In addition to the current habitat structure of the African sites, annual precipitation, and annual mean temperature, data of the above parameters detected in the nineteenth and twentieth centuries and currently in the Central European sites were included in the analyses (n = 40). Mann-Whitney tests, Principal Component Analysis, and Generalized Linear Models were used for the examinations. RESULTS: In addition to the apparent significant positive correlation of malaria cases with annual rainfall and mean temperature, several correlations were found for habitat parameters. The cover of marshlands in the 19th-century habitat structure of Central European quadrates was considerably the same as in the recent African ones. The extent of rural residential areas was significantly smaller in the 19th-century habitat structure of Central European quadrats than in present-day African ones. According to the revealed correlations, the surface cover of rural residential areas is the main driver of the number of autochthonous malaria cases that we can directly impact. CONCLUSIONS: The study confirmed with historical comparison that not only the annual rainfall and mean temperature, the cover of marshlands and other habitats with breeding sites, but also the elements of the rural human environment play a significant role in the high number of autochthonous malaria cases, probably through the concentration and enhancing sites for vector mosquitoes. The latter confirms that a rapid urbanization process could reduce malaria cases in the most infected areas of Africa. Until the latter happens, extensive biological control of Anopheles larvae and chemical control (both outdoor and indoor) of their imagoes, further mosquito nets, repellents, and carbon dioxide traps will need to be applied more widely in the most heavily infested areas.

Tonsils at Telethon: developing a standardised collection of tonsil photographs for group A streptococcal (GAS) research.

Introduction: Group A streptococcus (GAS) infections, such as pharyngitis and impetigo, can lead to rheumatic fever and rheumatic heart disease (RHD). Australian Aboriginal and Torres Strait Islander populations experience high rates of RHD and GAS skin infection, yet rates of GAS pharyngitis are unclear. Anecdotally, clinical presentations of pharyngitis, including tonsillar hypertrophy and sore throat, are uncommon. This study aimed to develop a standardised set of tonsil photographs and determine tonsil size distribution from an urban paediatric population. Methods: A prospective cohort of children aged 3-15 years were recruited at the public events "Discover Day" and "Telethon Weekend" (October 2017) in Perth, Western Australia, Australia. Tonsil photographs, symptomatology, and GAS rapid antigen detection tests (RADT) were collected. Tonsil size was graded from the photographs using the Brodsky Grading Scale of tonsillar hypertrophy (Brodsky) by two independent clinicians, and inter-rater reliability calculated. Pharyngitis symptoms and GAS RADT were correlated, and immediate results provided. Results: Four hundred and twenty-six healthy children participated in the study over three days. The median age was seven years [interquartile range (IQR) 5.9-9.7 years]. Tonsil photographs were collected for 92% of participants, of which 62% were rated as good-quality photographs and 79% were deemed of adequate quality for assessment by both clinicians. When scored by two independent clinicians, 57% received the same grade. Average Brodsky grades (between clinicians) were 11%, 35%, 28%, 22% and 5% of grades 0,1,2,3 and 4, respectively. There was moderate agreement in grading using photographs, and minimal to weak agreement for signs of infection. Of 394 participants, 8% reported a sore throat. Of 334 GAS RADT performed, <1% were positive. Discussion: We report the first standardised use of paediatric tonsil photographs to assess tonsil size in urban-living Australian children. This provides a proof of concept from an urban-living cohort that could be compared with children in other settings with high risk of GAS pharyngitis or rheumatic fever such as remote-living Australian Indigenous populations.

Studies on the Authorship of Albumen Vintage Photographs: A Combined Experimental and Chemometric Approach.

The differences in albumen photographs from vintage photographic studios were identified by energy-dispersive X-ray fluorescence spectroscopy and Fourier transform infrared spectroscopy. The results inspired the concept of finding common features characteristic of a given photographic studio. The obtained measurement data (i.e., positions of vibrational bands for characteristic groups of albumen and the mass contents of chosen elements) were analyzed chemometrically by employing the Principal Component Analysis (PCA). The PCA technique allowed us to reduce the number of relevant experimental parameters characterizing the unique features of the photographic objects. The two major components were able to distinguish the photographic objects in terms of their authorship and the time to produce a photograph. The method developed was examined for a selected group of photographs consisting of albumen prints from three Polish photographic ateliers. To validate ED-XRF measurements and, consequently, the chemometric findings, reference albumen photo samples were designed and prepared. The empirical functional relationships between the content of photochemically reduced silver particles on the photographic paper and several physicochemical factors, including time of exposure to UV light, AgNO3 concentration in a fixed bath, and concentrations of other additives, were proposed. These results can be used for the prediction of the experimental conditions under which the investigated photographs were developed.

Diagnostic Utility of Swept-Source OCT-Based Biometry and Fundus Photographs Compared to Spectral Domain OCT in Center-Involving Diabetic Macular Edema.

PURPOSE: This study was aimed to evaluate the agreement between the swept-source optical coherence tomography (SS-OCT)-based biometry, fundus photographs, and their combination, in comparison to the gold standard spectral-domain optical coherence tomography (SD-OCT) for the detection of center-involving diabetic macular edema (CI-DME). METHODS: We conducted a retrospective cross-sectional study involving 55 subjects (78 eyes) diagnosed with diabetic macular edema (DME) detected clinically and on SD-OCT (Carl Zeiss Meditec AG). Post-mydriatic 45-degree color fundus photograph (Crystal-Vue NFC-700), 1 mm macular scan obtained from SS-OCT-based biometry (IOL-Master 700), and macula cube scan obtained from SD-OCT was used to detect and grade DME into CI-DME and NCI-DME. RESULTS: Our findings revealed that SS-OCT-based biometry was noted to have a high sensitivity of 1 (0.94-1.00) and a specificity of 0.63 (0.31-0.89) in detecting CI-DME compared to the gold standard (SD-OCT). When combined with data from fundus photographs, specificity decreased to 0.32 (0.15-0.53). Fundus photographs alone exhibited a low sensitivity of 0.52 (0.38-0.64) and a specificity of 0.45 (0.16-0.76) in CI-DME detection. CONCLUSION: In conclusion, SS-OCT-based biometry can be used as an effective tool for the detection of CI-DME in diabetic patients undergoing cataract surgery and can serve as a screening tool in centers without SD-OCT facilities.

Diabetic Macular Edema (DME); Center Involving Diabetic Macular Edema (CI-DME); Non-Center Involving Diabetic Macular Edema (NCI-DME); Swept-Source Optical Coherence Tomography (SS-OCT); Spectral-Domain Optical Coherence Tomography (SD-OCT); Anti-Vascular Endothelial Growth Factor (Anti-VEGF); Central Retinal Thickness (CRT); Intra Retinal Fluid (IRF); Sub Retinal Fluid (SRF); Diabetic Retinopathy (DR); Non Proliferative Diabetic Retinopathy (NPDR); Proliferative Diabetic Retinopathy (PDR); Best Corrected Visual Acuity (BCVA); Glycosylated hemoglobin (HbA1c); Mean Spherical Error (MSE); Standard Deviation (SD); Positive Predictive value (PPV); Predictive value (PPV); Negative predictive value (NPV); Area under the Curve (AUC).

Age prediction using fundus parameters of normal eyes from the Kumejima population study.

PURPOSE: Artificial intelligence can predict the age of an individual using color fundus photographs (CFPs). This study aimed to investigate the accuracy of age prediction in the Kumejima study using fundus parameters and to clarify age-related changes in the fundus. METHODS: We used nonmydriatic CFPs obtained from the Kumejima population study, including 1,646 right eyes of healthy participants with reliable fundus parameter measurements. The tessellation fundus index was calculated as R/(R + G + B) using the mean value of the red-green-blue intensity in eight locations around the optic disc and foveal region. The optic disc ovality ratio, papillomacular angle, and retinal vessel angle were quantified as previously described. Least absolute shrinkage and selection operator regression with leave-one-out cross-validation was used to predict age. The relationship between the actual and predicted ages was investigated using Pearson's correlation coefficient. RESULTS: The mean age of included participants (834 males and 812 females) was 53.4 ± 10.1 years. The mean predicted age based on fundus parameters was 53.4 ± 8.9 years, with a mean absolute error of 3.64 years, and the correlation coefficient between actual and predicted age was 0.88 (p < 0.001). Older patients had greater red and green intensities and weaker blue intensities in the peripapillary area (p < 0.001). CONCLUSIONS: Age could be predicted using the CFP parameters, and there were notable age-related changes in the peripapillary color intensity. The age-related changes in the fundus may aid the understanding of the mechanism of fundus diseases such as age-related macular degeneration.