Measures of financial toxicity in cancer survivors: a systematic review.

PURPOSE: Comprehensive cancer-related financial toxicity (FT) measures as a multidimensional construct are lacking. The aims of this systematic review were to (1) identify full measures designed explicitly for assessing FT and evaluate their psychometric properties (content validity, structural validity, reliability, and other measurement properties) using Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN), and (2) provide an analysis of the domains of FT covered in these measures. METHODS: MEDLINE, CINAHL, Web of Science, and Cochrane CENTRAL were searched for quantitative studies published from January 2000 to July 2023 that reported psychometric properties of FT measures in cancer survivors. The psychometric properties of FT measures and study risk of bias were analysed using COSMIN. Each FT measure was compared against the six domains of FT recommended by Witte and colleagues. Results were synthesized narratively. The detailed search strategies are available in Table S1. RESULTS: Six FT tools including the COST-FACIT, PROFFIT, FIT, SFDQ, HARDS, and ENRICh-Spanish were identified. The COST-FACIT measure had good measurement properties. No measure reached an excellent level for overall quality but was mostly rated as sufficient. The SFDQ, HARDS, and ENRICh-Spanish were the most comprehensive in the inclusion of the six domains of FT. CONCLUSION: This review emphasizes the need for validated multidimensional FT measures that can be applied across various cancer types, healthcare settings, and cultural backgrounds. Furthermore, a need to develop practical screening tools with high predictive ability for FT is highly important, considering the significant consequences of FT. Addressing these gaps in future research will further enhance the understanding of FT.

Use of an Artificial Intelligence-Generated Vascular Severity Score Improved Plus Disease Diagnosis in Retinopathy of Prematurity.

PURPOSE: To evaluate whether providing clinicians with an artificial intelligence (AI)-based vascular severity score (VSS) improves consistency in the diagnosis of plus disease in retinopathy of prematurity (ROP). DESIGN: Multireader diagnostic accuracy imaging study. PARTICIPANTS: Eleven ROP experts, 9 of whom had been in practice for 10 years or more. METHODS: RetCam (Natus Medical Incorporated) fundus images were obtained from premature infants during routine ROP screening as part of the Imaging and Informatics in ROP study between January 2012 and July 2020. From all available examinations, a subset of 150 eye examinations from 110 infants were selected for grading. An AI-based VSS was assigned to each set of images using the i-ROP DL system (Siloam Vision). The clinicians were asked to diagnose plus disease for each examination and to assign an estimated VSS (range, 1-9) at baseline, and then again 1 month later with AI-based VSS assistance. A reference standard diagnosis (RSD) was assigned to each eye examination from the Imaging and Informatics in ROP study based on 3 masked expert labels and the ophthalmoscopic diagnosis. MAIN OUTCOME MEASURES: Mean linearly weighted κ value for plus disease diagnosis compared with RSD. Area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPR) for labels 1 through 9 compared with RSD for plus disease. RESULTS: Expert agreement improved significantly, from substantial (κ value, 0.69 [0.59, 0.75]) to near perfect (κ value, 0.81 [0.71, 0.86]), when AI-based VSS was integrated. Additionally, a significant improvement in plus disease discrimination was achieved as measured by mean AUC (from 0.94 [95% confidence interval (CI), 0.92-0.96] to 0.98 [95% CI, 0.96-0.99]; difference, 0.04 [95% CI, 0.01-0.06]) and AUPR (from 0.86 [95% CI, 0.81-0.90] to 0.95 [95% CI, 0.91-0.97]; difference, 0.09 [95% CI, 0.03-0.14]). CONCLUSIONS: Providing ROP clinicians with an AI-based measurement of vascular severity in ROP was associated with both improved plus disease diagnosis and improved continuous severity labeling as compared with an RSD for plus disease. If implemented in practice, AI-based VSS could reduce interobserver variability and could standardize treatment for infants with ROP. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity.

Significance: Retinopathy of prematurity (ROP) poses a significant global threat to childhood vision, necessitating effective screening strategies. This study addresses the impact of color channels in fundus imaging on ROP diagnosis, emphasizing the efficacy and safety of utilizing longer wavelengths, such as red or green for enhanced depth information and improved diagnostic capabilities. Aim: This study aims to assess the spectral effectiveness in color fundus photography for the deep learning classification of ROP. Approach: A convolutional neural network end-to-end classifier was utilized for deep learning classification of normal, stage 1, stage 2, and stage 3 ROP fundus images. The classification performances with individual-color-channel inputs, i.e., red, green, and blue, and multi-color-channel fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared. Results: For individual-color-channel inputs, similar performance was observed for green channel (88.00% accuracy, 76.00% sensitivity, and 92.00% specificity) and red channel (87.25% accuracy, 74.50% sensitivity, and 91.50% specificity), which is substantially outperforming the blue channel (78.25% accuracy, 56.50% sensitivity, and 85.50% specificity). For multi-color-channel fusion options, the early-fusion and intermediate-fusion architecture showed almost the same performance when compared to the green/red channel input, and they outperformed the late-fusion architecture. Conclusions: This study reveals that the classification of ROP stages can be effectively achieved using either the green or red image alone. This finding enables the exclusion of blue images, acknowledged for their increased susceptibility to light toxicity.

P Score: A Reference Image-Based Clinical Grading Scale for Vascular Change in Retinopathy of Prematurity.

PURPOSE: The International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), acknowledged that plus-like retinopathy of prematurity (ROP) vascular changes occurs along a spectrum. Historically, clinician-experts demonstrate variable agreement for plus diagnosis. We developed a 9-photograph reference image set for grading plus-like changes and compared intergrader agreement of the set with standard grading with no plus, preplus, and plus disease. DESIGN: Retinal photographic grading and expert consensus opinion. PARTICIPANTS: The development set included 34 international ICROP3 committee members. The validation set included 30 ophthalmologists with ROP expertise (15 ICROP3 committee members and 15 non-ICROP3 members) METHODS: Nine ROP fundus images (P1 through P9) representing increasing degrees of zone I vascular tortuosity and dilation, based on the 34 ICROP3 committee members' gradings and consensus image reviews, were used to establish standard photographs for the plus (P) score. Study participants graded 150 fundus photographs 2 ways, separated by a 1-week washout period: (1) no plus, preplus, or plus disease and (2) choosing the closest P score image. MAIN OUTCOME MEASURES: Intergrader agreement measured by intraclass correlation coefficient. RESULTS: Intergrader agreement was higher using the P score (intraclass correlation coefficient, 0.75; 95% confidence interval, 0.71-0.79) than no plus, preplus, or plus disease (intraclass correlation coefficient, 0.67; 95% confidence interval, 0.62-0.72). Mean ± standard deviation P scores for images with mode gradings of no plus, preplus, and plus disease were 2.5 ± 0.7, 4.8 ± 0.8, and 7.4 ± 0.8, respectively. CONCLUSIONS: Intergrader agreement of plus-like vascular change in ROP using the P score is high. We now incorporate this 9-image reference set into ICROP3 for use in clinician daily practice alongside zone, stage, and plus assessment. P score is not yet meant to replace plus diagnosis for treatment decisions, but its use at our institutions has permitted better comparison between examinations for progression and regression, communication between examiners, and documentation of vascular change without fundus imaging. P score also could provide more detailed ROP classification for clinical trials, consistent with the spectrum of plus-like change that is now formally part of the International Classification of Retinopathy of Prematurity. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Eye Care in Federally Qualified Health Centers.

PURPOSE: To assess changes in vision care availability at Federally Qualified Health Centers (FQHCs) between 2017 and 2021 and whether neighborhood-level demographic social risk factors (SRFs) associated with eye care services provided by FQHCs. DESIGN: Secondary data analysis of the Health Resources and Services Administration (HRSA) data and 2017-2021 American Community Survey (ACS). PARTICIPANTS: Federally Qualified Health Centers. METHODS: Patient and neighborhood characteristics for SRFs were summarized. Differences in FQHCs providing and not providing vision care were compared via Wilcoxon-Mann-Whitney tests for continuous measures and chi-square tests for categorical measures. Logistic regression models were used to test the associations between neighborhood measures and FQHCs providing vision care, adjusted for patient characteristics. MAIN OUTCOME MEASURES: Odds ratios (ORs) with 95% confidence intervals (CIs) for neighborhood-level predictors of FQHCs providing vision care services. RESULTS: Overall, 28.5% of FQHCs (n = 375/1318) provided vision care in 2017 versus 32% (n = 435/1362) in 2021 with some increases and decreases in both the number of FQHCs and those with and without vision services. Only 2.6% of people who accessed FQHC services received eye care in 2021. Among the 435 FQHCs that provided vision care in 2021, 27.1% (n = 118) had added vision services between 2017 and 2021, 71.5% (n = 311) had been offering vision services since at least 2017, and 1.4% (n = 6) were newly established. FQHCs providing vision care in 2021 were more likely to be in neighborhoods with a higher percentage of Hispanic/Latino individuals (OR, 1.08, 95% CI, 1.02-1.14, P = 0.0094), Medicaid-insured individuals (OR, 1.08, 95% CI, 1.02-1.14, P = 0.0120), and no car households (OR, 1.07, 95% CI, 1.01-1.13, P = 0.0142). However, FQHCs with vision care, compared to FQHCs without vision care, served a lower percentage of Hispanic/Latino individuals (27.2% vs. 33.9%, P = 0.0007), Medicaid-insured patients (42.8% vs. 46.8%, P < 0.0001), and patients living at or below 100% of the federal poverty line (61.3% vs. 66.3%, P < 0.0001). CONCLUSIONS: Vision care services are available at a few FQHCs, localized to a few states. Expanding eye care access at FQHCs would meet patients where they seek care to mitigate vision loss to underserved communities. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

ESMO expert consensus statements on the screening and management of financial toxicity in patients with cancer.

BACKGROUND: Financial toxicity, defined as both the objective financial burden and subjective financial distress from a cancer diagnosis and its treatment, is a topic of interest in the assessment of the quality of life of patients with cancer and their families. Current evidence implicates financial toxicity in psychosocial, economic and other harms, leading to suboptimal cancer outcomes along the entire trajectory of diagnosis, treatment, supportive care, survivorship and palliation. This paper presents the results of a virtual consensus, based on the evidence base to date, on the screening and management of financial toxicity in patients with and beyond cancer organized by the European Society for Medical Oncology (ESMO) in 2022. METHODS: A Delphi panel of 19 experts from 11 countries was convened taking into account multidisciplinarity, diversity in health system contexts and research relevance. The international panel of experts was divided into four working groups (WGs) to address questions relating to distinct thematic areas: patients with cancer at risk of financial toxicity; management of financial toxicity during the initial phase of treatment at the hospital/ambulatory settings; financial toxicity during the continuing phase and at end of life; and financial risk protection for survivors of cancer, and in cancer recurrence. After comprehensively reviewing the literature, statements were developed by the WGs and then presented to the entire panel for further discussion and amendment, and voting. RESULTS AND DISCUSSION: A total of 25 evidence-informed consensus statements were developed, which answer 13 questions on financial toxicity. They cover evidence summaries, practice recommendations/guiding statements and policy recommendations relevant across health systems. These consensus statements aim to provide a more comprehensive understanding of financial toxicity and guide clinicians globally in mitigating its impact, emphasizing the importance of further research, best practices and guidelines.

Multinational External Validation of Autonomous Retinopathy of Prematurity Screening.

Importance: Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening. Objective: To evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP. Design, Setting, and Participants: This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023. Exposures: An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine. Main Outcomes and Measures: The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels. Results: The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis. Conclusions and Relevance: Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.

Childhood vision impairment and blindness in West Africa: public health measures and implications for systemic health.

Childhood blindness is an issue of global health impact, affecting approximately 2 million children worldwide. Vision 2020 and the United Nations Sustainable Development Goals previously identified childhood blindness as a key issue in the twentieth century, and while public health measures are underway, the precise etiologies and management require ongoing investigation and care, particularly within resource-limited settings such as sub-Saharan Africa. We systematically reviewed the literature on childhood blindness in West Africa to identify the anatomic classification and etiologies, particularly those causes of childhood blindness with systemic health implications. Treatable causes included cataract, refractive error, and corneal disease. Systemic etiologies identified included measles, rubella, vitamin A deficiency, and Ebola virus disease. While prior public health measures including vitamin A supplementation and vaccination programs have been deployed in most countries with reported data, multiple studies reported preventable or reversible etiologies of blindness and vision impairment. Ongoing research is necessary to standardize reporting for anatomies and/or etiologies of childhood blindness to determine the necessity of further development and implementation of public health measures that would ameliorate childhood blindness and vision impairment.

Genetic association of COVID-19 severe versus non-severe cases by RNA sequencing in patients hospitalised in Hong Kong.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has caused extensive disruption of public health worldwide. There were reports of COVID-19 patients having multiple complications. This study investigated COVID-19 from a genetic perspective. METHODS: We conducted RNA sequencing (RNA-Seq) analysis of respiratory tract samples from 24 patients with COVID-19. Eight patients receiving mechanical ventilation or extracorporeal membrane oxygenation were regarded as severe cases; the remaining 16 patients were regarded as non-severe cases. After quality control, statistical analyses were performed by logistic regression and the Kolmogorov-Smirnov test to identify genes associated with disease severity. RESULTS: Six genes were associated with COVID-19 severity in both statistical tests, namely RPL15, BACE1-AS, CEPT1, EIF4G1, TMEM91, and TBCK. Among these genes, RPL15 and EIF4G1 played roles in the regulation of mRNA translation. Gene ontology analysis showed that the differentially expressed genes were mainly involved in nervous system diseases. CONCLUSION: RNA sequencing analysis showed that severe acute respiratory syndrome coronavirus 2 infection is associated with the overexpression of genes involved in nervous system disorders.

Genome-wide association identifies novel ROP risk loci in a multiethnic cohort.

We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 1 locus at genome-wide significance level (p < 5×10-8) and 9 with significance of p < 5×10-6 for ROP ≥ stage 3. The most significant locus, rs2058019, reached genome-wide significance within the full multiethnic cohort (p = 4.96×10-9); Hispanic and European Ancestry infants driving the association. The lead single nucleotide polymorphism (SNP) falls in an intronic region within the Glioma-associated oncogene family zinc finger 3 (GLI3) gene. Relevance for GLI3 and other top-associated genes to human ocular disease was substantiated through in-silico extension analyses, genetic risk score analysis and expression profiling in human donor eye tissues. Thus, we identify a novel locus at GLI3 with relevance to retinal biology, supporting genetic susceptibilities for ROP risk with possible variability by race and ethnicity.

Mepolizumab improves sense of smell in severe chronic rhinosinusitis with nasal polyps: SYNAPSE.

BACKGROUND: Loss of smell is one of the most bothersome and difficult-to-treat symptoms in patients with severe chronic rhinosinusitis with nasal polyps (CRSwNP). METHODOLOGY: SYNAPSE was a 52-week Phase III study of 4-weekly mepolizumab (100 mg subcutaneously) plus standard of care in adults with severe bilateral CRSwNP. This post hoc analysis assessed changes from baseline to study end in loss of smell visual analogue scale (VAS) symptom score, in patients stratified by several baseline clinical characteristics. SinoNasal Outcomes Test (SNOT)-22 sense of smell/taste item and University of Pennsylvania Smell Identification Test (UPSIT) scores were also assessed. RESULTS: SYNAPSE enrolled 407 patients (mepolizumab=206; placebo=201) with impaired sense of smell at baseline. Improvements from baseline to study end in loss of smell VAS score were greater with mepolizumab versus placebo (treatment difference: -0.37) and most notable in patients with fewer or more recent prior surgeries (treatment difference: 1 vs 2 vs more than 2 prior surgeries,-1.29 vs -0.23 vs -0.07; =3 years since last surgery, -.89 vs 0.22). Approximately 25% of patients had baseline UPSIT scoresavailable; among those scoring =19 by study end. The SNOT-22 sense of smell/taste item score improved with mepolizumab versus placebo. CONCLUSIONS: Mepolizumab treatment improved patients' perceived sense of smell, as measured by loss of smell VAS score and SNOT-22 sense of smell/taste item score in patients with severe refractory CRSwNP.

Evaluating the Relationship between Neighborhood-Level Social Vulnerability and Patient Adherence to Ophthalmology Appointments.

PURPOSE: To examine the association between neighborhood-level social vulnerability and adherence to scheduled ophthalmology appointments. METHODS: In this retrospective cohort study, records of all patients ≥18 years scheduled for an ophthalmology appointment between September 12, 2020, and February 8, 2021, were reviewed. Primary exposure is neighborhood-level Social Vulnerability Index (SVI) based on the patient's residential location. SVI is a rank score of 15 social factors into four themes (socioeconomic status, household composition/disability, minority status/language, and housing type/transportation), ranging from 0 to 1.0, with higher ranks indicating greater social vulnerability. The overall SVI score and each theme were analyzed separately as the primary exposure of interest in multivariable logistic regression models that controlled for age, sex, appointment status (new or established), race, and distance from clinic. The primary outcome, non-adherence, was defined as missing more than 25% of scheduled appointments. RESULTS: Of 8,322 patients (41% non-Hispanic Black, 24% Hispanic, 22% non-Hispanic White) with scheduled appointments, 28% were non-adherent. Non-adherence was associated with greater social vulnerability (adjusted odds ratio [aOR] per 0.01 increase in overall SVI = 2.46 [95% confidence interval, 1.99, 3.06]) and each SVI theme (socioeconomic status: aOR = 2.38 [1.94, 2.91]; household composition/disability: aOR = = 1.51 [1.26, 1.81]; minority status/language: aOR = 2.03 [1.55, 2.68]; housing type/transportation: aOR = 1.41 [1.16, 1.73]). CONCLUSION: Neighborhood-level social vulnerability is associated with greater risk of non-adherence to scheduled ophthalmology appointments, controlling for individual characteristics. Multi-level intervention strategies that incorporate neighborhood-level vulnerabilities are needed to reduce disparities in access to ophthalmology care.

Oxygenation Fluctuations Associated with Severe Retinopathy of Prematurity: Insights from a Multimodal Deep Learning Approach.

Purpose: Retinopathy of prematurity (ROP) is one of the leading causes of blindness in children. Although the role of oxygen in the pathophysiology of ROP is well established, a precise understanding of the dynamic relationship between oxygen exposure ROP incidence and severity is lacking. The purpose of this study was to evaluate the correlation between time-dependent oxygen variables and the onset of ROP. Design: Retrospective cohort study. Participants: Two hundred thirty infants who were born at a single academic center and met the inclusion criteria were included. Infants are mainly born between January 2011 and October 2022. Methods: Patient data were extracted from electronic health records (EHRs), with sufficient time-dependent oxygen data. Clinical outcomes for ROP were recorded as none/mild or moderate/severe (defined as type II or worse). Mixed-effects linear models were used to compare the 2 groups in terms of dynamic oxygen variables, such as daily average and the coefficient of variation (COV) fraction of inspired oxygen (FiO2). Support vector machine (SVM) and long-short-term memory (LSTM)-based multimodal models were trained with fivefold cross-validation to predict which infants would develop moderate/severe ROP. Gestational age (GA), birth weight, and time-dependent oxygen variables were used to develop predictive models. Main Outcome Measures: Model cross-validation performance was evaluated by computing the mean area under the receiver operating characteristic (AUROC) curve, precision, recall, and F1 score. Results: We found that both daily average and COV of FiO2 were associated with more severe ROP (adjusted P < 0.001). With fivefold cross-validation, the multimodal LSTM models had higher performance than the best static models (SVM using GA and 3 average FiO2 features) and SVM models trained on GA alone (mean AUROC = 0.89 ± 0.04 vs. 0.86 ± 0.05 vs. 0.83 ± 0.04). Conclusions: The development of severe ROP might not only be influenced by oxygen exposure but also by its fluctuation, which provides direction for future study of pathophysiological factors associated with severe ROP development. Additionally, we demonstrated that multimodal neural networks can be a method to extract useful information from time-series data, which may be a valuable methodology for the investigation of other diseases using EHR data. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data.

Purpose: This study aims to investigate generalizability of deep learning (DL) models trained on commonly used public fundus images to an instance of real-world data (RWD) for glaucoma diagnosis. Methods: We used Illinois Eye and Ear Infirmary fundus data set as an instance of RWD in addition to six publicly available fundus data sets. We compared the performance of DL-trained models on public data and RWD for glaucoma classification and optic disc (OD) segmentation tasks. For each task, we created models trained on each data set, respectively, and each model was tested on both data sets. We further examined each model's decision-making process and learned embeddings for the glaucoma classification task. Results: Using public data for the test set, public-trained models outperformed RWD-trained models in OD segmentation and glaucoma classification with a mean intersection over union of 96.3% and mean area under the receiver operating characteristic curve of 95.0%, respectively. Using the RWD test set, the performance of public models decreased by 8.0% and 18.4% to 85.6% and 76.6% for OD segmentation and glaucoma classification tasks, respectively. RWD models outperformed public models on RWD test sets by 2.0% and 9.5%, respectively, in OD segmentation and glaucoma classification tasks. Conclusions: DL models trained on commonly used public data have limited ability to generalize to RWD for classifying glaucoma. They perform similarly to RWD models for OD segmentation. Translational Relevance: RWD is a potential solution for improving generalizability of DL models and enabling clinical translations in the care of prevalent blinding ophthalmic conditions, such as glaucoma.

Evaluating spatial dependency of the spectral efficiency in trans-palpebral illumination for widefield fundus photography.

Multi-spectral widefield fundus photography is valuable for the clinical diagnosis and management of ocular conditions that may impact both central and peripheral regions of the retina and choroid. Trans-palpebral illumination has been demonstrated as an alternative to transpupillary illumination for widefield fundus photography without requiring pupil dilation. However, spectral efficiency can be complicated due to the spatial variance of the light property through the palpebra and sclera. This study aims to investigate the effect of light delivery location on spectral efficiency in trans-palpebral illumination. Four narrow-band light sources, covering both visible and near infrared (NIR) wavelengths, were used to evaluate spatial dependency of spectral illumination efficiency. Comparative analysis indicated a significant dependence of visible light efficiency on spatial location, while NIR light efficiency is only slightly affected by the illumination location. This study confirmed the pars plana as the optimal location for delivering visible light to achieve color imaging of the retina. Conversely, spatial location is not critical for NIR light imaging of the choroid.

Preserving polarization maintaining photons for enhanced contrast imaging of the retina.

The purpose of this study is to demonstrate the feasibility of using polarization maintaining photons for enhanced contrast imaging of the retina. Orthogonal-polarization control has been frequently used in conventional fundus imaging systems to minimize reflection artifacts. However, the orthogonal-polarization configuration also rejects the directly reflected photons, which preserve the polarization condition of incident light, from the superficial layer of the fundus, i.e., the retina, and thus reduce the contrast of retinal imaging. We report here a portable fundus camera which can simultaneously perform orthogonal-polarization control to reject back-reflected light from the ophthalmic lens and parallel-polarization control to preserve the backscattered light from the retina which partially maintains the polarization state of the incoming light. This portable device utilizes miniaturized indirect ophthalmoscopy illumination to achieve non-mydriatic imaging, with a snapshot field of view of 101° eye-angle (67° visual-angle). Comparative analysis of retinal images acquired with a traditional orthogonal-polarization fundus camera from both normal and diseased eyes was conducted to validate the usefulness of the proposed design. The parallel-polarization control for enhanced contrast in high dynamic range imaging has also been validated.

Optimizing the OCTA layer fusion option for deep learning classification of diabetic retinopathy.

The purpose of this study is to evaluate layer fusion options for deep learning classification of optical coherence tomography (OCT) angiography (OCTA) images. A convolutional neural network (CNN) end-to-end classifier was utilized to classify OCTA images from healthy control subjects and diabetic patients with no retinopathy (NoDR) and non-proliferative diabetic retinopathy (NPDR). For each eye, three en-face OCTA images were acquired from the superficial capillary plexus (SCP), deep capillary plexus (DCP), and choriocapillaris (CC) layers. The performances of the CNN classifier with individual layer inputs and multi-layer fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared. For individual layer inputs, the superficial OCTA was observed to have the best performance, with 87.25% accuracy, 78.26% sensitivity, and 90.10% specificity, to differentiate control, NoDR, and NPDR. For multi-layer fusion options, the best option is the intermediate-fusion architecture, which achieved 92.65% accuracy, 87.01% sensitivity, and 94.37% specificity. To interpret the deep learning performance, the Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to identify spatial characteristics for OCTA classification. Comparative analysis indicates that the layer data fusion options can affect the performance of deep learning classification, and the intermediate-fusion approach is optimal for OCTA classification of DR.

Artificial intelligence and digital health in global eye health: opportunities and challenges.

Global eye health is defined as the degree to which vision, ocular health, and function are maximised worldwide, thereby optimising overall wellbeing and quality of life. Improving eye health is a global priority as a key to unlocking human potential by reducing the morbidity burden of disease, increasing productivity, and supporting access to education. Although extraordinary progress fuelled by global eye health initiatives has been made over the last decade, there remain substantial challenges impeding further progress. The accelerated development of digital health and artificial intelligence (AI) applications provides an opportunity to transform eye health, from facilitating and increasing access to eye care to supporting clinical decision making with an objective, data-driven approach. Here, we explore the opportunities and challenges presented by digital health and AI in global eye health and describe how these technologies could be leveraged to improve global eye health. AI, telehealth, and emerging technologies have great potential, but require specific work to overcome barriers to implementation. We suggest that a global digital eye health task force could facilitate coordination of funding, infrastructural development, and democratisation of AI and digital health to drive progress forwards in this domain.