Melatonin suppression by light involves different retinal photoreceptors in young and older adults.

Age-related sleep and circadian rhythm disturbances may be due to altered nonvisual photoreception. Here, we investigated the temporal dynamics of light-induced melatonin suppression in young and older individuals. In a within-subject design study, young and older participants were exposed for 60 min (0030-0130 at night) to nine narrow-band lights (range: 420-620 nm). Plasma melatonin suppression was calculated at 15, 30, 45, and 60 min time intervals. Individual spectral sensitivity of melatonin suppression and photoreceptor contribution were predicted for each interval and age group. In young participants, melanopsin solely drove melatonin suppression at all time intervals, with a peak sensitivity at 485.3 nm established only after 15 min of light exposure. Conversely, in older participants, spectral light-driven melatonin suppression was best explained by a more complex model combining melanopsin, S-cone, and M-cone functions, with a stable peak (~500 nm) at 30, 45, and 60 min of light exposure. Aging is associated with a distinct photoreceptor contribution to melatonin suppression by light. While in young adults melanopsin-only photoreception is a reliable predictor of melatonin suppression, in older individuals this process is jointly driven by melanopsin, S-cone, and M-cone functions. These findings offer new prospects for customizing light therapy for older individuals.

Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied. METHODS: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS: The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS: A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke-NUS Ophthalmology and Visual Sciences Academic Clinical Program.).

Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities.

BACKGROUND: The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown. METHODS: In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists. The areas under the receiver-operating-characteristic curves were calculated for the BONSAI-DLS. Error rates, accuracy, sensitivity, and specificity of the algorithm were compared with those of 30 clinicians with or without ophthalmic training (6 general ophthalmologists, 6 optometrists, 6 neurologists, 6 internists, 6 emergency department [ED] physicians) who graded the same testing set of images. RESULTS: With an error rate of 15.3%, the DLS outperformed all clinicians (average error rates 24.4%, 24.8%, 38.2%, 44.8%, 47.9% for general ophthalmologists, optometrists, neurologists, internists and ED physicians, respectively) in the overall classification of optic disc appearance. The DLS displayed significantly higher accuracies than 100%, 86.7% and 93.3% of clinicians (n = 30) for the classification of papilledema, normal, and other disc abnormalities, respectively. CONCLUSIONS: The performance of the BONSAI-DLS to classify optic discs on fundus photographs was superior to that of clinicians with or without ophthalmic training. A trained DLS may offer valuable diagnostic aid to clinicians from various clinical settings for the screening of optic disc abnormalities harboring potentially sight- or life-threatening neurological conditions.

Rods contribute to the light-induced phase shift of the retinal clock in mammals.

While rods, cones, and intrinsically photosensitive melanopsin-containing ganglion cells (ipRGCs) all drive light entrainment of the master circadian pacemaker of the suprachiasmatic nucleus, recent studies have proposed that entrainment of the mouse retinal clock is exclusively mediated by a UV-sensitive photopigment, neuropsin (OPN5). Here, we report that the retinal circadian clock can be phase shifted by short duration and relatively low-irradiance monochromatic light in the visible part of the spectrum, up to 520 nm. Phase shifts exhibit a classical photon dose-response curve. Comparing the response of mouse models that specifically lack middle-wavelength (MW) cones, melanopsin, and/or rods, we found that only the absence of rods prevented light-induced phase shifts of the retinal clock, whereas light-induced phase shifts of locomotor activity are normal. In a "rod-only" mouse model, phase shifting response of the retinal clock to light is conserved. At shorter UV wavelengths, our results also reveal additional recruitment of short-wavelength (SW) cones and/or OPN5. These findings suggest a primary role of rod photoreceptors in the light response of the retinal clock in mammals.

Identifying the content for an item bank and computerized adaptive testing system to measure the impact of age-related macular degeneration on health-related quality of life.

PURPOSE: We are developing an age-related macular degeneration (AMD) health-related quality of life (HRQoL) item bank, applicable to Western and Asian populations. We report primarily on content generation and refinement, but also compare the HRQoL issues reported in our study with Western studies and current AMD-HRQoL questionnaires. METHODS: In this cross-sectional, qualitative study of AMD patients attending the Singapore National Eye Centre (May-December 2019), items/domains were generated from: (1) AMD-specific questionnaires; (2) published articles; (3) focus groups/semi-structured interviews with AMD patients (n = 27); and (4) written feedback from retinal experts. Following thematic analysis, items were systematically refined to a minimally representative set and pre-tested using cognitive interviews with 16 AMD patients. RESULTS: Of the 27 patients (mean ± standard deviation age 67.9 ± 7.0; 59.2% male), 18 (66.7%), two (7.4%), and seven (25.9%) had no, early-intermediate, and late/advanced AMD (better eye), respectively. Whilst some HRQoL issues, e.g. activity limitation, mobility, lighting, and concerns were similarly reported by Western patients and covered by other questionnaires, others like anxiety about intravitreal injections, work tasks, and financial dependency were novel. Overall, 462 items within seven independent HRQoL domains were identified: Activity limitation, Lighting, Mobility, Emotional, Concerns, AMD management, and Work. Following item refinement, items were reduced to 219, with 31 items undergoing amendment. CONCLUSION: Our 7-domain, 219-item AMD-specific HRQoL instrument will undergo psychometric testing and calibration for computerized adaptive testing. The future instrument will enable users to precisely, rapidly, and comprehensively quantify the HRQoL impact of AMD and associated treatments, with item coverage relevant across several populations.

Retinal neural dysfunction in diabetes revealed with handheld chromatic pupillometry.

BACKGROUND: To evaluate the ability of handheld chromatic pupillometry to reveal and localise retinal neural dysfunction in diabetic patients with and without diabetic retinopathy (DR). METHODS: This cross-sectional study included 82 diabetics (DM) and 93 controls (60.4 ± 8.4 years, 44.1% males). DM patients included those without (n = 25, 64.7 ± 6.3 years, 44.0% males) and with DR (n = 57, 60.3 ± 8.5 years, 64.9% males). Changes in horizontal pupil radius in response to blue (469 nm) and red (640 nm) light stimuli were assessed monocularly, in clinics, using a custom-built handheld pupillometer. Pupillometric parameters (phasic constriction amplitudes [predominantly from the outer retina], maximal constriction amplitudes [from the inner and outer retina] and post-illumination pupillary responses [PIPRs; predominantly from the inner retina]) were extracted from baseline-adjusted pupillary light response traces and compared between controls, DM without DR, and DR. Net PIPR was defined as the difference between blue and red PIPRs. RESULTS: Phasic constriction amplitudes to blue and red lights were decreased in DR compared to controls (p < 0.001; p < 0.001). Maximal constriction amplitudes to blue and red lights were decreased in DR compared to DM without DR (p < 0.001; p = 0.02), and in DM without DR compared to controls (p < 0.001; p = 0.005). Net PIPR was decreased in both DR and DM without DR compared to controls (p = 0.02; p = 0.03), suggesting a wavelength-dependent (and hence retinal) pupillometric dysfunction in diabetic patients with or without DR. CONCLUSIONS: Handheld chromatic pupillometry can reveal retinal neural dysfunction in diabetes, even without DR. Patients with DM but no DR displayed primarily inner retinal dysfunction, while patients with DR showed both inner and outer retinal dysfunction.

Associations between age-related macular degeneration and sleep dysfunction: A systematic review.

BACKGROUND: Age-related macular degeneration, a prevalent degenerative retinal disease, is associated with non-visual and psychosocial impairments that may affect sleep. In this systematic review, we evaluated associations between age-related macular degeneration (AMD) and sleep, highlighted knowledge gaps and provided evidence-based recommendations to clinicians to enable holistic management of AMD patients. METHODS: We searched PubMed, Embase and the Cochrane Central registries for papers published before May 2022. Non-English, qualitative studies and grey literature were excluded. Studies evaluating the association between AMD and sleep (including sleep disorders like insomnia and sleep apnea), and vice versa, were included. The quality of shortlisted studies was evaluated using the Newcastle Ottawa Scale. RESULTS: Six (two case-control studies, three longitudinal cohort studies and one cross-sectional study) of 551 studies were included in this review. Four studies found that AMD was associated with increased rates of sleep apnea and poorer reported sleep quality, while five studies showed that patients with sleep apnea or insomnia were at higher risk of developing AMD. Associations between self-reported sleep quantity and AMD were conflicting. No study evaluated the relationship between AMD and sleep using objective sleep assessment tools. CONCLUSION: Only a limited number of studies investigated associations between AMD and sleep. These studies suggest a bidirectional relationship between AMD and sleep dysfunction yet disagree on the relationship between sleep quantity and the likelihood of AMD. Additional studies, using objective characterisation of sleep in patients with AMD are required to confirm these findings.

Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists.

OBJECTIVE: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance. METHODS: The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. RESULTS: The system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96-0.98), 0.96 (95% CI = 0.94-0.97), and 0.89 (95% CI = 0.87-0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67-0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68-0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61-0.70) between the system and Expert 2. INTERPRETATION: The performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro-ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785-795.

Artificial intelligence for detection of optic disc abnormalities.

PURPOSE OF REVIEW: The aim of this review is to highlight novel artificial intelligence-based methods for the detection of optic disc abnormalities, with particular focus on neurology and neuro-ophthalmology. RECENT FINDINGS: Methods for detection of optic disc abnormalities on retinal fundus images have evolved considerably over the last few years, from classical ophthalmoscopy to artificial intelligence-based identification methods being applied to retinal imaging with the aim of predicting sight and life-threatening complications of underlying brain or optic nerve conditions. SUMMARY: Artificial intelligence and in particular newly developed deep-learning systems are playing an increasingly important role for the detection and classification of acquired neuro-ophthalmic optic disc abnormalities on ocular fundus images. The implementation of automatic deep-learning methods for detection of abnormal optic discs, coupled with innovative hardware solutions for fundus imaging, could revolutionize the practice of neurologists and other non-ophthalmic healthcare providers.

Melatonin suppression is exquisitely sensitive to light and primarily driven by melanopsin in humans.

INTRODUCTION: Light elicits a range of non-visual responses in humans. Driven predominantly by intrinsically photosensitive retinal ganglion cells (ipRGCs), but also by rods and/or cones, these responses include melatonin suppression. A sigmoidal relationship has been established between melatonin suppression and light intensity; however, photoreceptoral involvement remains unclear. METHODS AND RESULTS: In this study, we first modelled the relationships between alpha-opic illuminances and melatonin suppression using an extensive dataset by Brainard and colleagues. Our results show that (a) melatonin suppression is better predicted by melanopic illuminance compared to other alpha-opic illuminances, (b) melatonin suppression is predicted to occur at levels as low as ~1.5 melanopic lux (melanopsin-weighted irradiance 0.2 µW/cm2 ), (c) saturation occurs at 305 melanopic lux (melanopsin-weighted irradiance 36.6 µW/cm2 ). We then tested this melanopsin-weighted illuminance-response model derived from Brainard and colleagues' data and show that it predicts equally well melatonin suppression data from our laboratory, although obtained using different intensities and exposure duration. DISCUSSION: Together, our findings suggest that melatonin suppression by monochromatic lights is predominantly driven by melanopsin and that it can be initiated at extremely low melanopic lux levels in experimental conditions. This emphasizes the concern of the non-visual impacts of low light intensities in lighting design and light-emitting devices.

Retinal Neuronal Loss in Visually Asymptomatic Patients With Myoclonic Epilepsy With Ragged-Red Fibers.

BACKGROUND: Myoclonic epilepsy with ragged-red fibers (MERRF, OMIM, #545000) is a rare neurological condition mostly caused by the m.8344A>G mitochondrial DNA pathogenic variant, which can variably affect multiple tissues, including the retina and optic nerve. We report detection of visually asymptomatic neuroretinal loss in 3 patients with genetically confirmed MERRF, using spectral domain optical coherence tomography (SD-OCT). METHODS: All patients underwent a complete ophthalmic examination including assessments of visual acuity, color vision, pupillary reactions, extraocular movements, applanation tonometry, slit-lamp, and dilated fundus examinations. Standard automated perimetry or Goldmann kinetic perimetry was performed, as well as fundus photographs and SD-OCT of the optic nerve head and macula. RESULTS: Despite the absence of visual symptoms in all patients, and normal visual acuity and visual fields in 1 patient, the 3 genetically confirmed patients (point mutations m.8344A>G; age range: 18-62 years) with MERRF-related neurological manifestations, displayed thinning of the retinal nerve fiber layer and variable alterations of the macular ganglion cell complex. CONCLUSIONS: Visually asymptomatic patients with genetically confirmed MERRF can display features of structural neuroretinal loss, quantifiable with SD-OCT. Further investigations are needed to establish whether OCT can assess early neurodegeneration in MERRF.

Pupillary Responses to Full-Field Chromatic Stimuli Are Reduced in Patients with Early-Stage Primary Open-Angle Glaucoma.

PURPOSE: To evaluate the ability of chromatic pupillometry to reveal abnormal pupillary responses to light in patients with early-stage primary open-angle glaucoma (POAG) and to test whether the degree of pupillometric impairment correlates with structural hallmarks of optic nerve damage in the disease. DESIGN: Cross-sectional study. PARTICIPANTS: Forty-six patients with early-stage POAG (63.4±8.3 years, 63% male, 87% ethnic-Chinese) and 90 age-matched healthy controls (61.4±8.6 years, 34% male, 89% ethnic-Chinese). Patients with POAG had a visual field mean deviation (VFMD) of -6 decibels or better on automated perimetry. METHODS: Each participant underwent a monocular 2-minute exposure to blue light (462 nm) followed by another 2-minute exposure to red light (638 nm) using a modified Ganzfeld dome equipped with a light-emitting diode lighting system. The light stimuli intensity was increased logarithmically to evaluate the combined extrinsic and intrinsic response of intrinsically photosensitive retinal ganglion cells (ipRGCs). Light-induced changes in horizontal pupil diameter were assessed monocularly using infrared pupillography. MAIN OUTCOME MEASURES: Baseline-adjusted, light-induced pupillary constriction amplitudes were calculated, and individual irradiance-response curves were constructed for each stimulus. Pupillary constriction amplitudes were compared between groups and across light intensities using a linear mixed model analysis. The linear relationship between pupillometric parameters and different structural and functional features of glaucoma was assessed using Pearson's correlation analysis. RESULTS: Light-induced pupillary constriction was reduced in patients with early-stage POAG compared with controls at moderate to high irradiances (≥11 Log photons/cm2/s) of blue (P = 0.003) and red (P < 0.001) light. Maximal pupillary constriction amplitude was correlated with retinal nerve fiber layer thickness (RNFL) thickness (blue: r = 0.51, P < 0.001; red: r = 0.45, P = 0.002) in patients with POAG but not in controls. Conversely, pupillometric parameters were not correlated with visual field scores in patients with early-stage POAG. CONCLUSIONS: Patients with early-stage POAG exhibit reduced pupillary responses to moderate and high irradiances of blue and red lights. This wavelength-independent functional alteration correlates with structural thinning of the RNFL and could be the consequence of dysfunction or loss of melanopsin expressing ipRGCs in the early stages of the disease.

Cerebral neural correlates of differential melanopic photic stimulation in humans.

Photic stimulation of rods, cones and intrinsically photosensitive melanopsin-containing retinal ganglion cells (ipRGCs) mediates non-visual light responses, including entrainment of circadian rhythms and pupillary light reflex. Unlike visual responses to photic stimulation, the cerebral correlates of non-visual light responses in humans remains elusive. In this study, we used functional magnetic resonance imaging (fMRI) in 14 healthy young participants, to localize cerebral regions which are differentially activated by metameric light that gave rise to different levels of melanopic excitation. Mean blood oxygen-level dependent (BOLD) responses disclosed bilateral activation of the frontal eye fields during exposure to light geared towards melanopsin. Furthermore, multivariate pattern analyses showed distinct bilateral pattern activity in the inferior temporal gyri and the caudate nuclei. Taken together, our findings suggest that melanopsin-based photoreception activates a cerebral network including frontal regions, classically involved in attention and ocular motor responses.

Differences in the pupillary responses to evening light between children and adolescents.

BACKGROUND: In the mammalian retina, intrinsically-photosensitive retinal ganglion cells (ipRGC) detect light and integrate signals from rods and cones to drive multiple non-visual functions including circadian entrainment and the pupillary light response (PLR). Non-visual photoreception and consequently non-visual sensitivity to light may change across child development. The PLR represents a quick and reliable method for examining non-visual responses to light in children. The purpose of this study was to assess differences in the PLRs to blue and red stimuli, measured one hour prior to bedtime, between children and adolescents. METHODS: Forty healthy participants (8-9 years, n = 21; 15-16 years, n = 19) completed a PLR assessment 1 h before their habitual bedtime. After a 1 h dim-light adaptation period (< 1 lx), baseline pupil diameter was measured in darkness for 30 s, followed by a 10 s exposure to 3.0 × 1013 photons/cm2/s of either red (627 nm) or blue (459 nm) light, and a 40 s recovery in darkness to assess pupillary re-dilation. Subsequently, participants underwent 7 min of dim-light re-adaptation followed by an exposure to the other light condition. Lights were counterbalanced across participants. RESULTS: Across both age groups, maximum pupil constriction was significantly greater (p < 0.001, ηp2 = 0.48) and more sustained (p < 0.001, ηp2 = 0.41) during exposure to blue compared to red light. For adolescents, the post-illumination pupillary response (PIPR), a hallmark of melanopsin function, was larger after blue compared with red light (p = 0.02, d = 0.60). This difference was not observed in children. Across light exposures, children had larger phasic (p < 0.01, ηp2 = 0.20) and maximal (p < 0.01, ηp2 = 0.22) pupil constrictions compared to adolescents. CONCLUSIONS: Blue light elicited a greater and more sustained pupillary response than red light in children and adolescents. However, the overall amplitude of the rod/cone-driven phasic response was greater in children than in adolescents. Our findings using the PLR highlight a higher sensitivity to evening light in children compared to adolescents, and continued maturation of the human non-visual photoreception/system throughout development.

The influence of the environment and lifestyle on myopia.

BACKGROUND: Myopia, commonly known as near-sightedness, has emerged as a global epidemic, impacting almost one in three individuals across the world. The increasing prevalence of myopia during early childhood has heightened the risk of developing high myopia and related sight-threatening eye conditions in adulthood. This surge in myopia rates, occurring within a relatively stable genetic framework, underscores the profound influence of environmental and lifestyle factors on this condition. In this comprehensive narrative review, we shed light on both established and potential environmental and lifestyle contributors that affect the development and progression of myopia. MAIN BODY: Epidemiological and interventional research has consistently revealed a compelling connection between increased outdoor time and a decreased risk of myopia in children. This protective effect may primarily be attributed to exposure to the characteristics of natural light (i.e., sunlight) and the release of retinal dopamine. Conversely, irrespective of outdoor time, excessive engagement in near work can further worsen the onset of myopia. While the exact mechanisms behind this exacerbation are not fully comprehended, it appears to involve shifts in relative peripheral refraction, the overstimulation of accommodation, or a complex interplay of these factors, leading to issues like retinal image defocus, blur, and chromatic aberration. Other potential factors like the spatial frequency of the visual environment, circadian rhythm, sleep, nutrition, smoking, socio-economic status, and education have debatable independent influences on myopia development. CONCLUSION: The environment exerts a significant influence on the development and progression of myopia. Improving the modifiable key environmental predictors like time spent outdoors and engagement in near work can prevent or slow the progression of myopia. The intricate connections between lifestyle and environmental factors often obscure research findings, making it challenging to disentangle their individual effects. This complexity underscores the necessity for prospective studies that employ objective assessments, such as quantifying light exposure and near work, among others. These studies are crucial for gaining a more comprehensive understanding of how various environmental factors can be modified to prevent or slow the progression of myopia.

Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department.

PURPOSE: The Fundus photography vs Ophthalmoscopy Trial Outcomes in the Emergency Department (FOTO-ED) studies showed that ED providers poorly recognized funduscopic findings in patients in the ED. We tested a modified version of the Brain and Optic Nerve Study Artificial Intelligence (BONSAI) deep learning system on nonmydriatic fundus photographs from the FOTO-ED studies to determine if the deep learning system could have improved the detection of papilledema had it been available to ED providers as a real-time diagnostic aid. DESIGN: Retrospective secondary analysis of a cohort of patients included in the FOTO-ED studies. METHODS: The testing data set included 1608 photographs obtained from 828 patients in the FOTO-ED studies. Photographs were reclassified according to the optic disc classification system used by the deep learning system ("normal optic discs," "papilledema," and "other optic disc abnormalities"). The system's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 1-vs-rest strategy, with reference to expert neuro-ophthalmologists. RESULTS: The BONSAI deep learning system successfully distinguished normal from abnormal optic discs (AUC 0.92 [95% confidence interval {CI} 0.90-0.93]; sensitivity 75.6% [73.7%-77.5%] and specificity 89.6% [86.3%-92.8%]), and papilledema from normal and others (AUC 0.97 [0.95-0.99]; sensitivity 84.0% [75.0%-92.6%] and specificity 98.9% [98.5%-99.4%]). Six patients with missed papilledema in 1 eye were correctly identified by the deep learning system as having papilledema in the other eye. CONCLUSIONS: The BONSAI deep learning system was able to reliably identify papilledema and normal optic discs on nonmydriatic photographs obtained in the FOTO-ED studies. Our deep learning system has excellent potential as a diagnostic aid in EDs and non-ophthalmology clinics equipped with nonmydriatic fundus cameras. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

The BONSAI (Brain and Optic Nerve Study with Artificial Intelligence) deep learning system can accurately identify pediatric papilledema on standard ocular fundus photographs.

BACKGROUND: Pediatric papilledema often reflects an underlying severe neurologic disorder and may be difficult to appreciate, especially in young children. Ocular fundus photographs are easy to obtain even in young children and in nonophthalmology settings. The aim of our study was to ascertain whether an improved deep-learning system (DLS), previously validated in adults, can accurately identify papilledema and other optic disk abnormalities in children. METHODS: The DLS was tested on mydriatic fundus photographs obtained in a multiethnic pediatric population (<17 years) from three centers (Atlanta-USA; Bucharest-Romania; Singapore). The DLS's multiclass classification accuracy (ie, normal optic disk, papilledema, disks with other abnormality) was calculated, and the DLS's performance to specifically detect papilledema and normal disks was evaluated in a one-vs-rest strategy using the AUC, sensitivity and specificity, with reference to expert neuro-ophthalmologists. RESULTS: External testing was performed on 898 fundus photographs: 447 patients; mean age, 10.33 (231 patients ≤10 years of age; 216, 11-16 years); 558 normal disks, 254 papilledema, 86 other disk abnormalities. Overall multiclass accuracy of the DLS was 89.6% (range, 87.8%-91.6%). The DLS successfully distinguished "normal" from "abnormal" optic disks (AUC 0.99 [0.98-0.99]; sensitivity, 87.3% [84.9%-89.8%]; specificity, 98.5% [97.6%-99.6%]), and "papilledema" from "normal and other" (AUC 0.99 [0.98-1.0]; sensitivity, 98.0% [96.8%-99.4%]; specificity, 94.1% (92.4%-95.9%)]. CONCLUSIONS: Our DLS reliably distinguished papilledema from normal optic disks and other disk abnormalities in children, suggesting it could be utilized as a diagnostic aid for the assessment of optic nerve head appearance in the pediatric age group.

Differences in the Pupillary Responses to Evening Light between Children and Adolescents.

Purpose: To assess differences in the pupillary light responses (PLRs) to blue and red evening lights between children and adolescents. Methods: Forty healthy participants (8-9 years, n=21; 15-16 years, n=19) completed a PLR assessment 1 h before their habitual bedtime. After a 1 h dim-light adaptation period (<1 lux), baseline pupil diameter was measured in darkness for 30 s, followed by a 10 s exposure to 3.0×1013 photons/cm2/s of either red (627 nm) or blue (459 nm) light, and a 40 s recovery in darkness to assess pupillary re-dilation. Subsequently, participants underwent 7 min of dim-light re-adaptation followed by an exposure to the other light condition. Lights were counterbalanced across participants. Results: Across both age groups, maximum pupil constriction was significantly greater (p< 0.001, ηp2=0.48) and more sustained (p< 0.001, ηp2=0.41) during exposure to blue compared to red light. For adolescents, the post-illumination pupillary response (PIPR), a hallmark of melanopsin function, was larger after blue compared with red light (p= 0.02, d=0.60). This difference was not observed in children. Across light exposures, children had larger phasic (p< 0.01, ηp2=0.20) and maximal (p< 0.01, ηp2=0.22) pupil constrictions compared to adolescents. Conclusions: Blue light elicited a greater and more sustained pupillary response than red light across participants. However, the overall amplitude of the rod/cone-driven phasic response was greater in children than in adolescents. Our findings using the PLR highlight a higher sensitivity to evening light in children compared to adolescents, and continued maturation of the human non-visual photoreception/system throughout development.

ENLIGHT: A consensus checklist for reporting laboratory-based studies on the non-visual effects of light in humans.

BACKGROUND: There is no consensus on reporting light characteristics in studies investigating non-visual responses to light. This project aimed to develop a reporting checklist for laboratory-based investigations on the impact of light on non-visual physiology. METHODS: A four-step modified Delphi process (three questionnaire-based feedback rounds and one face-to-face group discussion) involving international experts was conducted to reach consensus on the items to be included in the checklist. Following the consensus process, the resulting checklist was tested in a pilot phase with independent experts. FINDINGS: An initial list of 61 items related to reporting light-based interventions was condensed to a final checklist containing 25 items, based upon consensus among experts (final n = 60). Nine items were deemed necessary to report regardless of research question or context. A description of each item is provided in the accompanying Explanation and Elaboration (E&E) document. The independent pilot testing phase led to minor textual clarifications in the checklist and E&E document. INTERPRETATION: The ENLIGHT Checklist is the first consensus-based checklist for documenting and reporting ocular light-based interventions for human studies. The implementation of the checklist will enhance the impact of light-based research by ensuring comprehensive documentation, enhancing reproducibility, and enabling data aggregation across studies. FUNDING: Network of European Institutes for Advanced Study (NETIAS) Constructive Advanced Thinking (CAT) programme; Sir Henry Wellcome Postdoctoral Fellowship (Wellcome Trust, 204686/Z/16/Z); Netherlands Organisation for Health Research and Development VENI fellowship (2020-09150161910128); U.S. Department of Defense Grant (W81XWH-16-1-0223); National University of Singapore (NUHSRO/2022/038/Startup/08); and National Research Foundation Singapore (NRF2022-THE004-0002).