Evaluation of Nuclear Cataract with Smartphone-Attachable Slit-Lamp Device

Assessing the Impact of AI-Assisted Portable Slit Lamps on Rural Primary Ophthalmic Medical Service

PURPOSE

To investigate the effect of an AI-assisted portable slit lamp (iSpector) and basic ophthalmology training on cataract detection, referral, and surgery rate in rural areas.

METHODS

This randomized control trial randomly assigned 63 village doctors to either the AI-assisted group (providing iSpector and training) or the control group (providing training). Doctors were followed for 1 year before intervention as a baseline and 1 year after to make the comparison. Multivariable Poisson regression was applied to compare the difference in cataract detection, referral, and surgery rate between the two groups, adjusted for primary doctors' baseline characteristics. We further conducted subgroup analysis to estimate the change after the intervention.

RESULTS

Compared to the control group, the detection, referral, and surgery rate of cataracts among the AI-assisted group was comparable, 1.7 times higher, and 4.9 times higher, respectively. Providing iSpector and training increased the detection, referral, and surgery rate of cataracts. However, only based on training to elevate the detection rate of cataracts did not change the referral and surgery rate.

CONCLUSIONS

iSpector helps village doctors detect and refer cataract patients appropriately, thus increasing the probability that patients receive cataract surgery.

Reliability and Accuracy of Smart Eye Camera in Determining Grading of Nuclear Cataract

PURPOSE

Cataracts are the cause of more than 50% of blindness in the world. Cataract is diagnosed using a slit lamp to evaluate the opacity of the lens. Slit lamps as the primary tools cataract are not available in primary care units. Smart Eye Camera (SEC) is a medical device placed on smartphone, capable of creating slit beam similar to conventional slit lamp. Previous studies have been conducted in Japan. We wanted to further compare ocular examination between SEC and conventional slit lamp with samples taken from UPTD Eye Clinic Ambon Vlissingen in Indonesia.

METHODS

Observational analytical study with a cross-sectional study design. The research sample consisted of 67 people selected by consecutive sampling. All samples were examined for cataracts using a conventional slit lamp and SEC. To evaluate the accuracy of SEC, diagnosis of cataract was carried out by one observer based on slit-lamp images. The results of examinations were then compared with the examinations by two observers based on the SEC video, where the videos were read two times in a span of 1 week.

RESULTS

Sixty-seven eye samples were taken with average age of 61.76 ± 5.83 years, visual acuity was 0.07 ± 0.12, intraocular pressure was 14.12 ± 2.06 mmHg, and SEC video duration was 4.15 ± 1.09 seconds. SEC intraobserver reliability has a κ of 0.795/0.818, SEC interobserver reliability has a κ of 0.795/0.817. SEC assessment accuracy for observer 1 has a κ of 0.606/0.681 and for observer 2 has a κ of 0.629/0.717, with the highest accuracy for cataract grade 3 (86.67% and 91.18%, respectively).

CONCLUSIONS

This study shows SEC has good consistency and reliability in assessing the degree of nuclear cataract compared to conventional slit lamp. This tool could be used for cataract screening in remote areas.

Validation and optimization of smart eye camera as teleophthalmology device for the reduction of preventable and treatable blindness in Nigeria

BACKGROUND/OBJECTIVES

Limited resources and staffing hinders efforts to reduce preventable blindness, especially in low- to middle-income countries. The slit-lamp examination (SLE), which is essential for ophthalmology practices, is often unavailable in primary and secondary eye care facilities due to the high costs and lengthy training required for operation. We conducted a cross-sectional, multicentre study exploring the potential for a smart eye camera (SEC; a tele-ophthalmology handheld device developed by OUI Inc., Japan) to address the limitations of the SLE.

SUBJECT/METHODS

Ocular diagnoses, visual acuity assessments and examinations of the eyes were performed independently using both a conventional SLE and a SEC. Four independent assessors (blind to the study) reviewed the images captured by the SEC and the SLE as administered by separate investigators. All analyses were performed using R version 4.2.2 for macOS at a 5% level of statistical significance.

RESULTS

The results of the image quality analysis demonstrated that the number of higher-quality images was significantly higher (p < 0.05) for the images captured using the SEC device compared to the SLE machine. Remarkably, up to 96% accuracy of diagnosis was recorded with SEC. Evaluation of diagnostic accuracy rates derived from images obtained from both machines revealed a degree of divergence in assessments among evaluators, yielding a Fleiss's Kappa value of 0.092. The sensitivity analysis for the SEC device revealed a reasonably strong capacity to correctly identify true positive cases, with an average sensitivity score of 90%.

CONCLUSION

The results of this study indicate that SEC can effectively evaluate anterior segment lesions in ophthalmology.

Artificial intelligence support improves diagnosis accuracy in anterior segment eye diseases

CorneAI, a deep learning model designed for diagnosing cataracts and corneal diseases, was assessed for its impact on ophthalmologists' diagnostic accuracy. In the study, 40 ophthalmologists (20 specialists and 20 residents) classified 100 images, including iPhone 13 Pro photos (50 images) and diffuser slit-lamp photos (50 images), into nine categories (normal condition, infectious keratitis, immunological keratitis, corneal scar, corneal deposit, bullous keratopathy, ocular surface tumor, cataract/intraocular lens opacity, and primary angle-closure glaucoma). The iPhone and slit-lamp images represented the same cases. After initially answering without CorneAI, the same ophthalmologists responded to the same cases with CorneAI 2-4 weeks later. With CorneAI's support, the overall accuracy of ophthalmologists increased significantly from 79.2 to 88.8% (P < 0.001). Specialists' accuracy rose from 82.8 to 90.0%, and residents' from 75.6 to 86.2% (P < 0.001). Smartphone image accuracy improved from 78.7 to 85.5% and slit-lamp image accuracy from 81.2 to 90.6% (both, P < 0.001). In this study, CorneAI's own accuracy was 86%, but its support enhanced ophthalmologists' accuracy beyond the CorneAI's baseline. This study demonstrated that CorneAI, despite being trained on diffuser slit-lamp images, effectively improved diagnostic accuracy, even with smartphone images.

Clinical evaluation of corneal ulcer with a portable and smartphone-attachable slit lamp device: Smart Eye Camera

Corneal ulcer is one of the most important ophthalmic emergencies. A portable, recordable, and smartphone-attachable slit-lamp device called the "Smart Eye Camera" (SEC) is introduced to compare evaluating corneal ulcers between the SEC and the conventional slit-lamp. A total of 110 participants were included in the study, consisting of 55 patients with corneal ulcers and 55 age- and gender-matched healthy volunteers as controls. The participants were first subjected to examination by a conventional slit lamp. The video recording with SEC and imaging with a slit lamp were done by a non-medical person. Both SEC videos and slit-lamp photos were reported by two independent ophthalmologists and compared with a slit-lamp examination as a gold standard. The average age of the study participants was 48.85 ± 20.45 years and 68 participants (61.8%) were male. All corneal ulcers were detected by two ophthalmologists using SEC. Ulcer size evaluated in slit lamp and SEC horizontally (Intraclass Correlation Coefficient (ICC); 0.90, 95% CI; 0.84-0.94) and vertically (ICC; 0.90, 95% CI; 0.84-0.94) is correlated about 90%. Hypopyon size (ICC; 0.95, 95% CI; 0.92-0.97), and corneal epithelial defect (CED) size horizontally (ICC; 0.94, 95% CI; 0.91-0.96) and vertically (ICC; 0.94, 95% CI; 0.91-0.96) also correlated about 94%. The infiltration pattern evaluated by SEC was consistent with the pattern evaluated in slit lamps in more than 80% of patients. The SEC is so accurate in diagnosing corneal ulcers and its reliability makes it a valuable telemedicine device.

Diagnostic Assessment of Nuclear Cataracts Using a Smartphone-Attachable Slit-Lamp Device: A Cross-Sectional Study in Vietnam

Purpose The purpose of this study was to compare the accuracy and agreement in nuclear cataract opacity classification between the Smart Eye Camera (SEC) device, and the slit-lamp. Methods A cross-sectional study was conducted with a convenient sampling of 221 eyes from 139 patients with phakic eyes, visiting IVISION Eye Center, Ho Chi Minh City, Vietnam, from November 1, 2023, to April 30, 2024. Two grading systems, Lucio-Buratto (Buratto) and World Health Organization (WHO), were used to compare the effectiveness of the two devices, with statistical analysis using Spearman's correlation coefficient and Cohen's kappa to evaluate the agreement level. Results Results showed no significant difference in Buratto and WHO grading between the two devices (p > 0.05), indicating consistency between the two measurement methods. Spearman's correlation coefficient demonstrated a strong correlation between the results from both devices, with r = 0.797 for Buratto and r = 0.579 for WHO (p < 0.001). The reliability was confirmed by high weighted-kappa values (k = 0.774 for Buratto and k = 0.539 for WHO). Conclusion The SEC's comparable effectiveness to the slit-lamp supports its potential utility in blindness-prevention screening efforts.

Primary Angle Closure Observed During a House Visit: A Case Treated With Laser Iridotomy

Laser iridotomy (LI) is an effective treatment for patients with pupillary block mechanisms. Here, we report a case of LI performed on a patient with primary angle closure (PAC) and elevated intraocular pressure (IOP), who was unsuitable for other treatments due to specific social circumstances. The patient, a 97-year-old female residing in a private nursing home, had a medical history notable only for mild dementia and was wheelchair-bound. She had not undergone ophthalmologic evaluation for over 50 years. The patient presented with intermittent tenderness and redness in the left eye. Therefore, an ophthalmologist visited the nursing home. Examination revealed visual acuity of 20/200 in the right eye and 20/100 in the left eye, IOP of 24 mmHg in the right eye and 26 mmHg in the left eye, no conjunctival hyperemia, shallow anterior chambers, and nuclear sclerosis grade 4 cataracts in both eyes. Fundus examination was challenging due to lens opacity, and both optic nerve papillae appeared pale. Given her history of episodic eye pain and hyperemia, PAC was diagnosed. Treatment options, including eye drops and cataract surgery, were discussed. However, the patient opted for LI due to her advanced age and inability to attend frequent follow-up visits. LI was successfully performed on both eyes during her visit to the clinic. One week post-procedure, IOP decreased to 12 mmHg bilaterally, with no complications. This case demonstrates that LI can be a viable option for managing PAC and high IOP in patients who are not candidates for surgery or eye drops due to social constraints.

A Case of Traumatic Hyphema Diagnoses by Telemedicine Between a Remote Island and the Mainland of Tokyo

Chichijima Island, part of the Ogasawara Islands in Tokyo, is a remote island with a population of approximately 2,000, served by a few resident general practitioners (GPs). This case report discusses the application of teleophthalmology in managing pediatric ocular trauma on this remote island. A pediatric patient sustained an ocular injury from a badminton shuttlecock and was initially examined by a resident GP using a recordable slit-lamp microscope. The ocular images were transmitted to a mainland ophthalmologist through a telemedicine system. The specialist provided remote consultation and recommended further examination and treatment, leading to the patient's transfer to the mainland. The successful management of this case underscores the critical role of telemedicine in enhancing healthcare delivery in isolated regions. With advancements in medical technology, teleophthalmology is expected to become increasingly vital in providing specialized care in remote and underserved areas. The case highlights the importance of telemedicine in improving access to specialized medical expertise, ensuring timely and effective patient care, and potentially reducing the need for patient transfers to more equipped healthcare facilities.

Evaluation of a new portable corneal topography system for self-measurement using smartphones: a pilot study

PURPOSE

Herein, we propose the use of the "KeraVio Ring", which is a portable, selfie-based, smartphone-attached corneal topography system that is based on the Placido ring videokeratoscope. The goal of this study was to evaluate and compare corneal parameters between KeraVio Ring and conventional corneal tomography images.

METHODS

We designed the KeraVio Ring as a device comprising 3D-printed LED rings for generating Placido rings that can be attached to a smartphone. Two LED rings are attached to a cone-shaped device, and both corneas are illuminated. Selfies were taken using the KeraVio Ring attached to the smartphone without assistance from any of the examiners. Captured Placido rings on the cornea were analysed by intelligent software to calculate corneal parameters. Patients with normal, keratoconus, or LASIK-treated eyes were included. Anterior segment optical coherence tomography (AS-OCT) was also performed for each subject.

RESULTS

We found highly significant correlations between the steepest and flattest keratometry, corneal astigmatism, and vector components obtained with the KeraVio Ring and AS-OCT. In subjects with normal, keratoconus, and LASIK-treated eyes, the mean difference in corneal astigmatism between the two devices was -0.8 ± 1.4 diopters (D) (95% limits of agreement (LoA), -3.6 to 2.0), -1.8 ± 3.7 D (95% LoA, -9.1 to 5.5), and -1.5 ± 1.3 D (95% LoA, -4.0 to 1.1), respectively.

CONCLUSIONS

The experimental results showed that the corneal parameters obtained by the KeraVio Ring were correlated with those obtained with AS-OCT. The KeraVio Ring has the potential to address an unmet need by providing a tool for portable selfie-based corneal topography.

The Slitscope

Slit lamp biomicroscope is the right hand of an Ophthalmologist. Even though precise, its bulky design and complex working process are limiting constraints, making it difficult for screening at outreach camps, which are an integral part of this field for the purpose of eliminating needless blindness. The torchlight is the main tool used for screening. Recently, the integration of smartphones with instruments and the digitization of slit lamp has been explored, to provide simple and easy hacks. By bringing the slit of the slit lamp to traditional torchlight, we have created "The Slitscope". It combines the best of both worlds as a simple innovative do-it-yourself novel technique for precise cataract screening. It is especially useful in peripheral centers, vision centers, and outreach camps. We present two prototypes which can also be 3D printed.

Medical Students' Perspectives Regarding the Use of a Slit-Lamp Smartphone Adapter for Clinical Slit-Lamp Photography

Background This study aimed to investigate medical students' perspectives regarding the ease and utility of smartphone slit-lamp photography. Methodology In this prospective experimental study, fourth and fifth-year medical students who were in or had finished ophthalmology rotation were included to attempt slit-lamp smartphone anterior segment photography on adult patients after a brief hands-on instruction course. Each medical student attempted to record five supervised slit-lamp videos of the anterior segment of five patients using the described adapter and their own smartphone. The time taken until photography was calculated for each attempt. After the fifth attempt, each medical student rated the ease of the use of this method of slit-lamp photography as well as their perspective regarding its utility as a potential means of medical education and telemedical consultations on a five-point Likert scale. Results A total of 33 medical students participated, with each successfully recording five slit-lamp examinations using their smartphones. The time used for the application of the adapter until the image capture ranged from 6 to 278 seconds (average = 39.51 ± 34.7 seconds) and markedly improved by the fifth attempt (30.5 ± 25.7 seconds) compared to the first attempt (67.3 ± 49.3 seconds). Learning this skill was perceived to be relatively easy (2.2 ± 1), with high potential in clinical education (4.6 ± 0.75) and teleconsultations (4.7 ± 0.65). Conclusions Smartphone slit-lamp photography is a relatively easy process. It can be quickly acquired by medical students and has the potential to enhance their medical education and telemedical consultation capabilities.

Development of the AI Pipeline for Corneal Opacity Detection

Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud storage for more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targets corneal opacity, which is a global cause of blindness. This study has two purposes. The first is to detect corneal opacity from videos in which the anterior segment of the eye is captured. The other is to develop an AI pipeline to detect corneal opacities. First, we extracted image frames from videos and processed them using a convolutional neural network (CNN) model. Second, we manually annotated the images to extract only the corneal margins, adjusted the contrast with CLAHE, and processed them using the CNN model. Finally, we performed semantic segmentation of the cornea using annotated data. The results showed an accuracy of 0.8 for image frames and 0.96 for corneal margins. Dice and IoU achieved a score of 0.94 for semantic segmentation of the corneal margins. Although corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. The incorporation of manual annotation into the AI pipeline, through semantic segmentation, facilitated high accuracy in detecting corneal opacity.

Mobile infrared slit-light scanner for rapid eye disease screening

Purpose

Timely detection and treatment of visual impairments and age-related eye diseases are essential for maintaining a longer, healthier life. However, the shortage of appropriate medical equipment often impedes early detection. We have developed a portable self-imaging slit-light device utilizing NIR light and a scanning mirror. The objective of our study is to assess the accuracy and compare the performance of our device with conventional nonportable slit-lamp microscopes and anterior segment optical coherence tomography (AS-OCT) for screening and remotely diagnosing eye diseases, such as cataracts and glaucoma, outside of an eye clinic.

Approach

The NIR light provides an advantage as measurements are nonmydriatic and less traumatic for patients. A cross-sectional study involving Japanese adults was conducted. Cataract evaluation was performed using photographs captured by the device. Van-Herick grading was assessed by the ratio of peripheral anterior chamber depth to peripheral corneal thickness, in addition to the iridocorneal angle using Image J software.

Results

The correlation coefficient between values obtained by AS-OCT, and our fabricated portable scanning slit-light device was notably high. The results indicate that our portable device is equally reliable as the conventional nonportable slit-lamp microscope and AS-OCT for screening and evaluating eye diseases.

Conclusions

Our fabricated device matches the functionality of the traditional slit lamp, offering a cost-effective and portable solution. Ideal for remote locations, healthcare facilities, or areas affected by disasters, our scanning slit-light device can provide easy access to initial eye examinations and supports digital eye healthcare initiatives.

Feasibility of Tear Meniscus Height Measurements Obtained with a Smartphone-Attachable Portable Device and Agreement of the Results with Standard Slit Lamp Examination

PURPOSE

We aimed to evaluate the feasibility of using a novel device, the Smart Eye Camera (SEC), for assessing tear meniscus height (TMH) after fluorescein staining and the agreement of the results with measurements obtained using standard slit lamp examination.

METHODS

TMH was assessed using both SEC and conventional slit lamp examination. The images were analyzed using the software ImageJ 1.53t (National Institutes of Health, Bethesda, MD, USA). A common measurement unit scale was established based on a paper strip, which was used as a calibration marker to convert pixels into metric scale. A color threshold was applied using uniform parameters for brightness, saturation, and hue. The images were then binarized to black and white to enhance the representation of the tear menisci. A 2 mm area around the upper and lower meniscus in the central eye lid zone was selected and magnified 3200 times to facilitate manual measurement. The values obtained using SEC were compared with those obtained with a slit lamp.

RESULTS

The upper and lower TMH values measured using the SEC were not statistically different from those obtained with a slit lamp (0.209 ± 0.073 mm vs. 0.235 ± 0.085, p = 0.073, and 0.297 ± 0.168 vs. 0.260 ± 0.173, p = 0.275, respectively). The results of Bland-Altman analysis demonstrated strong agreement between the two instruments, with a mean bias of -0.016 mm (agreement limits: -0.117 to 0.145 mm) for upper TMH and 0.031 mm (agreement limits: -0.306 to 0.368 mm) for lower TMH.

CONCLUSIONS

The SEC demonstrated sufficient validity and reliability for assessing TMH in healthy eyes in a clinical setting, demonstrating concordance with the conventional slit lamp examination.

Clinical Features of Sjögren Syndrome-Related Dry Eye Disease in Anterior Segment Photographs

PURPOSE

Dry eye disease (DED) is a major complication of autoimmune disorders, including Sjögren syndrome (SS), ocular graft-versus-host disease, and other rheumatic diseases. DED often affects patients' quality of life, necessitating early detection and treatment. However, no simple screening method for DED has yet been established in ophthalmologic practice. This retrospective study aimed to identify the characteristic features of SS-related DED from anterior segment images.

METHODS

Five hundred two cases (SS, 68 cases; ocular graft-versus-host disease, 50 cases; other conditions, 27 cases; simple DED, 72 cases; and no DED, 97 cases) were enrolled.

RESULTS

The inferior corneal fluorescein staining score (CFS_I) was significantly higher in the SS group ( P < 0.001). Moreover, the nasal lissamine green staining score (LG_N) was high in the SS group ( P < 0.001). The sensitivity, specificity, and area under the curve of the receiver operating characteristic curve were calculated for the CFS_I plus LG_N in relation to the SS-positive and SS-negative statuses; the sensitivity and specificity were 80.6% and 91.1%, respectively, with an area under the curve of 0.926.

CONCLUSIONS

A positive CFS_I combined with a positive LG_N correlates with a high risk for SS. A positive CFS_I and a positive LG_N are important signs for an immune-related DED, especially SS, and may be useful in the early detection of SS-related DED.

AI-based diagnosis of nuclear cataract from slit-lamp videos

In ophthalmology, the availability of many fundus photographs and optical coherence tomography images has spurred consideration of using artificial intelligence (AI) for diagnosing retinal and optic nerve disorders. However, AI application for diagnosing anterior segment eye conditions remains unfeasible due to limited standardized images and analysis models. We addressed this limitation by augmenting the quantity of standardized optical images using a video-recordable slit-lamp device. We then investigated whether our proposed machine learning (ML) AI algorithm could accurately diagnose cataracts from videos recorded with this device. We collected 206,574 cataract frames from 1812 cataract eye videos. Ophthalmologists graded the nuclear cataracts (NUCs) using the cataract grading scale of the World Health Organization. These gradings were used to train and validate an ML algorithm. A validation dataset was used to compare the NUC diagnosis and grading of AI and ophthalmologists. The results of individual cataract gradings were: NUC 0: area under the curve (AUC) = 0.967; NUC 1: AUC = 0.928; NUC 2: AUC = 0.923; and NUC 3: AUC = 0.949. Our ML-based cataract diagnostic model achieved performance comparable to a conventional device, presenting a promising and accurate auto diagnostic AI tool.

LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality

Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p < 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.

A Review of Smartphone Adapters Capable of Imaging the Anterior Segment in Ophthalmology

Teleophthalmology is a widely recognised way to provide health care to patients living in rural and remote regions by leveraging limited clinician availability and resources. This is most important in low socioeconomic areas, where the disparity between prevalence of preventable blindness and practicing ophthalmologists is greatest. The ubiquity and accessibility of smartphones allow them to be utilised in a clinical setting and facilitate teleophthalmology. While the current market of smartphone adapters capable of imaging ocular pathology is expanding, few focus on the anterior segment and operate independently of the slit-lamp microscope. This article reviews the available smartphone adapters capable of imaging anterior segment pathology.

A study establishing sensitivity and accuracy of smartphone photography in ophthalmologic community outreach programs: Review of a smart eye camera

Purpose

Diseases affecting the cornea are a major cause of corneal blindness globally. The pressing issue we are facing today is the lack of diagnostic devices in rural areas to diagnose these conditions. The aim of the study is to establish sensitivity and accuracy of smartphone photography using a smart eye camera (SEC) in ophthalmologic community outreach programs.

Methods

In this pilot study, a prospective non-randomized comparative analysis of inter-observer variability of anterior segment imaging recorded using an SEC was performed. Consecutive 100 patients with corneal pathologies, who visited the cornea specialty outpatient clinic, were enrolled. They were examined with a conventional non-portable slit lamp by a cornea consultant, and the diagnoses were recorded. This was compared with the diagnoses made by two other consultants based on SEC videos of the anterior segment of the same 100 patients. The accuracy of SEC was accessed using sensitivity, specificity, PPV, and NPV. Kappa statistics was used to find the agreement between two consultants by using STATA 17.0 (Texas, USA).

Results

There was agreement between the two consultants to diagnosing by using SEC. Above 90% agreements were found in all the diagnoses, which were statistically significant (P-value < 0.001). More than 90% sensitivity and a negative predictive value were found.

Conclusion

SEC can be used successfully in the community outreach programs like field visits, eye camps, teleophthalmology, and community centers, where either a clinical setup is lacking or ophthalmologists are not available.

Ocular conditions and injuries, detection and management in spaceflight

Ocular trauma or other ocular conditions can be significantly debilitating in space. A literature review of over 100 articles and NASA evidence books, queried for eye related trauma, conditions, and exposures was conducted. Ocular trauma and conditions during NASA space missions during the Space Shuttle Program and ISS through Expedition 13 in 2006 were reviewed. There were 70 corneal abrasions, 4 dry eyes, 4 eye debris, 5 complaints of ocular irritation, 6 chemical burns, and 5 ocular infections noted. Unique exposures on spaceflight, such as foreign bodies, including celestial dust, which may infiltrate the habitat and contact the ocular surface, as well as chemical and thermal injuries due to prolonged CO2 and heat exposure were reported. Diagnostic modalities used to evaluate the above conditions in space flight include vision questionnaires, visual acuity and Amsler grid testing, fundoscopy, orbital ultrasound, and ocular coherence tomography. Several types of ocular injuries and conditions, mostly affecting the anterior segment, are reported. Further research is necessary to understand the greatest ocular risks that astronauts face and how better we can prevent, but also diagnose and treat these conditions in space.

Clinical observation of tear film breakup time with a novel smartphone-attachable technology

PURPOSE

To demonstrate the practicability of a portable instrument in assessing tear film breakup time (TFBUT): a lens attachment for smartphones (LAS).

METHODS

By applying LAS in combination with the iPhone 12 pro, and a recordable slit-lamp microscope, we obtained TFBUT videos from 58 volunteers. The comparison between the conventional slit-lamp microscope and LAS by an experienced ophthalmologist. Moreover, we also invited an ophthalmic postgraduate student and an undergraduate student with no clinical experience to assess TFBUT in those videos. The inter-observer reliability was assessed using intraclass correlation coefficients (ICC).

RESULTS

The TFBUT of 116 eyes in total was recorded. Reliability indexes were adequate. The Spearman's correlation analysis and the intraclass correlation coefficient suggested a strong correlation between the 2 modalities (Right eye: Spearman's r = 0.929, 95% confidence interval (CI) = 0.847-0.963, ICC = 0.978, p < 0.001; Left eye: Spearman's r = 0.931, 95% CI = 0.866-0.964, ICC = 0.985, p < 0.001;). Between instruments, the majority of TFBUT measurements showed good agreement on Bland Altman plot. A high concordance was observed in TFBUT, when assessed by an ophthalmologist and an ophthalmic postgraduate student (Left eye: LAS ICC = 0.951, p < 0.001; Left eye: slit-lamp microscope ICC = 0.944, p < 0.001).

CONCLUSIONS

Compared with the conventional slit-lamp microscope, the LAS has sufficient validity for evaluating TFBUT in clinics or at home.

Artificial intelligence to estimate the tear film breakup time and diagnose dry eye disease

The use of artificial intelligence (AI) in the diagnosis of dry eye disease (DED) remains limited due to the lack of standardized image formats and analysis models. To overcome these issues, we used the Smart Eye Camera (SEC), a video-recordable slit-lamp device, and collected videos of the anterior segment of the eye. This study aimed to evaluate the accuracy of the AI algorithm in estimating the tear film breakup time and apply this model for the diagnosis of DED according to the Asia Dry Eye Society (ADES) DED diagnostic criteria. Using the retrospectively corrected DED videos of 158 eyes from 79 patients, 22,172 frames were annotated by the DED specialist to label whether or not the frame had breakup. The AI algorithm was developed using the training dataset and machine learning. The DED criteria of the ADES was used to determine the diagnostic performance. The accuracy of tear film breakup time estimation was 0.789 (95% confidence interval (CI) 0.769-0.809), and the area under the receiver operating characteristic curve of this AI model was 0.877 (95% CI 0.861-0.893). The sensitivity and specificity of this AI model for the diagnosis of DED was 0.778 (95% CI 0.572-0.912) and 0.857 (95% CI 0.564-0.866), respectively. We successfully developed a novel AI-based diagnostic model for DED. Our diagnostic model has the potential to enable ophthalmology examination outside hospitals and clinics.

[Corneal endothelial cell photography: comparison of smartphones]

BACKGROUND

The documentation of ophthalmologic findings using smartphone photography can confirm diagnoses and enable follow-up assessments in outpatient care. Photographing corneal endothelial cells using a smartphone on a slit lamp is complex for both smartphone and examiner. Smartphone models differ in their ability to quickly and safely take images of the corneal endothelium.

AIM OF THIS WORK

In this paper different smartphone models are presented with respect to their applicability for corneal endothelial cell photography and success factors for good smartphone imaging are described.

MATERIAL AND METHODS

In a cross-sectional study, a selection of 16 different smartphone models were compared with respect to their use in corneal endothelial cell photography. The smartphones were attached to the slit lamp eyepiece using an adjustable adapter. It was tested whether high-quality endothelial cell images of healthy subjects could be obtained within 3 min using the standard photo app of the respective smartphone. In addition, the subjective difficulty of creating the image was recorded. Factors contributing to successful imaging of corneal endothelial cells were summarized in a figure.

RESULTS AND DISCUSSION

Distinct differences regarding feasibility and quality of endothelial cell photography were detected between the different smartphones. Not every smartphone is suitable for endothelial cell photography.

Computational intelligence in eye disease diagnosis: a comparative study

In recent years, eye disorders are an important health issue among older people. Generally, individuals with eye diseases are unaware of the gradual growth of symptoms. Therefore, routine eye examinations are required for early diagnosis. Usually, eye disorders are identified by an ophthalmologist via a slit-lamp investigation. Slit-lamp interpretations are inadequate due to the differences in the analytical skills of the ophthalmologist, inconsistency in eye disorder analysis, and record maintenance issues. Therefore, digital images of an eye and computational intelligence (CI)-based approaches are preferred as assistive methods for eye disease diagnosis. A comparative study of CI-based decision support models for eye disorder diagnosis is presented in this paper. The CI-based decision support systems used for eye abnormalities diagnosis were grouped as anterior and retinal eye abnormalities diagnostic systems, and numerous algorithms used for diagnosing the eye abnormalities were also briefed. Various eye imaging modalities, pre-processing methods such as reflection removal, contrast enhancement, region of interest segmentation methods, and public eye image databases used for CI-based eye disease diagnosis system development were also discussed in this paper. In this comparative study, the reliability of various CI-based systems used for anterior eye and retinal disorder diagnosis was compared based on the precision, sensitivity, and specificity in eye disease diagnosis. The outcomes of the comparative analysis indicate that the CI-based anterior and retinal disease diagnosis systems attained significant prediction accuracy. Hence, these CI-based diagnosis systems can be used in clinics to reduce the burden on physicians, minimize fatigue-related misdetection, and take precise clinical decisions.

P4 Medicine for Heterogeneity of Dry Eye: A Mobile Health-based Digital Cohort Study

During the 5th Science, Technology, and Innovation Basic Plan, the Japanese government proposed a novel societal concept -Society 5.0- that promoted a healthcare system characterized by its capability to provide unintrusive, predictive, longitudinal care through the integration of cyber and physical space. The role of Society 5.0 in managing our quality of vision will become more important in the modern digitalized and aging society, both of which are known risk factors for developing dry eye. Dry eye is the most common ocular surface disease encountered in Japan with symptoms including increased dryness, eye discomfort, and decreased visual acuity. Owing to its complexity, implementation of P4 (predictive, preventive, personalized, participatory) medicine in managing dry eye requires a comprehensive understanding of its pathology, as well as a strategy to visualize and stratify its risk factors. Using DryEyeRhythm®, a mobile health (mHealth) smartphone software (app), we established a route to collect holistic medical big data on dry eye, such as the subjective symptoms and lifestyle data for each individual. The studies to date aided in determining the risk factors for severe dry eye, the association between major depressive disorder and dry eye exacerbation, eye drop treatment adherence, app-based stratification algorithms based on symptomology, blink detection biosensoring as a dry eye-related digital phenotype, and effectiveness of app-based dry eye diagnosis support compared to traditional methods. These results contribute to elucidating disease pathophysiology and promoting preventive and effective measures to counteract dry eye through mHealth.

Towards a Connected Mobile Cataract Screening System: A Future Approach

Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities. Over the years, the proposed methods have shown significant improvement and reasonable effort towards automated cataract detection and grading systems that utilise various imaging modalities, such as optical coherence tomography (OCT), fundus, and slit-lamp images. However, more robust and fully automated cataract detection and grading systems are still needed. In addition, imaging modalities such as fundus, slit-lamps, and OCT images require medical equipment that is expensive and not portable. Therefore, the use of digital images from a smartphone as the future of cataract screening tools could be a practical and helpful solution for ophthalmologists, especially in rural areas with limited healthcare facilities.

Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity

PURPOSE

To develop and evaluate deep learning models to perform automated diagnosis and quantitative classification of age-related cataract, including all three anatomical types, from anterior segment photographs.

DESIGN

Application of deep learning models to Age-Related Eye Disease Study (AREDS) dataset.

PARTICIPANTS

18,999 photographs (6,333 triplets) from longitudinal follow-up of 1,137 eyes (576 AREDS participants).

METHODS

Deep learning models were trained to detect and quantify nuclear cataract (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical (CLO; scale 0-100%) and posterior subcapsular (PSC; scale 0-100%) cataract from retroillumination photographs. Model performance was compared with that of 14 ophthalmologists and 24 medical students. The ground truth labels were from reading center grading.

MAIN OUTCOME MEASURES

Mean squared error (MSE).

RESULTS

On the full test set, mean MSE values for the deep learning models were: 0.23 (SD 0.01) for NS, 13.1 (SD 1.6) for CLO, and 16.6 (SD 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for the models was 0.23 (SD 0.02), compared to 0.98 (SD 0.23; p=0.000001) for the ophthalmologists, and 1.24 (SD 0.33; p=0.000005) for the medical students. For CLO, mean MSE values were 53.5 (SD 14.8), compared to 134.9 (SD 89.9; p=0.003) and 422.0 (SD 944.4; p=0.0007), respectively. For PSC, mean MSE values were 171.9 (SD 38.9), compared to 176.8 (SD 98.0; p=0.67) and 395.2 (SD 632.5; p=0.18), respectively. In external validation on the Singapore Malay Eye Study (sampled to reflect the distribution of cataract severity in AREDS), MSE was 1.27 for NS and 25.5 for PSC.

CONCLUSIONS

A deep learning framework was able to perform automated and quantitative classification of cataract severity for all three types of age-related cataract. For the two most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), the accuracy was similar. The framework may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are publicly available at https://XXX.

Design and Performance Characterization of a Novel, Smartphone-Based, Portable Digital Slit Lamp for Anterior Segment Screening Using Telemedicine

Purpose

Telemedicine-enabled, portable digital slit lamps can help to decentralize screening to close-to-patient contexts. We report a novel design for a portable, digital slit lamp using a smartphone. It works on an advanced optical design and has the capability of instantaneous, objective photodocumentation to capture anterior segment images and is telemedicine-enabled.

Methods

The device is constructed keeping its usability and the importance of design ergonomics for nonspecialized field personnel in mind. The optical design is described, and the resolution and magnification are compared with traditional desktop-based slit lamps. A Health Insurance Portability and Accountability Act (HIPAA)–compliant, patient management software is integrated to synchronize the captured images with a secure cloud server along with a sharpness algorithm to extract the best focused frames of the cornea, iris, and lens, from videos. We demonstrate its photodocumentation ability and teleophthalmology feasibility by capturing images in a pilot study from nine subjects.

Results

Images were obtained in various illumination, magnification, and filter settings. Synchronous and asynchronous teleophthalmology consults were conducted. The performance of the device was shown to be limited by the smartphone sensor resolution and not the optical design, because the Air Force target resolution was found to be the same on smartphone-mounted traditional slit lamps despite a lower magnification.

Conclusions

The novel, portable, digital slit lamp with advanced optical design using smartphones has the ability to screen for anterior segment pathologies using telemedicine.

Translational Relevance

A portable, telemedicine-friendly, ergonomically designed, slit lamp used by nonspecialist personnel allows for both synchronous and asynchronous modes of consultation at remote locations, facilitating mass screening programs.

Smart Eye Camera: A Validation Study for Evaluating the Tear Film Breakup Time in Human Subjects

Purpose

This study aimed to demonstrate the efficacy of a "Smart Eye Camera (SEC)" in comparison with the efficacy of the conventional slit-lamp microscope by evaluating their diagnostic functionality for dry eye disease (DED) in clinical cases.

Methods

This retrospective study included 106 eyes from 53 adult Japanese patients who visited the Ophthalmology outpatient clinics in Keio University Hospital from June 2019 to March 2020. Tear film breakup time (TFBUT) and corneal fluorescence score (CFS) measurements for the diagnosis of DED were compared between the conventional slit-lamp microscope and SEC.

Results

The objective findings of DED showed that there was a strong correlation between the conventional slit-lamp microscope and SEC with respect to TFBUT and CFS results (Spearman's r = 0.887, 95% confidence interval [CI] = 0.838-0.922, and r = 0.920, 95% CI = 0.884-0.945, respectively). The interobserver reliability between the conventional slit-lamp microscope and SEC showed a moderate agreement (weighted Kappa κ = 0.527, 95% CI = 0.517-0.537 and κ = 0.550, 95% CI = 0.539-0.561 for TFBUT and CFS, respectively). The diagnostic performance of the SEC for Asia Dry Eye Society diagnostic criteria showed a sensitivity of 0.957 (95% CI = 0.841-0.992), specificity of 0.900 (95% CI = 0.811-0.927), positive predictive value of 0.880 (95% CI = 0.774-0.912), and negative predictive value of 0.964 (95% CI = 0.869-0.993). Moreover, the area under the receiver operating characteristic curve was 0.928 (95% CI = 0.849-1.000).

Conclusions

Compared with the conventional slit-lamp microscope, SEC has sufficient validity and reliability for diagnosing DED in the clinical setting.

Translational Relevance

The SEC can portably evaluate TFBUT in both basic research and clinical care.

Clinical Observation of Allergic Conjunctival Diseases with Portable and Recordable Slit-Lamp Device

BACKGROUND

The incidence of allergic conjunctival diseases (ACDs) is gradually increasing worldwide. Both ophthalmologists and non-ophthalmologists prescribe eye drops to treat ACDs; however, there are many cases which are treated without sufficient examination and diagnosis of the eyes. We have invented a portable, recordable, and smartphone-attachable slit-lamp device-Smart Eye Camera (SEC). The purpose of this study was to compare the diagnostic abilities of ACDs between the SEC and the conventional, non-portable slit-lamp microscope.

METHODS

This prospective observational study included 32 eyes of 17 Japanese patients (mean age: 21.5 ± 14.8 years; range: 11-51 years; female: 5). The severity of 10 objective signs in the palpebral conjunctiva, bulbar conjunctiva, limbus, and cornea were scored on a grading scale of 0 to 4 (0 = normal; 1+ = mild; 2+ = moderate; 3+ = severe), respectively. First, the conventional slit-lamp microscope was used to examine the grade of the ACDs. Second, another ophthalmologist filmed the eyes using the SEC and two other ophthalmologists evaluated the grades on another day. The correlation and inter-rater reproducibility in total scores among the two devices were determined.

RESULTS

Total scores of clinical signs, evaluated by the two approaches, correlated significantly (both eyes: r = 0.918 (95% CI: 0.839 to 0.959; p < 0.001)), with substantial inter-rater agreement (weighted κ value = 0.631 (95% CI: 0.601 to 0.661; p < 0.001)).

CONCLUSIONS

The SEC is as reliable as the conventional non-portable slit-lamp microscope for assessing ACDs.

Renal cell carcinoma choroidal metastasis recorded by smartphone with interface eyepiece adapter mounted on slit lamp: A case report

RATIONALE

Ocular metastasis of renal cell carcinoma (RCC) is rare, and mainly located on the choroid. We report a choroidal metastasis from RCC, which was recorded by a smartphone with an interface eyepiece adapter mounted on a slit lamp.

PATIENT CONCERNS

A 45-year-old female presented with 1-month history of painless occlusion of the vision field on the left eye, who had undergone right nephrectomy for RCC 19 months ago.

DIAGNOSES

A smooth, hemispherical and brown protrusion was found behind the pupil nasally. An enhanced computed tomography scan of the orbit showed a slightly high-density hemispherical nodule involving the nasal portions of the left eyeball, the enhancement of the lesion was obvious and homogeneous. A metastatic choroidal space-occupying lesion from RCC was highly suspected according to the clinical and radiological findings.

INTERVENTIONS

The patient was advised to undergo further treatment, such as radiotherapy.

OUTCOMES

The images of choroid metastasis were recorded by the smartphone with the interface eyepiece adapter mounted on the slit lamp handily.

CONCLUSIONS

The smartphone with an interface eyepiece adapter mounted on the slit lamp can be widely used to record the precious images in the clinic in a timely manner.

A Study Validating the Estimation of Anterior Chamber Depth and Iridocorneal Angle with Portable and Non-Portable Slit-Lamp Microscopy

This study assessed the anterior chamber depth (ACD) and iridocorneal angle using a portable smart eye camera (SEC) compared to the conventional slit-lamp microscope and anterior-segment optical coherence tomography (AS-OCT). This retrospective case-control study included 170 eyes from 85 Japanese patients. The correlation between the ACD evaluations conducted with the SEC and conventional slit-lamp was high (r = 0.814). The correlation between the Van-Herick Plus grade obtained using two devices was also high (r = 0.919). A high kappa value was observed for the Van-Herick Plus grading (Kappa = 0.757). A moderate correlation was observed between the ACD measured using AS-OCT and the slit-lamp image acquired with the conventional slit-lamp microscope and SEC (r = 0.609 and 0.641). A strong correlation was observed between the trabecular-iris angle (TIA) measured using AS-OCT and Van-Herick Plus grade obtained with the conventional slit-lamp microscope and SEC (r = 0.702 and 0.764). Strong correlations of ACD evaluation and high kappa value of the Van-Herick Plus grading indicated the adequate subjective assessment function of the SEC. Moderate correlations between the ACD objective measurement and evaluation and strong correlation between the TIA and Van-Herick Plus grade suggested the good objective assessment function of the SEC. The SEC demonstrated adequate performance for ACD evaluation and angle estimation.