A novel approach to anterior segment imaging with smartphones in the COVID-19 era

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.

Red-Free (Green) Filter-Enhanced Gonioscopy with Smartphone: A Pilot Study

AIM

This pilot study aimed to demonstrate the usefulness of the red-free (green) filter as a novel modification for better iridocorneal angle visibility during routine gonioscopy.

METHODS

As a pilot project, we observed 20 eyes of 10 patients aged 22 to 60 who attended the glaucoma department of a tertiary eye hospital in Bangladesh. All patients underwent a thorough ocular examination, from best-corrected visual acuity to the dilated fundus evaluation. Images and videos were obtained with a smartphone during gonioscopy with standard halogen light and the red-free (green) filter, subjectively analyzed by two glaucoma specialists.

RESULTS

The mean age of the patients was 37 ± 13.42 years, of whom 70% were men. In this study, 40% of the patients had open-angle glaucoma, and 60% had open-angle without glaucoma. Without impairing the ability to see the iridocorneal angle structures in detail, the gonioscopy picture contrast was enhanced objectively for red-free filter images compared to standard light photos. The built-in warm filter of the slit-lamp also provided better visualization of the iridocorneal angle structures.

CONCLUSION

Using the red-free (green) filter and a warm filter instead of the traditionally used standard light of the slit-lamp may significantly enhance the diagnostic capability during routine gonioscopy.

Telemedicine for Cornea and External Disease: A Scoping Review of Imaging Devices

OBJECTIVE

The objective of this scoping review is to understand the extent and type of evidence in relation to telemedicine imaging devices for cornea and external segment conditions.

INTRODUCTION

The coronavirus pandemic has emphasized the benefits of telemedicine in diagnosing and managing ocular diseases. With the rapid advancement of technology in slit lamp biomicroscopes, smartphones and other ocular surface imaging modalities, telemedicine applications for cornea and external diseases have become an active area of research.

INCLUSION CRITERIA

For studies to be included, they had to discuss the concept of imaging devices for cornea and external diseases in the context of telemedicine. There was no restriction on the studied population or participants.

METHODS

A scoping review was conducted according to an a priori protocol. Documents written in English were identified from the PubMed and Embase databases and searches. Anterior segment imaging devices were then classified into different categories.

RESULTS

Anterior segment imaging devices identified in this review included 19 slit lamp-based devices, 17 smartphone-based devices and 15 other devices. These tools can detect a wide variety of cornea and external diseases (e.g., pterygium, conjunctivitis, corneal opacity, corneal ulcer, and blepharitis). Fewer than half of the devices (24/51) were assessed for diagnostic performance. Their diagnostic accuracy varied greatly from condition to condition and from device to device. The inter-rater reliability of different photo-graders assessing images was assessed in only a few studies.

CONCLUSIONS

Anterior segment imaging devices are promising tools for remote diagnosis and management of patients with cornea and external disease. However, there are significant gaps in the literature regarding the diagnostic accuracy and inter-rater reliability of several devices. Future research with rigorous methods is required to validate the use of these devices in telemedicine settings.

Smartphone Slit Lamp Imaging-Usability and Quality Assessment

PURPOSE

To assess the usability and image quality of a smartphone adapter for direct slit lamp imaging.

METHODS

A single-center, prospective, clinical study conducted in the Department of Ophthalmology at the University Hospital Zurich, Switzerland. The smartphone group consisted of 26 medical staff (consultants, residents, and students). The control group consisted of one ophthalmic photographer. Both groups took images of the anterior and the posterior eye segment of the same proband. The control group used professional photography equipment. The participant group used an Apple iPhone 11 mounted on a slit lamp via a removable SlitREC smartphone adapter (Custom Surgical GmbH, Munich, Germany). The image quality was graded independently by two blinded ophthalmologists on a scale from 0 (low) to 10 (high quality). Images with a score ≥ 7.0/10 were considered as good as the reference images. The acquisition time was measured. A questionnaire on usability and experience in smartphone and slit lamp use was taken by all of the participants.

RESULTS

Each participant had three attempts at the same task. The overall smartphone quality was 7.2/10 for the anterior and 6.4/10 for the posterior segment. The subjectively perceived difficulty decreased significantly over the course of three attempts (Kendall's W). Image quality increased as well but did not improve significantly from take 1 to take 3. However, the image quality of the posterior segment was significantly, positively correlated (Spearman's Rho) with work experience. The mean acquisition time for anterior segment imaging was faster in the smartphone group compared to the control group (156 vs. 206 s). It was vice versa for the posterior segment (180 vs. 151 s).

CONCLUSION

Slit lamp imaging with the presented smartphone adapter provides high-quality imaging of the anterior segment. Posterior segment imaging remains challenging in terms of image quality. The adapter constitutes a cost-effective, portable, easy-to-use solution for recording ophthalmic photos and videos. It can facilitate clinical documentation and communication among colleagues and with the patient especially outside normal consultation hours. Direct slit lamp imaging allows for time to be saved and increases the independence of ophthalmologists in terms of patient mobility and the availability of photographic staff.

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.