Quantitative analysis of retinal vasculature in normal eyes using ultra-widefield fluorescein angiography

AIM: To quantify the area and density of retinal vascularity by ultra-widefield fluorescein angiography (UWFA). METHODS: In a retrospective study, UWFA images were obtained using an ultra-widefield imaging device in 42 normal eyes of 42 patients. Central and peripheral steered images were used to define the edge of retinal vasculature by a certified grader. The length from the center of the optic disc to the edge of retinal vascularity (RVL) in each quadrant and the total retinal vascular perfusion area (RVPA) were determined by the grader using OptosAdvance software. The density of retinal vascularity (RVD) was quantified in different zones of central-steered images using Image J software. RESULTS: Among 42 healthy eyes, the values for mean RVL in each quadrant were 19.007±0.781 mm (superior), 18.467±0.869 mm (inferior), 17.738±0.622 mm (nasal) and 24.241±1.336 mm (temporal). The mean RVPA was 1140.117±73.825 mm2. The mean RVD of the total retina was 4.850%±0.638%. RVD varied significantly between different retina zones (P<0.001), and significant differences existed in the RVD values for total retinal area in patients over 50 years old compared to those under 50 years old (P=0.033). No gender difference was found. CONCLUSION: The UWFA device can be a promising tool for analyzing the overall retinal vasculature and may provide a better understanding of retinal vascular morphology in normal eyes. Aging may be related to lower RVD.

Universal architecture of corneal segmental tomography biomarkers for artificial intelligence-driven diagnosis of early keratoconus

AimsTo develop a comprehensive three-dimensional analyses of segmental tomography (placido and optical coherence tomography) using artificial intelligence (AI).MethodsPreoperative imaging data (MS-39, CSO, Italy) of refractive surgery patients with stable outcomes and diagnosed with asymmetric or bilateral keratoconus (KC) were used. The curvature, wavefront aberrations and thickness distributions were analysed with Zernike polynomials (ZP) and a random forest (RF) AI model. For training and cross-validation, there were groups of healthy (n=527), very asymmetric ectasia (VAE; n=144) and KC (n=454). The VAE eyes were the fellow eyes of KC patients but no further manual segregation of these eyes into subclinical or forme-fruste was performed.ResultsThe AI achieved an excellent area under the curve (0.994), accuracy (95.6%), recall (98.5%) and precision (92.7%) for the healthy eyes. For the KC eyes, the same were 0.997, 99.1%, 98.7% and 99.1%, respectively. For the VAE eyes, the same were 0.976, 95.5%, 71.5% and 91.2%, respectively. Interestingly, the AI reclassified 36 (subclinical) of the VAE eyes as healthy though these eyes were distinct from healthy eyes. Most of the remaining VAE (n=104; forme fruste) eyes retained their classification, and were distinct from both KC and healthy eyes. Further, the posterior surface features were not among the highest ranked variables by the AI model.ConclusionsA universal architecture of combining segmental tomography with ZP and AI was developed. It achieved an excellent classification of healthy and KC eyes. The AI efficiently classified the VAE eyes as ‘subclinical’ and ‘forme-fruste’.

Anterior-Segment Swept-Source Ocular Coherence Tomography and Scheimpflug Imaging Agreement for Keratometry and Pupil Measurements in Healthy Eyes

This study examines agreement between the devices Anterion® and Pentacam HR® used for corneal and pupil measurements in healthy eyes. The parameters compared between the two devices were: anterior Km (D), anterior K2 (D), anterior K1 (D), anterior K1 axis (°), anterior astigmatism (D), anterior K max (D), posterior Km (D), posterior K2 (D), posterior K1 (D), posterior K1 axis (°), posterior astigmatism (D), CCT (µm), thinnest point thickness (µm), thinnest point X-coordinate (mm), thinnest point Y-coordinate (mm), pupil diameter (mm), pupil center-corneal vertex distance (mm) (angle kappa), pupil centroid angle (°), pupil centroid X-coordinate (mm), and pupil centroid Y-coordinate (mm). The Student’s t test for independent samples identified significant differences (p < 0.005) between devices for the measurements anterior and posterior flat K axis, posterior flat K, steep K, and mean K. For these last three measurements, although significant, none of the differences were clinically relevant. Corneal power and thickness measurements except Kf axis showed excellent agreement between Anterion and Pentacam. In a clinical setting we would not recommend the interchangeable use of Pentacam and Anterion for measurement of pupil parameters.

Long-term repeatability of peripapillary optical coherence tomography angiography measurements in healthy eyes

This is a prospective observational study to establish the short- and long-term repeatability of measurements of peripapillary optical coherence tomography angiography (OCTA) parameters in healthy eyes and identify factors affecting long-term repeatability. We enrolled 84 healthy eyes. Participants with a history of any ophthalmic disease (except high myopia) or intraocular surgery were excluded from the study. An experienced examiner performed OCTA using disc-centered 6 × 6 mm scans. All examinations were conducted twice at 5-min intervals at the initial visit and repeated at least 6 months later. For short-term repeatability, the coefficient of variation (CV) was 2.94–4.22% and the intraclass correlation coefficient (ICC) was 0.840–0.934. For long-term repeatability, the CV was 2.73–3.84% and the ICC was 0.737–0.934. Multivariate analyses showed that the axial length (AL) (B = 0.970; p = 0.002) and mean signal strength (SS) (B = − 2.028; p < 0.001) significantly affected long-term repeatability. Measurements of peripapillary OCTA parameters exhibited excellent short-term and good long-term repeatability in healthy individuals. The mean SS and AL affected long-term repeatability and should be considered while interpreting peripapillary OCTA images.

Corneal Biomechanics Computational Analysis for Keratoconus Diagnosis

For machine learning techniques to be used in early keratoconus diagnosis, researchers aimed to find and model representations of corneal biomechanical characteristics from exam images generated by the Corvis ST. Image segments were used to identify and convert anterior data into vectors for representation and representation of apparent posterior surfaces, apparent pachymetry, and the composition of apparent anterior data in images. Chained (batch images) and simplified with wavelet, the vectors were also arranged as 2D histograms for deep learning use in a neural network. An interval of 0.7843 to 1 and a significance level of 0.0157 were used in the scoring, with the classifications getting points for being as sensitive as they could be while also being as precise as they could be. In order to train and validate the used data from examination bases in Europe and Iraq, in grades I to IV, researchers looked at data from 686 healthy eyes and 406 keratoconus-afflicted eyes. With a score of 0.8247, sensitivity of 89.49%, and specificity of 92.09%, the European database found that apparent pachymetry from batch images applied with level 4 wavelet and processed quickly had the highest accuracy. This is a 2D histogram of apparent pachymetry with a score of 0.8361, which indicates that it is 88.58 percent sensitive and 94.389% specific. According to the findings, keratoconus can be diagnosed using biomechanical models.

Diagnosability of Keratoconus Using Deep Learning With Placido Disk-Based Corneal Topography

Purpose: Placido disk-based corneal topography is still most commonly used in daily practice. This study was aimed to evaluate the diagnosability of keratoconus using deep learning of a color-coded map with Placido disk-based corneal topography.Methods: We retrospectively examined 179 keratoconic eyes [Grade 1 (54 eyes), 2 (52 eyes), 3 (23 eyes), and 4 (50 eyes), according to the Amsler-Krumeich classification], and 170 age-matched healthy eyes, with good quality images of corneal topography measured with a Placido disk corneal topographer (TMS-4TM, Tomey). Using deep learning of a color-coded map, we evaluated the diagnostic accuracy, sensitivity, and specificity, for keratoconus screening and staging tests, in these eyes.Results: Deep learning of color-coded maps exhibited an accuracy of 0.966 (sensitivity 0.988, specificity 0.944) in discriminating keratoconus from normal eyes. It also exhibited an accuracy of 0.785 (0.911 for Grade 1, 0.868 for Grade 2, 0.920 for Grade 3, and 0.905 for Grade 4) in classifying the stage. The area under the curve value was 0.997, 0.955, 0.899, 0.888, and 0.943 as Grade 0 (normal) to 4 grading tests, respectively.Conclusions: Deep learning using color-coded maps with conventional corneal topography effectively distinguishes between keratoconus and normal eyes and classifies the grade of the disease, indicating that this will become an aid for enhancing the diagnosis and staging ability of keratoconus in a clinical setting.

Anaesthetic protocol for paediatric glaucoma examinations: the prospective EyeBIS Study protocol

IntroductionNeonates and young infants with diagnosed or highly suspected glaucoma require an examination under anaesthesia to achieve accurate intraocular pressure (IOP) measurements, since crying or squinting of the eyes may increase IOP and lead to falsely high values. IOP considerably depends on perioperative variables such as haemodynamic factors, anaesthetics, depth of anaesthesia and airway management. The aim of this paper is to report the design and baseline characteristics of EyeBIS, which is a study to develop a standardised anaesthetic protocol for the measurement of IOP under anaesthesia in childhood glaucoma, by investigating the link between the magnitude of IOP and depth of anaesthesia.Methods and analysisThis is a single-centre, prospective cohort study in 100 children with diagnosed or highly suspected glaucoma all undergoing ophthalmological examination under general anaesthesia. 20 children, who undergo general anaesthesia for other reasons, are included as controls. The primary outcome measure is the establishment of a standardised anaesthetic protocol for IOP measurement in childhood glaucoma by assessing the relationship between IOP and depth of anaesthesia (calculated as an electroencephalography variable, the bispectral index), with special emphasis on airway management and haemodynamic parameters. The dependence of IOP under anaesthesia on airway management and haemodynamic parameters will be described, using a mixed linear model. Restricting the model to patients with healthy eyes will allow to determine a 95% reference region, in which 95% of the measurement values of patients with healthy eyes can be expected.Ethics and disseminationThe study has been approved by the local ethics committee of the Medical Association of Rhineland-Palatine (Ethik-Kommisssion der Landesaerztekammer Rheinland-Pfalz), Germany (approval number: 2019-14207). This work will be disseminated by publication of peer-reviewed manuscripts, presentation in abstract form at national and international scientific meetings and data sharing with other investigators.Trial registration numberClinicalTrials.gov Registry (NCT03972852).

Comparison among TonoVet, TonoVet Plus, Tono-Pen Avia Vet, and Kowa HA-2 portable tonometers for measuring intraocular pressure in dogs

Background and Aim: Tonometers are an important instrument for measuring intraocular pressure (IOP) in the diagnosis of glaucoma or uveitis. This study aimed to compare the accuracy of the main types of tonometers with different IOP measurement methodologies in dogs: TonoVet and TonoVet Plus (rebound), Tono-Pen Avia Vet (applanation), and Kowa HA-2 (Goldmann applanation). Materials and Methods: IOP was measured in 152 eyes of 76 dogs. A postmortem study was performed by comparing manometry and tonometry values and calculating the correlation coefficient (r2), in vivo real IOP (manometry) among the tonometers was compared, and an outpatient study was conducted with healthy eyes and eyes with signs of glaucoma and uveitis. Results: In the postmortem study, the values of r2 in descending order were Kowa (0.989), TonoVet Plus (0.984), TonoVet (0.981), and Tono-Pen Avia Vet (0.847). The IOP values in mmHg in the in vivo study were as follows: Aneroid manometer (16.8±2.5.7), TonoVet (18.1±2.9), TonoVet Plus (20.6±2.3), Tono-Pen Avia Vet (17.1±2.5), and Kowa (16.1±1.7); in outpatient clinics: TonoVet (16.8±3.8), TonoVet Plus (19.2±2.9), Tono-Pen Avia Vet (16.2±2.4), and Kowa (15.0±1.3); glaucoma: TonoVet (30.2±3.5), TonoVet Plus (35.0±6.1), Tono-Pen Avia Vet (29.5±4.2), and Kowa (23.9±5.0); and uveitis: TonoVet (14.2±1.4), TonoVet Plus (17.6±1.9), Tono-Pen Avia Vet (13.7±2.1), and Kowa (12.6±1.7). Conclusion: There was a strong correlation between IOP values and manometry in all the tonometers. The highest values were obtained with TonoVet Plus and the lowest with Kowa HA-2. All tonometers accurately measured IOP in dogs, including the latest TonoVet Plus, which showed an excellent correlation coefficient.