Unveiling Key Pathological Indicators for Disease Progression in Vogt Koyanagi Harada Disease and Sympathetic Ophthalmia Through Advanced Choroidal Volume Analysis.

PURPOSE: To evaluate the association between quantitative parameters derived from volume analysis of optical coherence tomography (OCT) data and disease worsening in Vogt-Koyanagi-Harada disease (VKHD) and sympathetic ophthalmia (SO). METHODS: This retrospective study, conducted at Osaka University Hospital, employed swept-source OCT scans from patients diagnosed with VKHD or SO between October 2012 and January 2021. The choroidal vessel structure was segmented and visualized in three dimensions, generating quantitative vessel volume maps. Region-specific choroidal vessel volume (CVV), choroidal volume (CV), and vessel index (VI) were scrutinized for their potential correlation with disease severity. RESULTS: Thirty-five eyes of 18 VKHD and 2 SO patient (8 females, 10 males) were evaluated. OCT-derived CVV maps revealed regional CV alterations in VKHD and SO patients. Two parameters, i.e. CV at 3- and 6-month follow-ups (p = 0.044, p = 0.040, respectively, with area under the ROC curve of 0.70) and CVV at 6 months (p = 0.046, area under the ROC curve of 0.71), were significantly higher in recurrent VKHD and SO compared to effectively treated cases. CONCLUSIONS: The volume analysis of OCT images facilitates a three-dimensional visualization of choroidal alterations, which may serve as a reflection of disease severity in VKHD and SO patients. Furthermore, noninvasive initial CVV or CV measurements may serve as potential biomarkers for predicting disease recurrence in VKHD and SO.

Choroidal Vessel and Stromal Volumetric Analysis After Photodynamic Therapy or Focal Laser for Central Serous Chorioretinopathy.

Purpose: To utilize volumetric analysis to quantify volumetric changes in choroidal vessels and stroma after photodynamic therapy (PDT) and focal laser photocoagulation (PC) for central serous chorioretinopathy (CSCR). Methods: This retrospective, comparative study included 58 eyes (58 patients) with CSCR (PC, 33 eyes; PDT, 25 eyes) followed up with swept-source optical coherence tomography at 3 months after treatment. Three-dimensional (3D) choroidal vessel and stromal volumes in each area of the central 1.5-mm-diameter circle, the torus-shaped area with 6-mm-diameter circle excluding the area of the central 1.5-mm-diameter circle, and the treated area of the Early Treatment Diabetic Retinopathy Study (ETDRS) grid centered at the fovea were analyzed using a deep learning-based method. Changes in volume at baseline and 1 and 3 months after treatment were compared. Results: The mean patient age was 49.3 ± 10.5 years. In the central 1.5-mm-diameter circle, the mean vessel and stromal volume rates significantly decreased after the treatment in both the PDT and PC groups (P = 0.00029 and P = 0.0014, respectively), and significant differences between the PDT and PC groups of continuous variables within times were observed in both volumes (P = 0.024 and P = 0.037, respectively). In the torus-shaped area and treated area, the PDT and PC groups both showed similar decreases in vessel and stromal volume over time. Conclusions: In the 3D optical coherence tomography volumetric analysis, both PDT and focal PC reduced choroid vessel volume in eyes with CSCR. Translational Relevance: This new finding is useful in elucidating the pathogenesis and healing mechanisms of CSCR.

Risk prediction of chronic diseases with a two-stage semi-supervised clustering method.

Early detection of chronic diseases such as cardiovascular disease (CVD) and diabetes can make the difference between life and death. Previous studies have demonstrated the feasibility of disease diagnosis and prediction using machine learning and disease-indicating biomarkers. The aim of this study is to develop a method to detect the risk of future disease even when disease-indicating biomarker readings are in the normal range. Data from the US Centers for Disease Control and Prevention (CDC) National Health and Nutrition Examination Surveys (NHANES) are used for this study. A two-stage semi-supervised K-Means (SSK-Means) clustering approach was developed to identify the underlying risk of each individual and categorize them into high or low-risk groups for CVD and diabetes. Our developed method of classification can identify groups as high risk or low risk, even if they would have been considered normal using traditional biomarker threshold criteria. For CVD, the SSK-Means clustering results showed that individuals over 30 years of age in the high-risk group were almost twice as likely to develop CVD as individuals in the low-risk group. For diabetes, the SSK-Means clustering results showed that individuals over 50 years in the high-risk group have at least two times the risk of developing diabetes compared with individuals in the low-risk group.

Determining posterior vitreous structure by analysis of images obtained by AI-based 3D segmentation and ultrawidefield optical coherence tomography.

AIMS: To determine the three-dimensional (3D) structure of the vitreous fluid including the posterior precortical vitreous pockets (PPVP), Cloquet's canal and cisterns in healthy subjects by AI-based segmentation of the vitreous of swept-source optical coherence tomography (OCT) images. In addition, to analyse the vitreous structures over a wide and deep area using ultrawidefield swept-source OCT (UWF-OCT). METHODS: Ten eyes of six patients with the mean age was 40.7±8.4 years and the mean refractive error (spherical equivalent) was -3.275±2.2 diopters were examined. RESULTS: In the UWF OCT images, the structure of the vitreous was observed in detail over 23 mm wide and 5 mm area. AI-guided analyses showed the complex 3D vitreous structures from any angle. Cisterns were observed to overlie the PPVP from the anterior. The morphology and locations of the cisterns varied among the subjects but tended to be similar in the two eyes of one individual. Cisterns joined the PPVPs superior to the macula to form a large trunk. This joined trunk was clearly seen in 3D images even in eyes whose trunk was not detected in the B scan OCT images. In some eyes, the vitreous had a complex appearance resembling an ant nest without large fluid-filled spaces. CONCLUSIONS: A combination of UWF-OCT and 3D imaging is very helpful in visualising the complex structure of the vitreous. These technologies are powerful tools that can be used to clarify the normal evolution of the vitreous, pathological changes of vitreous and implications of vitreous changes in various vitreoretinal diseases.

Deep learning-based 3D OCT imaging for detection of lamina cribrosa defects in eyes with high myopia.

The lamina cribrosa (LC) is a collagenous tissue located in the optic nerve head, and its dissection is observed in eyes with pathologic myopia as a LC defect (LCD). The diagnosis of LCD has been difficult because the LC is located deep beneath the retinal nerve fibers. The purpose of this study was to determine the prevalence and three-dimensional shape of LCDs in highly myopic eyes. Swept-source OCT scan images of a 3 × 3-mm cube centered on the optic disc were obtained from 119 eyes of 62 highly myopic patients. Each LC was manually labelled in cross-sectional OCT images along the axial, coronal, and sagittal planes. A deep convolutional neural network (DCNN) was trained with the manually labeled images, and the trained DCNN was applied to the detection of the LC in every image in each plane. Three-dimensional images of the LC were generated from the labeled image of each eye. The results showed that LCDs were detected in 12 of the 42 (29%) eyes in which an LC was visible. The LCDs ran vertically at the temporal edge of the optic disc. In conclusion, 3D OCT imaging with the application of DCNN is helpful in diagnosing LCDs.

Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images.

The structure of the human vitreous varies considerably because of age-related liquefactions of the vitreous gel. These changes are poorly studied in vivo mainly because their high transparency and mobility make it difficult to obtain reliable and repeatable images of the vitreous. Optical coherence tomography can detect the boundaries between the vitreous gel and vitreous fluid, but it is difficult to obtain high resolution images that can be used to convert the images to three-dimensional (3D) images. Thus, the purpose of this study was to determine the shape and characteristics of the vitreous fluid using machine learning-based 3D modeling in which manually labelled fluid areas were used to train deep convolutional neural network (DCNN). The trained DCNN labelled vitreous fluid automatically and allowed us to obtain 3D vitreous model and to quantify the vitreous fluidic cavities. The mean volume and surface area of posterior vitreous fluidic cavities are 19.6 ± 7.8 mm3 and 104.0 ± 18.9 mm2 in eyes of 17 school children. The results suggested that vitreous fluidic cavities expanded as the cavities connects with each other, and this modeling system provided novel imaging markers for aging and eye diseases.

Three-Dimensional Volume Calculation of Intrachoroidal Cavitation Using Deep-Learning-Based Noise Reduction of Optical Coherence Tomography.

Purpose: Intrachoroidal cavitations (ICCs) are peripapillary pathological lesions generally associated with high myopia that can cause visual field (VF) defects. The current study aimed to evaluate a three-dimensional (3D) volume parameter of ICCs segmented from volumetric swept-source optical coherence tomography (SS-OCT) images processed using deep learning (DL)-based noise reduction and to investigate its correlation with VF sensitivity. Methods: Thirteen eyes of 12 consecutive patients with peripapillary ICCs were enrolled. DL-based denoising and further analyses were applied to parapapillary 6 × 6-mm volumetric SS-OCT scans. Then, 3D ICC volume and two-dimensional depth and length measurements of the ICCs were calculated. The correlations between ICC parameters and VF sensitivity were investigated. Results: The ICCs were located in the inferior hemiretina in all eyes. ICC volume (P = 0.02; regression coefficient [RC], -0.007) and ICC length (P = 0.04; RC, -4.51) were negatively correlated with the VF mean deviation, whereas ICC depth (P = 0.15) was not. All of the parameters, including ICC volume (P = 0.01; RC, -0.004), ICC depth (P = 0.02; RC, -0.008), and ICC length (P = 0.045; RC, -2.11), were negatively correlated with the superior mean total deviation. Conclusions: We established the volume of ICCs as a new 3D parameter, and it reflected their influence on visual function. The automatic delineation and 3D rendering may lead to improved detection and pathological understanding of ICCs. Translational Relevance: This study demonstrated the correlation between the 3D volume of ICCs and VF sensitivity.

DENOISING SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY VOLUMETRIC SCANS USING A DEEP LEARNING MODEL.

PURPOSE: To evaluate the use of a deep learning noise reduction model on swept source optical coherence tomography volumetric scans. METHODS: Three groups of images including single-line highly averaged foveal scans (averaged images), foveal B-scans from volumetric scans using no averaging (unaveraged images), and deep learning denoised versions of the latter (denoised images) were obtained. We evaluated the potential increase in the signal-to-noise ratio by evaluating the contrast-to-noise ratio of the resultant images and measured the multiscale structural similarity index to determine whether the unaveraged and denoised images held true in structure to the averaged images. We evaluated the practical effects of denoising on a popular metric of choroidal vascularity known as the choroidal vascularity index. RESULTS: Ten eyes of 10 subjects with a mean age of 31 years (range 24-64 years) were evaluated. The deep choroidal contrast-to-noise ratio mean values of the averaged and denoised image groups were similar (7.06 vs. 6.81, P = 0.75), and both groups had better maximum contrast-to-noise ratio mean values (27.65 and 46.34) than the unaveraged group (14.75; P = 0.001 and P < 0.001, respectively). The mean multiscale structural similarity index of the average-denoised images was significantly higher than the one from the averaged--unaveraged images (0.85 vs. 0.61, P < 0.001). Choroidal vascularity index values from averaged and denoised images were similar (71.81 vs. 71.16, P = 0.554). CONCLUSION: Using three different metrics, we demonstrated that the deep learning denoising model can produce high-quality images that emulate, and may exceed, the quality of highly averaged scans.

Diagnosis of Choroidal Disease With Deep Learning-Based Image Enhancement and Volumetric Quantification of Optical Coherence Tomography.

Purpose: The purpose of this study was to quantify choroidal vessels (CVs) in pathological eyes in three dimensions (3D) using optical coherence tomography (OCT) and a deep-learning analysis. Methods: A single-center retrospective study including 34 eyes of 34 patients (7 women and 27 men) with treatment-naïve central serous chorioretinopathy (CSC) and 33 eyes of 17 patients (7 women and 10 men) with Vogt-Koyanagi-Harada disease (VKH) or sympathetic ophthalmitis (SO) were imaged consecutively between October 2012 and May 2019 with a swept source OCT. Seventy-seven eyes of 39 age-matched volunteers (26 women and 13 men) with no sign of ocular pathology were imaged for comparison. Deep-learning-based image enhancement pipeline enabled CV segmentation and visualization in 3D, after which quantitative vessel volume maps were acquired to compare normal and diseased eyes and to track the clinical course of eyes in the disease group. Region-based vessel volumes and vessel indices were utilized for disease diagnosis. Results: OCT-based CV volume maps disclose regional CV changes in patients with CSC, VKH, or SO. Three metrics, (i) choroidal volume, (ii) CV volume, and (iii) CV index, exhibit high sensitivity and specificity in discriminating pathological choroids from healthy ones. Conclusions: The deep-learning analysis of OCT images described here provides a 3D visualization of the choroid, and allows quantification of features in the datasets to identify choroidal disease and distinguish between different diseases. Translational Relevance: This novel analysis can be applied retrospectively to existing OCT datasets, and it represents a significant advance toward the automated diagnosis of choroidal pathologies based on observations and quantifications of the vasculature.

Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans.

A deep-learning (DL) based noise reduction algorithm, in combination with a vessel shadow compensation method and a three-dimensional (3D) segmentation technique, has been developed to achieve, to the authors best knowledge, the first automatic segmentation of the anterior surface of the lamina cribrosa (LC) in volumetric ophthalmic optical coherence tomography (OCT) scans. The present DL-based OCT noise reduction algorithm was trained without the need of noise-free ground truth images by utilizing the latest development in deep learning of de-noising from single noisy images, and was demonstrated to be able to cover more locations in the retina and disease cases of different types to achieve high robustness. Compared with the original single OCT images, a 6.6 dB improvement in peak signal-to-noise ratio and a 0.65 improvement in the structural similarity index were achieved. The vessel shadow compensation method analyzes the energy profile in each A-line and automatically compensates the pixel intensity of locations underneath the detected blood vessel. Combining the noise reduction algorithm and the shadow compensation and contrast enhancement technique, medical experts were able to identify the anterior surface of the LC in 98.3% of the OCT images. The 3D segmentation algorithm employs a two-round procedure based on gradients information and information from neighboring images. An accuracy of 90.6% was achieved in a validation study involving 180 individual B-scans from 36 subjects, compared to 64.4% in raw images. This imaging and analysis strategy enables the first automatic complete view of the anterior LC surface, to the authors best knowledge, which may have the potentials in new LC parameters development for glaucoma diagnosis and management.