Oxygen-saturation-related functional parameter as a biomarker for diabetes mellitus-extraction method and clinical validation.

Purpose: To develop a computational method for oxygen-saturation-related functional parameter analysis of retinal vessels based on traditional color fundus photography, and to explore their characteristic alterations in type 2 diabetes mellitus (DM). Methods: 50 type 2 DM patients with no-clinically detectable retinopathy (NDR) and 50 healthy subjects were enrolled in the study. An optical density ratio (ODR) extraction algorithm based on the separation of oxygen-sensitive and oxygen-insensitive channels in color fundus photography was proposed. With precise vascular network segmentation and arteriovenous labeling, ODRs were acquired from different vascular subgroups, and the global ODR variability (ODRv) was calculated. Student's t-test was used to analyze the differences of the functional parameters between groups, and regression analysis and receiver operating characteristic (ROC) curves were used to explore the discrimination efficiency of DM patients from healthy subjects based on these functional parameters. Results: There was no significant difference in the baseline characteristics between the NDR and healthy normal groups. The ODRs of all vascular subgroups except the micro venule were significantly higher (p<0.05, respectively) while ODRv was significantly lower (p<0.001) in NDR group than that in healthy normal group. In the regression analysis, the increased ODRs except micro venule and decreased ODRv were significantly correlated with the incidence of DM, and the C-statistic for discrimination DM with all ODR is 0.777 (95% CI 0.687-0.867, p<0.001). Conclusion: A computational method to extract the retinal vascular oxygen-saturation-related optical density ratios (ODRs) with single color fundus photography was developed, and increased ODRs and decreased ODRv of retinal vessels could be new potential image biomarkers of DM.

Classification of Vogt-Koyanagi-Harada disease using feature selection and classification based on wide-field swept-source optical coherence tomography angiography.

Background: Vogt-Koyanagi-Harada (VKH) disease is a common and easily blinded uveitis entity, with choroid being the main involved site. Classification of VKH disease and its different stages is crucial because they differ in clinical manifestations and therapeutic interventions. Wide-field swept-source optical coherence tomography angiography (WSS-OCTA) provides the advantages of non-invasiveness, large-field-of-view, high resolution, and ease of measuring and calculating choroid, offering the potential feasibility of simplified VKH classification assessment based on WSS-OCTA. Methods: 15 healthy controls (HC), 13 acute-phase and 17 convalescent-phase VKH patients were included, undertaken WSS-OCTA examination with a scanning field of 15 × 9 mm2. 20 WSS-OCTA parameters were then extracted from WSS-OCTA images. To classify HC and VKH patients in acute and convalescent phases, two 2-class VKH datasets (HC and VKH) and two 3-class VKH datasets (HC, acute-phase VKH, and convalescent-phase VKH) were established by the WSS-OCTA parameters alone or in combination with best-corrected visual acuity (logMAR BCVA) and intraocular pressure (IOP), respectively. A new feature selection and classification method that combines an equilibrium optimizer and a support vector machine (called SVM-EO) was adopted to select classification-sensitive parameters among the massive datasets and to achieve outstanding classification performance. The interpretability of the VKH classification models was demonstrated based on SHapley Additive exPlanations (SHAP). Results: Based on pure WSS-OCTA parameters, we achieved classification accuracies of 91.61% ± 12.17% and 86.69% ± 8.30% for 2- and 3-class VKH classification tasks. By combining the WSS-OCTA parameters and logMAR BCVA, we achieved better classification performance of 98.82% ± 2.63% and 96.16% ± 5.88%, respectively. Through SHAP analysis, we found that logMAR BCVA and vascular perfusion density (VPD) calculated from the whole field of view region in the choriocapillaris (whole FOV CC-VPD) were the most important features for VKH classification in our models. Conclusion: We achieved excellent VKH classification performance based on a non-invasive WSS-OCTA examination, which provides the possibility for future clinical VKH classification with high sensitivity and specificity.

Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search.

Analysis of the vascular tree is the basic premise to automatically diagnose retinal biomarkers associated with ophthalmic and systemic diseases, among which accurate identification of intersection and bifurcation points is quite challenging but important for disentangling complex vascular network and tracking vessel morphology. In this paper, we present a novel directed graph search-based multi-attentive neural network approach to automatically segment the vascular network and separate intersections and bifurcations from color fundus images. Our approach uses multi-dimensional attention to adaptively integrate local features and their global dependencies while learning to focus on target structures at different scales to generate binary vascular maps. A directed graphical representation of the vascular network is constructed to represent the topology and spatial connectivity of the vascular structures. Using local geometric information including color difference, diameter, and angle, the complex vascular tree is decomposed into multiple sub-trees to finally classify and label vascular feature points. The proposed method has been tested on the DRIVE dataset and the IOSTAR dataset containing 40 images and 30 images, respectively, with 0.863 and 0.764 F1-score of detection points and average accuracy of 0.914 and 0.854 for classification points. These results demonstrate the superiority of our proposed method outperforming state-of-the-art methods in feature point detection and classification.

Portable boom-type ultrahigh-resolution OCT with an integrated imaging probe for supine position retinal imaging.

To expand the clinical applications and improve the ease of use of ultrahigh-resolution optical coherence tomography (UHR-OCT), we developed a portable boom-type ophthalmic UHR-OCT operating in supine position that can be used for pediatric subjects, bedridden patients and perioperative conditions. By integrating the OCT sample arm probe with real-time iris display and automatic focusing electric lens for easy alignment, coupling the probe on a self-locking multi-directional manipulator to reduce motion artifacts and operator fatigue, and installing the OCT module on a moveable cart for system mobility, our customized portable boom-type UHR-OCT enables non-contact, high-resolution and high-stability retinal examinations to be performed on subjects in supine position. The spectral-domain UHR-OCT operates at a wavelength of 845 nm with 130 nm FWHM (full width at half maximum) bandwidth, achieving an axial resolution of ≈2.3µm in tissue with an A-line acquisition rate up to 128 kHz. A high-definition two-dimensional (2D) raster protocol was used for high-quality cross-sectional imaging while a cube volume three-dimensional (3D) scan was used for three-dimensional imaging and en-face reconstruction, resolving major layer structures of the retina. The feasibility of the system was demonstrated by performing supine position 2D/3D retinal imaging on healthy human subjects, sedated infants, and non-sedated awake neonates.

EVA: Fully automatic hemodynamics assessment system for the bulbar conjunctival microvascular network.

BACKGROUND AND OBJECTIVE: Conjunctival microcirculation has been used to quantitatively assess microvascular changes due to systemic disorders. The space between red blood cell clusters in conjunctival microvessels is essential for assessing hemodynamics. However, it causes discontinuities in vessel image segmentation and increases the difficulty of automatically measuring blood velocity. In this study, we developed an EVA system based on deep learning to maintain vessel segmentation continuity and automatically measure blood velocity. METHODS: The EVA system sequentially performs image registration, vessel segmentation, diameter measurement, and blood velocity measurement on conjunctival images. A U-Net model optimized with a connectivity-preserving loss function was used to solve the problem of discontinuities in vessel segmentation. Then, an automatic measurement algorithm based on line segment detection was proposed to obtain accurate blood velocity. Finally, the EVA system assessed hemodynamic parameters based on the measured blood velocity in each vessel segment. RESULTS: The EVA system was validated for 23 videos of conjunctival microcirculation captured using functional slit-lamp microscopy. The U-Net model produced the longest average vessel segment length, 158.03 ± 181.87 µm, followed by the adaptive threshold method and Frangi filtering, which produced lengths of 120.05 ± 151.47 µm and 99.94 ± 138.12 µm, respectively. The proposed method and one based on cross-correlation were validated to measure blood velocity for a dataset consisting of 30 vessel segments. Bland-Altman analysis showed that compared with the cross-correlation method (bias: 0.36, SD: 0.32), the results of the proposed method were more consistent with a manual measurement-based gold standard (bias: -0.04, SD: 0.14). CONCLUSIONS: The proposed EVA system provides an automatic and reliable solution for quantitative assessment of hemodynamics in conjunctival microvascular images, and potentially can be applied to hypoglossal microcirculation images.

Efficient Subject-Independent Detection of Anterior Cruciate Ligament Deficiency Based on Marine Predator Algorithm and Support Vector Machine.

Anterior cruciate ligament (ACL) deficiency not only reduces knee stability, but also increases the risk of more disease and impairs daily life, thus requiring efficient detection of ACL deficiency. To build an efficient subject-independent ACL deficiency detection model, this study proposes a new method called SVM-MPA that fuses marine predator algorithm (MPA) and support vector machine (SVM) for simultaneous feature selection, hyperparameter optimization and classification. 35ACL-deficient (ACLD) and 35 ACL-intact (ACLI) participants were recruited to collect 6-degree-of-freedom knee kinematic data. Then, 216-dimensional multi-domain features covering time domain, frequency domain, time-frequency domain and nonlinearity were extracted. The error rate of SVM classification based on 5-fold cross-validation was used to construct the fitness of MPA, and MPA served to select features and optimize two hyperparameters for SVM. The majority voting strategy-based post-processing was introduced to convert the gait cycle-level to knee-level ACL deficiency detection. Comparing with 7 well-known meta-heuristic algorithms and running all 20 times, the best average gait cycle-level ACL deficiency detection performance (sensitivity: 96.78±0.4.84%, specificity: 99.43±5.70%, and accuracy: 98.48±1.70%) was obtained using the proposed method. With post-processing, this study improved the best (final) detection performance (sensitivity: 97.78±4.97%, specificity: 100±0.00%, and accuracy: 99.13±1.94%). These results demonstrate the feasibility and effectiveness of the proposed method and shows that an efficient subject-independent ACL deficiency detection model can be constructed using the proposed method, which makes it possible to provide a non-invasive, objective and accurate preoperative auxiliary detection method for diagnosing ACL deficiency clinically.

Retinal vascular geometry detection as a biomarker in diabetes mellitus.

OBJECTIVE: To compare the vessel geometry characteristics of color fundus photographs in normal control and diabetes mellitus (DM) patients and to find potential biomarkers for early diabetic retinopathy (DR) based on a neural network vessel segmentation system and automated vascular geometry parameter analysis software. METHODS: A total of 102 consecutive patients with type 2 DM (T2DM) and 132 healthy controls were recruited. All participants underwent general ophthalmic examinations, and retinal fundus photographs were taken with a digital fundus camera without mydriasis. Color fundus photographs were input into a dense-block generative adversarial network (D-GAN)-assisted retinal vascular segmentation system (http://www.gdcerc.cn:8081/#/login) to obtain binary images. These images were then analyzed by customized software (ocular microvascular analysis system V2.9.1) for automatic processing of vessel geometry parameters, including the monofractal dimension (Dbox), multifractal dimension (D0), vessel area ratio (R), max vessel diameter (dmax), average vessel diameter (dave), arc-chord ratio (A/C), and tortuosity (τn). Geometric differences between the healthy subjects and DM patients were analyzed. Then, regression analysis and receiver operating characteristic (ROC) curve analysis were performed to evaluate the diagnostic efficiency of the vascular geometry parameters. RESULTS: No significant differences were observed between the baseline characteristics of each group. DM patients had lower Dbox and D0 values (1.330 ± 0.041; 1.347 ± 0.038) than healthy subjects (1.343 ± 0.048, p < 0.05; 1.362 ± 0.042, p < 0.05) and showed increasing values of dmax, dave, A/C, and τn compared with normal controls, although only the differences in dave and τn between the groups were statistically significant. In the regression analysis, dave and τn showed a good correlation with diabetes (dave, OR 1.765, 95% CI 1.319-2.362, p < 0.001; τn, OR 9.323, 95% CI 1.492-58.262, p < 0.05). CONCLUSIONS: We demonstrated the relationship between retinal vascular geometry and the process in DM patients, showing that Dbox, D0, dave, and τn may be indicators of morphological changes in retinal vessels in DM patients and can be early biomarkers of DR.

Microvasculature Features of Vogt-Koyanagi-Harada Disease Revealed by Widefield Swept-Source Optical Coherence Tomography Angiography.

Background: Vogt-Koyanagi-Harada (VKH) disease is a multisystem autoimmune disorder which could induce bilateral panuveitis involving the posterior pole and peripheral fundus. Optical coherence tomography angiography (OCTA) provides several advantages over traditional fluorescence angiography for revealing pathological abnormalities of the retinal vasculature. Until recently, however, the OCTA field of view (FOV) was limited to 6 × 6 mm2 scans. Purpose: This study examined retinal vasculature and choriocapillaris abnormalities across multiple regions of the retina (15 × 9 mm2 wide field, macular, peripapillary regions) among acute and convalescent VKH patients using a novel widefield swept-source OCTA (WSS-OCTA) device and assessed correlations between imaging features and best-corrected visual acuity (BCVA). Methods: Twenty eyes of 13 VHK disease patients in the acute phase, 30 eyes of 17 patients in the convalescent phase, and 30 eyes of 15 healthy controls (HCs) were included in this study. Vascular length density (VLD) in superficial and deep vascular plexuses (SVP, DVP), vascular perfusion density (VPD) in SVP, DVP, and choriocapillaris (CC), and flow voids (FV) in CC were measured across multiple retinal regions via WSS-OCTA (PLEX Elite 9000, Carl Zeiss Meditec Inc., USA) using the 15 × 9 mm2 scan pattern centered on the fovea and quantified by ImageJ. Results: Compared to HCs, acute phase VKH patients exhibited significantly reduced SVP-VLD, SVP-VPD, and CC-VPD across multiple retinal regions (all p < 0.01). Notably, the FV area was more extensive in VKH patients, especially those in the acute phase (p < 0.01). These changes were reversed in the convalescent phase. Stepwise multiple linear regression analysis demonstrated that macular DVP-VLD and macular CC-VPD were the best predictive factors for BCVA in the acute and convalescent VKH groups. Conclusion: The wider field of SS-OCAT provides more comprehensive and detailed images of the microvasculature abnormalities characterizing VKH disease. The quantifiable and layer-specific information from OCTA allows for the identification of sensitive and specific imaging markers for prognosis and treatment guidance, highlighting WSS-OCTA as a promising modality for the clinical management of VKH disease.

A novel multiple subdivision-based algorithm for quantitative assessment of retinal vascular tortuosity.

Vascular tortuosity as an indicator of retinal vascular morphological changes can be quantitatively analyzed and used as a biomarker for the early diagnosis of relevant disease such as diabetes. While various methods have been proposed to evaluate retinal vascular tortuosity, the main obstacle limiting their clinical application is the poor consistency compared with the experts' evaluation. In this research, we proposed to apply a multiple subdivision-based algorithm for the vessel segment vascular tortuosity analysis combining with a learning curve function of vessel curvature inflection point number, emphasizing the human assessment nature focusing not only global but also on local vascular features. Our algorithm achieved high correlation coefficients of 0.931 for arteries and 0.925 for veins compared with clinical grading of extracted retinal vessels. For the prognostic performance against experts' prediction in retinal fundus images from diabetic patients, the area under the receiver operating characteristic curve reached 0.968, indicating a good consistency with experts' predication in full retinal vascular network evaluation.

An automated and multiparametric algorithm for objective analysis of meibography images.

BACKGROUND: Meibography is a non-contact imaging technique used by ophthalmologists and eye care practitioners to acquire information on the characteristics of meibomian glands. One of its most important applications is to assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD). As the artificial qualitative analysis of meibography images can lead to low repeatability and efficiency, automated and quantitative evaluation would greatly benefit the image analysis process. Moreover, since the morphology and function of meibomian glands varies at different stages of MGD, multiparametric analysis offering more comprehensive information could help in discovering subtle changes to glands during MGD progression. Therefore, an automated and multiparametric objective analysis of meibography images is urgently needed. METHODS: An algorithm was developed to perform multiparametric analysis of meibography images with fully automatic and repeatable segmentation based on image contrast enhancement and noise reduction. The full architecture can be divided into three steps: (I) segmentation of the tarsal conjunctiva area as the region of interest (ROI); (II) segmentation and identification of glands within the ROI; and (III) quantitative multiparametric analysis including a newly defined gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI). To evaluate the performance of this automated algorithm, the similarity index (k) and the segmentation error including the false-positive rate (rP ) and the false-negative rate (rN ) were calculated between the manually defined ground truth and the automatic segmentations of both the ROI and meibomian glands of 15 typical meibography images. RESULTS: The results of the performance evaluation between the manually defined ground truth and automatic segmentations were as follows: for ROI segmentation, the similarity index (k)=0.94±0.02, the false-positive rate (rP )=6.02%±2.41%, and the false-negative rate (rN )=6.43%±1.98%; for meibomian gland segmentation, the similarity index (k)=0.87±0.01, the false-positive rate (rP )=4.35%±1.50%, and the-false negative rate (rN )=18.61%±1.54%. The algorithm was successfully applied to process typical meibography images acquired from subjects of different meibomian gland health statuses, by providing the gland area ratio (GA), the gland length (L), gland width (D), gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI). CONCLUSIONS: A fully automated algorithm was developed which demonstrated high similarity with moderate segmentation errors for meibography image segmentation compared with the manual approach, offering multiple parameters to quantify the morphology and function of meibomian glands for the objective evaluation of meibography images.

Macular Vascular Geometry Changes With Sex and Age in Healthy Subjects: A Fundus Photography Study.

Purpose: To characterize the sex- and age-related alterations of the macular vascular geometry in a population of healthy eyes using fundus photography. Methods: A cross-sectional study was conducted with 610 eyes from 305 healthy subjects (136 men, 169 women) who underwent fundus photography examination and was divided into four age groups (G1 with age ≤ 25 years, G2 with age 26-35 years, G3 with age 36-45 years, and G4 with age ≥ 46 years). A self-developed automated retinal vasculature analysis system allowed segmentation and separate multiparametric quantification of the macular vascular network according to the Early Treatment Diabetic Retinopathy Study (ETDRS). Vessel fractal dimension (Df), vessel area rate (VAR), average vessel diameter (Dm), and vessel tortuosity (τn) were acquired and compared between sex and age groups. Results: There was no significant difference between the mean age of male and female subjects (32.706 ± 10.372 and 33.494 ± 10.620, respectively, p > 0.05) and the mean age of both sexes in each age group (p > 0.05). The Df, VAR, and Dm of the inner ring, the Df of the outer ring, and the Df and VAR of the whole macula were significantly greater in men than women (p < 0.001, p < 0.001, p < 0.05, respectively). There was no significant change of τn between males and females (p > 0.05). The Df, VAR, and Dm of the whole macula, the inner and outer rings associated negatively with age (p < 0.001), whereas the τn showed no significant association with age (p > 0.05). Comparison between age groups observed that Df started to decrease from G2 compared with G1 in the inner ring (p < 0.05) and Df, VAR, and Dm all decreased from G3 compared with the younger groups in the whole macula, inner and outer rings (p < 0.05). Conclusion: In the healthy subjects, macular vascular geometric parameters obtained from fundus photography showed that Df, VAR, and Dm are related to sex and age while τn is not. The baseline values of the macular vascular geometry were also acquired for both sexes and all age groups.

The inter-visit variability of retinal blood flow velocity measurements using retinal function imager (RFI).

BACKGROUND: To determine the inter-visit variability of retinal blood flow velocities (BFVs) using a retinal function imager (RFI) in healthy young subjects. METHODS: Twenty eyes of 20 healthy young subjects were enrolled. RFI imaging was performed to obtain the BFVs in retinal arterioles and venules in a field measuring 7.3 × 7.3 mm2 (setting: 35 degrees) centered on the fovea, and repeated measurements were obtained on two separate days. The inter-visit variability of BFVs was assessed by the concordance correlation coefficient (CCC) and coefficient of variance (CV). RESULTS: At the first visit, the mean BFV was 3.6 ± 0.8 mm/s and 3.0 ± 0.7 mm/s in arterioles and venules, respectively, which were not significantly different from those at the second visit (the BFV of arterioles was 3.5 ± 0.8 mm/s, and the BFV of venules was 3.0 ± 0.7 mm/s, P > 0.05, respectively). The CCC was 0.72 in the BFVs of arterioles and 0.67 in venules, and the CV was 10.8% in the BFVs of arterioles and 11.0% in venules. CONCLUSION: The inter-visit variability using the retinal function imager (RFI) with a large field of view appeared to be good and comparable to previously reported intra-visit and inter-eye variability.

[Development of a visualization system for minimally invasive surgical abortion].

This article introduces the principle, structure and components of a visualization system for carrying out minimally invasive surgical abortion. Without altering the current surgical approach or increasing the surgical difficulty, the surgical system integrated a mini-CMOS image sensor and LED light and a visual device to allow fixed-point removal of the fetus or embryo in the minimally invasive surgery.