Explore the vast range of titles available.

Aims/hypothesisCorneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either time-consuming manual annotation or a less-sensitive automated image analysis approach. We aimed to develop and validate an artificial intelligence-based, deep learning algorithm for the quantification of nerve fibre properties relevant to the diagnosis of diabetic neuropathy and to compare it with a validated automated analysis program, ACCMetrics.MethodsOur deep learning algorithm, which employs a convolutional neural network with data augmentation, was developed for the automated quantification of the corneal sub-basal nerve plexus for the diagnosis of diabetic neuropathy. The algorithm was trained using a high-end graphics processor unit on 1698 corneal confocal microscopy images; for external validation, it was further tested on 2137 images. The algorithm was developed to identify total nerve fibre length, branch points, tail points, number and length of nerve segments, and fractal numbers. Sensitivity analyses were undertaken to determine the AUC for ACCMetrics and our algorithm for the diagnosis of diabetic neuropathy.ResultsThe intraclass correlation coefficients for our algorithm were superior to those for ACCMetrics for total corneal nerve fibre length (0.933 vs 0.825), mean length per segment (0.656 vs 0.325), number of branch points (0.891 vs 0.570), number of tail points (0.623 vs 0.257), number of nerve segments (0.878 vs 0.504) and fractals (0.927 vs 0.758). In addition, our proposed algorithm achieved an AUC of 0.83, specificity of 0.87 and sensitivity of 0.68 for the classification of participants without (n = 90) and with (n = 132) neuropathy (defined by the Toronto criteria).Conclusions/interpretationThese results demonstrated that our deep learning algorithm provides rapid and excellent localisation performance for the quantification of corneal nerve biomarkers. This model has potential for adoption into clinical screening programmes for diabetic neuropathy.Data availabilityThe publicly shared cornea nerve dataset (dataset 1) is available at http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm and http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Data%20Set.htm.

We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named by us view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class. To validate its effectiveness, we design a multi-view CNN instantiating it for salient view selection and interest point detection of 3D objects, which quintessentially cannot be handled by supervised learning due to the difficulty of collecting sufficient and consistent training data. Our unsupervised multi-view CNN, namely UMVCNN, branches off two channels which encode the knowledge within each 2D view and the 3D object respectively and also exploits both intra-view and inter-view knowledge of the object. It ends with a new loss layer which formulates the view-object consistency by impelling the two channels to generate consistent classification outcomes. The UMVCNN is then integrated with a global distinction adjustment scheme to incorporate global cues into salient view selection. We evaluate our method for salient view section both qualitatively and quantitatively, demonstrating its superiority over several state-of-the-art methods. In addition, we showcase that our method can be used to select salient views of 3D scenes containing multiple objects. We also develop a method based on the UMVCNN for 3D interest point detection and conduct comparative evaluations on a publicly available benchmark, which shows that the UMVCNN is amenable to different 3D shape understanding tasks.

Yttria stabilized zirconia (YSZ) thin wall components were fabricated using laser engineered net shaping (LENS) technique. It was found that after LENS processing, the monoclinic (m) phase in as-received YSZ powders transformed to tetragonal (t) and cubic (c) phases with the lenticular shaped t-ZrO 2 embedded in the c-ZrO 2 matrix. The relative density of the parts reached up to 98.7%. Our investigation showed that micro cracks within the wall structure were reduced by judiciously choosing laser power parameter. The fabricated parts have surface roughness values that ranged from 20 to 40 μm. The maximum hardness and elastic modulus achieved from the LENSed YSZ parts were 19.8 GPa and 236.1 GPa, respectively. We also demonstrated that dark brown color of the LENSed parts could be removed via heat treatment.

Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer.

In ophthalmologic practice, retinal images are routinely obtained to diagnose and monitor primary eye diseases and systemic conditions affecting the eye, such as diabetic retinopathy. Recent studies have shown that biomarkers on retinal images, for example, retinal blood vessel density or tortuosity, are associated with cardiac function and may identify patients at risk of coronary artery disease. In this work we investigate the use of retinal images, alongside relevant patient metadata, to estimate left ventricular mass and left ventricular end-diastolic volume, and subsequently, predict incident myocardial infarction. We trained a multichannel variational autoencoder and a deep regressor model to estimate left ventricular mass (4.4 (–32.30, 41.1) g) and left ventricular end-diastolic volume (3.02 (–53.45, 59.49) ml) and predict risk of myocardial infarction (AUC = 0.80 ± 0.02, sensitivity = 0.74 ± 0.02, specificity = 0.71 ± 0.03) using just the retinal images and demographic data. Our results indicate that one could identify patients at high risk of future myocardial infarction from retinal imaging available in every optician and eye clinic. Routine eye clinic imaging could help screen patients with cardiovascular risk as studies indicate strong associations between biomarkers in the retina and the heart. This potential is supported by a multimodal study, employing a deep learning model, that can infer cardiac functional indices based on retinal images and demographic data.

Contrarily, patients with ICAS had a higher burden of ischemic symptoms. [...]ICAS patients had reduced arteriolar density (1.79 ± 0.17% vs. 1.88 ± 0.19%, t = 6.26, P <0.001) but higher superficial vascular plexus (SVC) density (16.94 ± 0.82% vs. 16.59 ± 0.89%, t = –3.14, P = 0.002) compared to ECAS. A similar correlation was seen in the ECAS patients. Since hypertension reduces microvascular density (especially arterioles), this study suggests that this inverse correlation may be a compensatory mechanism. Given that retinal arterioles reflect intracranial arteries, our data provide further evidence to support the concept that intracranial atherosclerosis is associated with more severe microvascular dysfunction and clinical microvascular diseases in the brain. [...]we suggest that retinal imaging may be an approach to assess small vessel impairment in cerebral atherosclerosis and may have the potential to elucidate underlying microvascular pathologies between patients with different plaque locations. The effect of interventions for atherosclerotic stenosis, such as stenting and endarterectomy, and therapy for vascular risk factors on retinal microvasculature are proposed to be investigated to validate the relationship between retinal microvasculature and cerebral artery stenosis. [...]this is a single-center study and patients were only included from China, which limits the generalization of the findings.

Objective: To develop and validate an artificial intelligence (AI) system for automated lens opacities classification system III (LOCS III)-based grading of all three major cataract subtypes using anterior segment optical coherence tomography (AS-OCT). Methods: This is a single-center cross-sectional study. AS-OCT images were collected and manually graded by ophthalmologists according to LOCS III. The dataset was randomly split into training, validation, and test sets. We propose a novel multi-granularity mask-guided network (MMNet) that jointly performs lens substructure segmentation and severity grading. The model’s performance was assessed on an independent test set for automatic grading of cortical cataract (CC), nuclear cataract (NC), and posterior subcapsular cataract (PSC) and the grading performance of the proposed method against ophthalmologists was also evaluated. The model’s interpretability was assessed via attention heatmaps and feature visualization. Results: The proposed MMNet exhibited high agreement with ground truth conducted through gold standard. The proportions of predictions with an absolute error < 1.0 for three subtypes range from 83.02% to 89.94%. The model’s grading accuracy for cataract subtypes was between 82.20 ± 1.41% and 89.76 ± 1.31% among the three subtypes, the Area Under the Curve (AUC) was between 0.954 (95% CI, 0.952–0.969; p < 0.001) and 0.973 (95% CI, 0.964–0.985; p < 0.001). The MMNet shows a satisfactory mean absolute error (MAE) of 0.14 ± 0.35 in CC, 0.10 ± 0.30 in NC, and 0.17 ± 0.38 in PSC grading. It also achieved a fast grading speed of 0.0178 s/image against manual grading. Conclusions: The proposed AI model presented advanced performance on AS-OCT images in automated LOCS III-based cataract grading for CC and NC, and also showed feasibility in PSC assessment.

Coronary artery disease (CAD) is a leading cause of cardiovascular mortality worldwide, and its early diagnosis is essential for prevention and treatment. Emerging evidence from ocular imaging suggests that structural and functional alterations in the retinal vasculature may mirror systemic vascular changes, offering a promising avenue for the early identification of cardiovascular conditions such as CAD. Among these techniques, OCTA stands out as a non-invasive, high-resolution modality capable of capturing detailed microvascular architecture and quantifying retinal blood flow dynamics. In this study, we analyzed OCTA images from 747 participants including 332 patients with CAD and 415 controls to extract retinal microvascular parameters and evaluate their associations with disease status. The results revealed that patients with CAD exhibited significantly lower retinal vessel density, vessel length density, tortuosity, and vascular bifurcation complexity on OCTA compared to controls, particularly in the left eye. This suggests possible lateral asymmetry in microvascular responses associated with CAD. Overall, we highlight the potential of OCTA-derived retinal biomarkers in supporting the early diagnosis of CAD, providing a standard tool for future clinical services and research. Biomarkers in retinal OCTA images can provide useful information for clinical decision making and diagnosis of CAD.

Background Coronary artery disease (CAD), remains a leading global cause of mortality. Invasive coronary angiography (CAG), the diagnostic gold standard, is unsuitable for large-scale screening due to its cost and procedural risks. Retinal microvascular changes, reflecting systemic vascular pathology via the retino-coronary axis, offer a promising noninvasive alternative. Optical coherence tomography angiography (OCTA) enables high-resolution retinal imaging but lacks robust computational frameworks for accurate CAD prediction. Objective To develop and validate an artificial intelligence (AI)-driven CAD prediction model by integrating OCTA-derived retinal features with electronic health record (EHR) data. Methods This retrospective cohort study included 542 patients undergoing both OCTA and invasive coronary angiography between July 2022 and October 2023. A Transformer-based multi-scale style learning algorithm was developed to extract features from 3 × 3 mm 2 macular OCTA images, achieving joint segmentation of the foveal avascular zone (FAZ) and retinal vessel (RV) by leveraging task-specific characteristics, while simultaneously quantifying 98 retinal vascular imaging biomarkers. Relevant predictors were selected via LASSO regression and modeled using restricted cubic splines. An XGBoost classifier was trained on the combined OCTA-EHR feature set and compare it with four baseline deep learning network models. Model performance was evaluated using AUC, sensitivity, specificity, calibration, and decision curve analysis. Results The multimodal model demonstrated superior discriminative power (AUC = 0.850; sensitivity = 0.806; specificity = 0.667), significantly outperforming OCTA-only models (AUC = 0.512; sensitivity = 0.581; specificity = 0.417). Key predictors included Glycosylated Hemoglobin (Ghb), Hypersensitive C-Reactive Protein (hs-CRP), and OCTA-derived vascular density metrics (e.g., vessel length density in temporal-internal macular sectors). Conclusion Integration of retinal OCTA biomarkers with EHR data enables accurate, noninvasive CAD prediction. This approach validates the retina-coronary axis and establishes a scalable screening paradigm for subclinical atherosclerosis.

Neuromyelitis optica spectrum disorders (NMOSD) are autoimmune central nervous system diseases characterized by the immune system's abnormal attack on glial cells and neurons. Optic neuritis (ON) is one of the indicators of NMOSD, often starting unilaterally and potentially affecting both eyes later in the disease progression, leading to visual impairment. Optical coherence tomography angiography (OCTA) has the potential to aid in the early diagnosis of NMOSD by examining ophthalmic imaging and may offer a window for disease prevention. In this study, we collected OCTA images from 22 NMOSD patients (44 images) and 25 healthy individuals (50 images) to investigate retinal microvascular changes in NMOSD. We employed effective retinal microvascular segmentation and foveal avascular zone (FAZ) segmentation techniques to extract key OCTA structures for biomarker analysis. A total of 12 microvascular features were extracted using specifically designed methods based on the segmentation results. The OCTA images of NMOSD patients were classified into two groups: optic neuritis (ON) and non-optic neuritis (non-ON). Each group was compared separately with a healthy control (HC) group. Statistical analysis revealed that the non-ON group displayed shape changes in the deep layer of the retina, specifically in the FAZ. However, there were no significant microvascular differences between the non-ON group and the HC group. In contrast, the ON group exhibited microvascular degeneration in both superficial and deep retinal layers. Sub-regional analysis revealed that pathological variations predominantly occurred on the side affected by ON, particularly within the internal ring near the FAZ. The findings of this study highlight the potential of OCTA in evaluating retinal microvascular changes associated with NMOSD. The shape alterations observed in the FAZ of the non-ON group suggest localized vascular abnormalities. In the ON group, microvascular degeneration in both superficial and deep retinal layers indicates more extensive vascular damage. Sub-regional analysis further emphasizes the impact of optic neuritis on pathological variations, particularly near the FAZ's internal ring. This study provides insights into the retinal microvascular changes associated with NMOSD using OCTA imaging. The identified biomarkers and observed alterations may contribute to the early diagnosis and monitoring of NMOSD, potentially offering a time window for intervention and prevention of disease progression.