Superficial ocular vascular changes after orbital decompression in patients with thyroid ophthalmopathy measured by anterior segment OCT angiography; an observational study.

Thyroid eye disease (TED) is a common ophthalmologic manifestation of thyroid dysfunction. Despite various imaging techniques available, there hasn't been a widely adopted method for assessing the anterior segment vasculature in TED patients. Our study aimed to evaluate alterations in ocular surface circulation following orbital decompression surgery in TED patients and investigate factors influencing these changes. Using anterior segment optical coherence tomography-angiography (AS-OCTA), we measured ocular surface vascularity features, including vessel density (VD), vessel diameter index (VDI), and vessel length density (VLD), both before and after decompression surgery, alongside standard ophthalmic examinations. Our AS-OCTA analysis revealed a significant decrease in most of the temporal vasculature measurements six weeks post-surgery (p < 0.05). However, differences in the nasal region were not statistically significant. These findings indicate notable changes in ocular surface circulation following orbital decompression in TED patients, which may have implications for intraocular pressure (IOP) control and ocular surface symptoms management. AS-OCTA holds promise as a tool for evaluating the effectiveness of decompression surgery and assessing the need for further interventions.

Sector area index: a novel supporting marker for blepharoptosis screening and grading.

PURPOSE: To introduce a new supporting marker for discriminating different grades of ptosis called Sector Area Index (SAI) and a semi-automated technique to calculate it. METHODS: In this cross-sectional comparative case series, a circle enclosing the intercanthal distance was automatically drawn after choosing two points as the medial and lateral canthus and manually selecting the palpebral fissure region. Finally, 15-degree apart sectors are applied to the enclosed circle. SAI was measured automatically by dividing the area of each 15-degree sector marked with the upper eyelid contour by the total area of the sector marked with the edge of the surrounding circle. SAI values and inter-eye SAI differences were compared between patients with different grades of ptosis as well as normal patients. RESULTS: In the current study, 106 eyes were recruited (30, 25, 27, and 24 in the control, mild, moderate, and severe ptosis groups, respectively). Mean values of SAI in all sectors showed a decreasing trend from normal individuals toward patients with severe ptosis. The mean difference values of SAI between study eyes and fellow eyes in all four groups of patients showed a statistically significant difference (p < 0.05). In a pairwise comparison between groups, mean values of SAI in all nasal sectors from 15° to 60° showed a statistically significant difference between all groups (p < 0.05). CONCLUSION: The mean difference of SAI between study eyes and fellow eyes, including eyelid curvature, especially in 15°-60° and 120°-165° sectors, can demonstrate differentiating performance for detecting and discriminating varying grades of ptosis.

Automated assessment of the smoothness of retinal layers in optical coherence tomography images using a machine learning algorithm.

Quantifying the smoothness of different layers of the retina can potentially be an important and practical biomarker in various pathologic conditions like diabetic retinopathy. The purpose of this study is to develop an automated machine learning algorithm which uses support vector regression method with wavelet kernel and automatically segments two hyperreflective retinal layers (inner plexiform layer (IPL) and outer plexiform layer (OPL)) in 50 optical coherence tomography (OCT) slabs and calculates the smoothness index (SI). The Bland-Altman plots, mean absolute error, root mean square error and signed error calculations revealed a modest discrepancy between the manual approach, used as the ground truth, and the corresponding automated segmentation of IPL/ OPL, as well as SI measurements in OCT slabs. It was concluded that the constructed algorithm may be employed as a reliable, rapid and convenient approach for segmenting IPL/OPL and calculating SI in the appropriate layers.

A digital viscoelastic liver phantom for investigation of elastographic measurements.

To develop elastography imaging technologies and implement image reconstruction algorithms, testing is done with phantoms. Although the validation step is usually taken using real data and physical phantoms, their geometry as well as composition, biomechanical parameters, and details of applying stress cannot be modified readily. Such considerations have gained increasing importance with the growth of elastography techniques as one of the non-invasive medical imaging modalities, which can map the elastic properties and stiffness of soft tissues. In this article, we develop a digital viscoelastic phantom using computed tomography (CT) imaging data and several application software tools based on illustrations of normal liver anatomy so as to investigate the biomechanics of elastography via finite element modeling (FEM). Here we discuss how to create this phantom step by step, demonstrate typical shear wave elastography (SWE) experiments of applying transient stress to the liver model, and calculate quantitative measurements. In particular, shear wave velocities are investigated through a parametric study designed based on tissue stiffness and distance from the applied stress. According to the results of FEM analysis, low errors were obtained for shear wave velocity estimation for both mechanical stress (~2-5%) and acoustic radiation force (~3-7%). Results show that our model is a powerful framework and benchmark for simulating and implementing different algorithms in shear wave elastography, which can serve as a guide for upcoming researches and assist scientists to optimize their subsequent experiments in terms of design.