Quantitative analysis of the eyelid curvature in patients with blepharoptosis.

BACKGROUND: The aim of this study was to evaluate the ability of two novel eyelid curvature measurements to distinguish between normal eyes and different severities of blepharoptosis. METHODS: A comparative cross-sectional analysis of upper eyelid curvature was performed for different severities of patients with unilateral blepharoptosis (congenital and aponeurotic) and normal controls. Mean upper lid contour index (ULCI) and area circularity index (ACI) were calculated for each group by dividing the intercanthal distance by upper eyelid margin length (ULCI) and dividing the interpalpebral area by the area of a circle enclosing the eye (ACI). The ratio of each index for the study and fellow normal eye of each patient was also calculated and compared between groups. RESULTS: A total of 106 eyes including 30 eyes in the control group and 25, 27, and 24 eyes in the mild, moderate, and severe ptosis groups were enrolled in the study. ULCI and ACI showed a statistically significant difference between the groups (p < 0.001, p < 0.001). The inter-eye ratio (ULCI-ratio and ACI-ratio) of indices was also significantly different between groups (p = 0.002, p < 0.001). Pairwise comparisons revealed that ACI and ACI-ratio were significantly different between all pairs of study groups. CONCLUSION: The results of our study showed that ACI based on area measurements may distinguish blepharoptosis patients from normal controls and from each other. Including the data from the fellow normal eyes in the form of ratio indices may improve the differentiating power. These results can be useful in designing the optimal eyelid curvature measurements.

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.

Application of ImageJ in Optical Coherence Tomography Angiography (OCT-A): A Literature Review.

Background: This study aimed to review the literature on the application of ImageJ in optical coherence tomography angiography (OCT-A) images. Methods: A general search was performed in PubMed, Google Scholar, and Scopus databases. The authors evaluated each of the selected articles in order to assess the implementation of ImageJ in OCT-A images. Results: ImageJ can aid in reducing artifacts, enhancing image quality to increase the accuracy of the process and analysis, processing and analyzing images, generating comparable parameters such as the parameters that assess perfusion of the layers (vessel density (VD), skeletonized density (SD), and vessel length density (VLD)) and the parameters that evaluate the structure of the layers (fractal dimension (FD), vessel density index (VDI), and lacunarity (LAC)), and the foveal avascular zone (FAZ) that are used widely in the retinal and choroidal studies), and establishing diagnostic criteria. It can help to save time when the dataset is huge with numerous plugins and options for image processing and analysis with reliable results. Diverse studies implemented distinct binarization and thresholding techniques, resulting in disparate outcomes and incomparable parameters. Uniformity in methodology is required to acquire comparable data from studies employing diverse processing and analysis techniques that yield varied outcomes. Conclusion: Researchers and professionals might benefit from using ImageJ because of how quickly and correctly it processes and analyzes images. It is highly adaptable and potent software, allowing users to evaluate images in a variety of ways. There exists a diverse range of methodologies for analyzing OCTA images through the utilization of ImageJ. However, it is imperative to establish a standardized strategy to ensure the reliability and consistency of the method for research purposes.

Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography.

PRCIS: We developed a deep learning-based classifier that can discriminate primary angle closure suspects (PACS), primary angle closure (PAC)/primary angle closure glaucoma (PACG), and also control eyes with open angle with acceptable accuracy. PURPOSE: To develop a deep learning-based classifier for differentiating subtypes of primary angle closure disease, including PACS and PAC/PACG, and also normal control eyes. MATERIALS AND METHODS: Anterior segment optical coherence tomography images were used for analysis with 5 different networks including MnasNet, MobileNet, ResNet18, ResNet50, and EfficientNet. The data set was split with randomization performed at the patient level into a training plus validation set (85%), and a test data set (15%). Then 4-fold cross-validation was used to train the model. In each mentioned architecture, the networks were trained with original and cropped images. Also, the analyses were carried out for single images and images grouped on the patient level (case-based). Then majority voting was applied to the determination of the final prediction. RESULTS: A total of 1616 images of normal eyes (87 eyes), 1055 images of PACS (66 eyes), and 1076 images of PAC/PACG (66 eyes) eyes were included in the analysis. The mean ± SD age was 51.76 ± 15.15 years and 48.3% were males. MobileNet had the best performance in the model, in which both original and cropped images were used. The accuracy of MobileNet for detecting normal, PACS, and PAC/PACG eyes was 0.99 ± 0.00, 0.77 ± 0.02, and 0.77 ± 0.03, respectively. By running MobileNet in a case-based classification approach, the accuracy improved and reached 0.95 ± 0.03, 0.83 ± 0.06, and 0.81 ± 0.05, respectively. For detecting the open angle, PACS, and PAC/PACG, the MobileNet classifier achieved an area under the curve of 1, 0.906, and 0.872, respectively, on the test data set. CONCLUSION: The MobileNet-based classifier can detect normal, PACS, and PAC/PACG eyes with acceptable accuracy based on anterior segment optical coherence tomography images.

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.

Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images.

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. Objective: This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. Material and Methods: This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. Results: The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Conclusion: Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications.

Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors.

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of pancreatic ductal adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a multi-objective loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fine-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.

Changes in Serum Alkaline Phosphatase, Calcium, and Parathyroid Hormone with Different Doses of Iodine Therapy:.

Background: Despite the benefits of radioactive iodine (RAI) therapy as an adjunctive treatment for thyroid cancer, it can be associated with several side effects. The main purpose of this study was to determine the changes in serum alkaline phosphatase (ALP), calcium (Ca), and parathyroid hormone (PTH) at different doses of RAI therapy among patients who were referred to the nuclear medicine department of Namazi Hospital, Shiraz. Materials and Methods: This cross-sectional study was conducted on 60 patients with papillary thyroid cancer who underwent RAI therapy at different doses of 100, 150, and 200 mCi. The ALP, Ca, and PTH levels of patients were measured before and 60 days after RAI therapy. Results: Our study revealed that RAI therapy at all doses significantly increased ALP level in comparison with baseline amounts (P≤0.05). However, changes in PTH and Ca levels were not significant among patients who received different doses of RAI (P˃0.05). Conclusion: RAI therapy could affect important hormones and enzymes such as ALP and PTH. This issue can be considered in diagnostic and therapeutic prescriptions of RAI for the treatment of thyroid cancer.[GMJ.2022;11:e2397].

Evaluation of radiation maculopathy after treatment of choroidal melanoma with ruthenium-106 using optical coherence tomography angiography.

BACKGROUND: To assess the impact of brachytherapy on macular microvasculature utilizing optical coherence tomography angiography (OCTA) in treated choroidal melanoma. METHODS: In this retrospective observational case series, we reviewed the recorded data of the patients with unilateral extramacular choroidal melanoma treated with ruthenium - 106 (106Ru) plaque radiotherapy with a follow-up period of more than 6 months. Automatically measured OCTA retinal parameters were analysed after image processing. RESULTS: Thirty-one eyes of 31 patients with the mean age of 51.1 years were recruited. Six eyes had no radiation maculopathy (RM). From 25 eyes with RM, nine eyes (36%) revealed a burnout macular microvasculature with imperceptible vascular details. Twenty-one non-irradiated fellow eyes from the enrolled patients were considered as the control group. Foveal and optic disc radiation dose had the highest value to predict the burnout pattern (ROC, AUC: 0.763, 0.727). Superficial and deep foveal avascular zone (FAZ) were larger in irradiated eyes in comparison to non-irradiated fellow eyes (1629 µm2 vs. 428 µm2, P = 0.005; 1837 µm2 vs 268 µm2, P = 0.021; respectively). Foveal and parafoveal vascular area density (VAD) and vascular skeleton density (VSD) in both superficial and deep capillary plexus (SCP and DCP) were decreased in all irradiated eyes in comparison with non-irradiated fellow eyes (P < 0.001). Compared with non-irradiated fellow eyes, irradiated eyes without RM had significantly lower VAD and VSD at foveal and parafoveal DCP (all P < 0.02). However, these differences at SCP were not statistically significant. CONCLUSION: The OCTA is a valuable tool for evaluating RM. Initial subclinical microvascular insult after 106Ru brachytherapy is more likely to occur in DCP. The deep FAZ area was identified as a more critical biomarker of BCVA than superficial FAZ in these patients.

Livelayer: a semi-automatic software program for segmentation of layers and diabetic macular edema in optical coherence tomography images.

Given the capacity of Optical Coherence Tomography (OCT) imaging to display structural changes in a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called "Livelayer" which is designed for straightforward segmentation of retinal layers and fluids. This method is chiefly based on Dijkstra's Shortest Path First (SPF) algorithm and the Live-wire function together with some preprocessing operations on the to-be-segmented images. The software is indeed suitable for obtaining detailed segmentation of layers, exact localization of clear or unclear fluid objects and the ground truth, demanding far less endeavor in comparison to a common manual segmentation method. It is also valuable as a tool for calculating the irregularity index in deformed OCT images. The amount of time (seconds) that Livelayer required for segmentation of Inner Limiting Membrane, Inner Plexiform Layer-Inner Nuclear Layer, Outer Plexiform Layer-Outer Nuclear Layer was much less than that for the manual segmentation, 5 s for the ILM (minimum) and 15.57 s for the OPL-ONL (maximum). The unsigned errors (pixels) between the semi-automatically labeled and gold standard data was on average 2.7, 1.9, 2.1 for ILM, IPL-INL, OPL-ONL, respectively. The Bland-Altman plots indicated perfect concordance between the Livelayer and the manual algorithm and that they could be used interchangeably. The repeatability error was around one pixel for the OPL-ONL and < 1 for the other two. The unsigned errors between the Livelayer and the manual algorithm was 1.33 for ILM and 1.53 for Nerve Fiber Layer-Ganglion Cell Layer in peripapillary B-Scans. The Dice scores for comparing the two algorithms and for obtaining the repeatability on segmentation of fluid objects were at acceptable levels.

Automated measurement of iris surface smoothness using anterior segment optical coherence tomography.

Fuchs uveitis (FU) is a chronic and often unilateral ocular inflammation and characteristic iris atrophic changes, other than heterochromia, are common in FU and are key to the correct diagnosis in many cases. With the advent of anterior segment optical coherence tomography (AS-OCT), some investigators attempted to quantitatively study these atrophic changes; mostly by introducing various methods to measure iris thickness in AS-OCT images. We aimed to present an automated method in an observational case series to measure the smoothness index (SI) of the iris surface in AS-OCT images. The ratio of the length of the straight line connecting the most peripheral and central points of the anterior iris border (in nasal and temporal sides) to the actual length of this border on AS-OCT images, was defined as SI. In a uveitis referral center, twenty-two eyes of 11 patients with unilateral Fuchs uveitis (FU) (7 female) and 22 eyes of 11 healthy control subjects underwent AS-OCT imaging. Image J and a newly developed MATLAB algorithm were used for manual and automated SI measurements, respectively. Agreement between manual and automated measurements was evaluated with Bland-Altman analysis and interclass correlation coefficient. The inter-eye difference of SI was compared between the FU group and the control group. Automated mean overall SI was 0.868 ± 0.037 and 0.840 ± 0.039 in FU and healthy fellow eyes, respectively (estimated mean difference = - 0.028, 95% CI [- 0.038, - 0.018], p < 0.001). Bland- Altman plots showed good agreement between two methods in both healthy and FU eyes. The interclass correlation coefficient between the manual and automated measurements in the FU and healthy fellow eyes was 0.958 and 0.964, respectively. The inter-eye difference of overall SI was 0.029 ± 0.015 and 0.012 ± 0.008 in FU group and control group, respectively (p = 0.01). We concluded that the automated algorithm can rapidly and conveniently measure SI with results comparable to the manual method.

Psychometrics properties of the Iranian version of fertility quality of life tool: A cross- sectional study.

BACKGROUND: Clinical measurement of quality of life (QoL) for assessing reproductive problems should be considered as a standard investigation at the initial and continuing medical consultations with infertile people. OBJECTIVE: The purpose of this study was comprehensive testing the psychometric properties of the Iranian version of fertility quality of life (FertiQoL). MATERIALS AND METHODS: This cross-sectional study was conducted on300 women referred to infertility clinic. After linguistic validation, a semi-structured interview was conducted to assess face validity. Consequently exploratory factor analysis was performed to indicate the scale constructs. Discriminate validity was assessed using the known groups comparison. Convergent validity was evaluated by assessing the correlation between similar content on the 12-Item Short Form Health Survey (SF12), Hospital Anxiety and Depression Scale and FertiQol. In addition, reliability analysis was carried out with internal consistency. RESULTS: The reliability of the Iranian version of the FertiQoL was satisfactory in all dimensions (0.77-0.83). Six factors (emotional, mind/body, relational, social, environmental, and tolerability) were extracted from the results of exploratory factor analysis. Discrimination validity showed that FertiQoL can differentiate between female patients with differing duration of infertility and number of children. Moreover, the results of convergent validity showed a favorable correlation between the related dimensions of SF12 (0.43-0.68), Hospital Anxiety and Depression Scale (0.47-0.52) and FertiQoL. CONCLUSION: The Iranian version of FertiQoL is valid and reliable for assessing infertility problems and the effects of treatment on QoL of infertile patients referred for diagnosis and treatment at infertility clinic.