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PurposeTo compare rates of retinal nerve fiber layer change over time in healthy, eyes with nonprogressing glaucoma and eyes with progressing glaucoma using single wide-field (SWF) and optic nerve head (ONH) cube scan optical coherence tomography (OCT) images.MethodsForty-five eyes of 25 healthy individuals and 263 eyes of 161 glaucoma patients from the Diagnostic Innovations in Glaucoma Study were included. All eyes underwent 24-2 visual field testing and OCT (Spectralis SD-OCT) ONH and macular imaging. SWF images (up to 43° × 28°) were created by stitching together ONH cube scans centered on the optic disc and macular cube scans centered on the fovea. Visual field progression was defined as guided progression analysis likely progression and/or a significant (P < 0.01) mean deviation slope of less than -1.0 dB/year. Mixed effects models were used to compare rates of change. Highly myopic eyes were included.ResultsThirty glaucomatous eyes were classified as progressing. In eyes with glaucoma, mean global rate of change was -1.22 µm/year (P < 0.001) using SWF images and -0.83 µm/year (P = 0.003) using ONH cube scans. Rate of change was significantly greater in eyes with progressing glaucoma compared with eyes with nonprogressing glaucoma (-1.51 µm/year vs. -1.24 µm/year; P = 0.002) using SWF images and was similar using ONH cube scans (P = 0.27).ConclusionsIn this cohort that includes eyes with and without high axial myopia, the mean rate of retinal nerve fiber layer thinning measured using SWF images was faster in eyes with progressing glaucoma than in eyes with nonprogressing glaucoma. Wide-field OCT images including the ONH and macula can be effective for monitoring glaucomatous progression in patients with and without high myopia.

PurposeTo develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations.MethodsImage quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC).ResultsThe DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88).ConclusionsThe DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model.Translational relevanceIncorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.

A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries at varying lengths of time prior to surgical intervention. The highest performing predictions achieved an AUC of 0.93, 0.92, and 0.93 in predicting surgical intervention at 1 year, 2 years, and 3 years, respectively. The models were also able to achieve high sensitivity (0.89, 0.77, 0.86 at 1, 2, and 3 years, respectively) and specificity (0.85, 0.90, and 0.91 at 1, 2, and 3 years, respectively) at an 0.80 level of precision. The multi-modal models trained on a combination of data types predicted surgical interventions with high accuracy up to three years prior to surgery and could provide an important tool to predict the need for glaucoma intervention.

Rehabilitation devices such as actuated exoskeletons can provide mobility assistance for patients suffering from paralysis or muscle weakness. In order to improve the well-being of patients, the control design of exoskeletons is of paramount importance and highest priority. In this paper, we present a sliding reinforcement learning (RL) method control for an upper-limb exoskeleton, enabling it to learn following a desired trajectory in the Cartesian space. The deep deterministic policy gradient (DDPG) using an actor-critic architecture is employed to continuously adjust the non-singular terminal sliding mode control (NSTSMC) control inputs, based on previous experiences. The designed actor network learns the policy and the critic evaluates the quality of the actions chosen by the actor. The robustness of the proposed approach is studied when the system is subjected to random disturbances. The simulation results demonstrate that the proposed approach based on the RL method effectively fulfills exoskeleton tracking tasks. Moreover, a comparative analysis with the standard NSTSMC, computed torque (CT), and RL-based CT shows the superiority of the proposed approach in terms of position tracking error. These findings are further confirmed by various performance evaluation metrics.

PurposeTo compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG) and identify the salient areas of the photographs most important for each model's decision-making process.DesignEvaluation of a diagnostic technology.Subjects participants and controlsOverall 66 715 photographs from 1636 OHTS participants and an additional 5 external datasets of 16 137 photographs of healthy and glaucoma eyes.MethodsData-efficient image Transformer models were trained to detect 5 ground-truth OHTS POAG classifications: OHTS end point committee POAG determinations because of disc changes (model 1), visual field (VF) changes (model 2), or either disc or VF changes (model 3) and Reading Center determinations based on disc (model 4) and VFs (model 5). The best-performing DeiT models were compared with ResNet-50 models on OHTS and 5 external datasets.Main outcome measuresDiagnostic performance was compared using areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities. The explainability of the DeiT and ResNet-50 models was compared by evaluating the attention maps derived directly from DeiT to 3 gradient-weighted class activation map strategies.ResultsCompared with our best-performing ResNet-50 models, the DeiT models demonstrated similar performance on the OHTS test sets for all 5 ground-truth POAG labels; AUROC ranged from 0.82 (model 5) to 0.91 (model 1). Data-efficient image Transformer AUROC was consistently higher than ResNet-50 on the 5 external datasets. For example, AUROC for the main OHTS end point (model 3) was between 0.08 and 0.20 higher in the DeiT than ResNet-50 models. The saliency maps from the DeiT highlight localized areas of the neuroretinal rim, suggesting important rim features for classification. The same maps in the ResNet-50 models show a more diffuse, generalized distribution around the optic disc.ConclusionsVision Transformers have the potential to improve generalizability and explainability in deep learning models, detecting eye disease and possibly other medical conditions that rely on imaging for clinical diagnosis and management.

Abstract Background we aim to discuss the origin and the differences of the phenotypic features and the management care of rare form of disorder of sex development due to Mosaic monosomy X and Y chromosome materiel. Methods We report our experience with patients harboring mosaic monosomy X and Y chromosome material diagnosed by blood cells karyotypes and cared for in our department from 2005 to 2022. Results We have included five infants in our study. The current average age was 8 years. In four cases, the diagnosis was still after born and it was at the age of 15 years in one case. Physical examination revealed a variable degree of virilization, ranging from a normal male phallus with unilateral ectopic gonad to ambiguous with a genital tubercle and bilateral not palpable gonads in four cases and normal female external genitalia in patient 5. Karyotype found 45, X/46, XY mosaicism in patient 1 and 2 and 45, X/46, X, der (Y) mosaicism in patient 3, 4 and 5. Three cases were assigned to male gender and two cases were assigned to female. After radiologic and histologic exploration, four patients had been explored by laparoscopy to perform gonadectomy in two cases and Mullerian derivative resection in the other. Urethroplasty was done in two cases of posterior hypospadias. Gender identity was concordant with the sex of assignment at birth in only 3 cases. Conclusion Because of the phenotypic heterogeneity of this sexual disorders and the variability of its management care, then the decision should rely on a multidisciplinary team approach.

PurposeTo evaluate the diagnostic accuracy of a novel optical coherence tomography texture-based en face image analysis (SALSA-Texture) that requires segmentation of only 1 retinal layer for glaucoma detection in eyes with axial high myopia, and to compare SALSA-Texture with standard macular ganglion cell-inner plexiform layer (GCIPL) thickness, macular retinal nerve fiber layer (mRNFL) thickness, and ganglion cell complex (GCC) thickness maps.DesignComparison of diagnostic approaches.MethodsCross-sectional data were collected from 92 eyes with primary open-angle glaucoma (POAG) and 44 healthy control eyes with axial high myopia (axial length >26 mm). Optical coherence tomography texture en face images, developed using SALSA-Texture to model the spatial arrangement patterns of the pixel intensities in a region, were generated from 70-μm slabs just below the vitreal border of the inner limiting membrane. Areas under the receiver operating characteristic curves (AUROCs) and areas under the precision recall curves (AUPRCs) adjusted for both eyes, axial length, age, disc area, and image quality were used to compare different approaches.ResultsThe best parameter-adjusted AUROCs (95% confidence intervals) for differentiating between healthy and glaucoma high myopic eyes were 0.92 (0.88-0.94) for texture en face images, 0.88 (0.86-0.91) for macular RNFL thickness, 0.87 (0.83-0.89) for macula GCIPL thickness, and 0.87 (0.84-0.89) for GCC thickness. A subset analysis of highly advanced myopic eyes (axial length ≥27 mm; 38 glaucomatous eyes and 22 healthy eyes) showed the best AUROC was 0.92 (0.89-0.94) for texture en face images compared with 0.86 (0.84-0.88) for macular GCIPL, 0.86 (0.84-0.88) for GCC, and 0.84 (0.81-0.87) for RNFL thickness (P ≤ .02 compared with texture for all comparisons).ConclusionThe current results suggest that our novel en face texture-based analysis method can improve on most investigated macular tissue thickness measurements for discriminating between highly myopic glaucomatous and highly myopic healthy eyes. While further investigation is needed, texture en face images show promise for improving the detection of glaucoma in eyes with high myopia where traditional retinal layer segmentation often is challenging.

Background: Inguinal hernia (IH) is a prevalent condition in children necessitating surgical repair. However, determining the optimal timing (early or delayed) of inguinal herniotomy in neonates and preterm infants remains debatable. While open herniotomy traditionally serves as the standard inguinal hernia repair (IHR) procedure, laparoscopic repair has gained traction in recent decades. Our study aims to scrutinize both the optimal timing and approach to inguinal hernia repair in neonates and preterm infants. Methods: We conducted a literature review on surgical repair of IH in neonates and preterm infants published between 1999 and 2024. Results: Twenty studies met the inclusion criteria for this review. All studies were retrospective, predominantly originating from Europe and the United States. Thirteen out of the 20 studies focused solely on preterm infants. Patient sample sizes ranged from 30 to 8037, totaling 14533 patients. Most studies indicate that delaying inguinal hernia repair in newborns and preterm infants does not increase the risk of incarceration or recurrence. Moreover, postponing repair until after discharge from the NICU correlates with a shorter postoperative hospital stay and notably diminishes the risk of long-term postoperative ventilator dependence, thereby mitigating potential perioperative complications. Hence, this approach seems safe for certain patients whose families can reliably access appropriate surgical care. Conclusion: Significant disparities exist in the timing of inguinal hernia repair for newborns and preterm infants across various pediatric surgery centers. Current evidence suggests delayed inguinal hernia repair may be a viable option for selected patients. Regarding the optimal approach in this population, laparoscopy appears safe and effective.

Background: Fetus in fetu is a rare congenital malformation resulting from abnormal embryogenesis in a monochorionic diamniotic twinning gestation. This study aimed to document our experience with this anomaly in a developing country. Methods: This retrospective analysis covers cases of fetus in fetu diagnosed at our institution between 1999 and 2023. Patients presenting with an intracorporeal mass containing a vertebral column and an appropriate arrangement of organs and limbs around the axis were included in the study. Results: Five female patients with fetus in fetu were identified. The timing of diagnosis varied from incidental antenatal ultrasound discovery to postnatal abdominal mass presentation. The initial diagnosis involved radiological and serological investigations, confirmed by histopathology after surgical excision. The mean age at the time of surgery was 44 days. All patients underwent complete excision of the fetal mass, resulting in excellent postoperative outcomes and no recurrences. Conclusion: Fetus in fetu is a pediatric rarity and must be distinguished from a teratoma mass, which carries a high malignant potential. Histological and genetic studies may contribute to understanding the pathogenesis and etiology of the disease.

Several studies have investigated muscle rigidity using SWE. However, the assessments may not consider the most affected regions within the same muscle tissue nor the intramuscular variability of rigidity between muscles of the same muscle group, e.g., plantar flexors. The data presented in this article aimed to explore the inter–and intramuscular variability of plantar flexors stiffness during prone and standing positions at different muscle lengths in healthy and paretic individuals. Shear wave ultrasound images were acquired for the three plantar flexor muscles (gastrocnemius medialis [GM], gastrocnemius lateralis [GL], and soleus [SOL]) in two positions: prone and standing. The imaging was conducted at various dorsiflexion angles (0°, 10°, and 20°), and measurements were taken at different proximo-distal regions within each muscle. This data set allowed us to highlight the impact of stroke on mechanical properties that varies depending on whether ankle muscles are in an active or passive state during dorsiflexion. Additionally, the modification of the ankle muscle state influences the distribution of stiffness both within and between the plantar flexors.

PurposeTo determine the predictors of Bruch membrane opening (BMO) location accuracy and the visibility of the BMO location in glaucoma and healthy individuals with and without axial high myopia.DesignCross-sectional study.MethodsHealthy eyes and eyes with glaucoma from an American study and a Korean clinic population were classified into 2 groups: those with no axial high myopia (axial length [AL]

PurposeTo compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of instrument-provided, feature-based optical coherence tomography angiography (OCTA) vessel density measurements and OCT retinal nerve fiber layer (RNFL) thickness measurements for classifying healthy and glaucomatous eyes.DesignComparison of diagnostic approaches.MethodsA total of 130 eyes of 80 healthy individuals and 275 eyes of 185 glaucoma patients with optic nerve head (ONH) OCTA and OCT imaging were included. Classification performance of a VGG16 CNN trained and tested on entire en face 4.5 × 4.5-mm radial peripapillary capillary OCTA ONH images was compared to the performance of separate GBC models trained and tested on standard OCTA and OCT measurements. Five-fold cross-validation was used to test predictions for CNNs and GBCs. Areas under the precision recall curves (AUPRC) were calculated to control for training/test set size imbalance and were compared.ResultsAdjusted AUPRCs for GBC models were 0.89 (95% CI = 0.82, 0.92) for whole image vessel density GBC, 0.89 (0.83, 0.92) for whole image capillary density GBC, 0.91 (0.88, 0.93) for combined whole image vessel and whole image capillary density GBC, and 0.93 (0.91, 095) for RNFL thickness GBC. The adjusted AUPRC using CNN analysis of en face vessel density images was 0.97 (0.95, 0.99) resulting in significantly improved classification compared to GBC OCTA-based results and GBC OCT-based results (P ≤ 0.01 for all comparisons).ConclusionDeep learning en face image analysis improves on feature-based GBC models for classifying healthy and glaucoma eyes.

PurposeTo compare optic nerve head (ONH) ovality index and rotation angle measurements based on semi-automated delineation of the clinical ONH margin derived from photographs and automated BMO configuration derived from optical coherence tomography (OCT) images in healthy and glaucomatous eyes with high-, mild- and no axial myopia.MethodsOne hundred seventy-five healthy and glaucomatous eyes of 146 study participants enrolled in the Diagnostic Innovations in Glaucoma Study (DIGS) with optic disc photographs and Spectralis OCT ONH scans acquired on the same day were stratified by level of axial myopia (non-myopic [n = 56, axial length (AL) 26 mm]. The clinical disc margin of each photograph was manually annotated, and semi-automated measurements were recorded of the ovality index and rotation angle based on a best-fit ellipse generated using ImageJ software. These semi-automated photograph-based measurements were compared to ovality index and rotation angle generated from custom automated BMO-based analysis using segmented OCT ONH volumes. R 2 values from linear mixed effects models were used to describe the associations between semi-automated, photograph-based and automated OCT-based measurements.ResultsAverage (95% CI) axial length was 23.3 (23.0, 23.3) mm, 24.8 (24.7, 25.0) mm and 26.8 (26.6, 27.0) mm in non-myopic, mild-myopic and high-myopic eyes, respectively (ANOVA, p ≤ 0.001 for all). The R 2 association (95% CI) between semi-automated photograph-based and automated OCT-based assessment of ONH OI for all eyes was [0.26 (0.16, 0.36); p < 0.001]. This association was weakest in non-myopic eyes [0.09 (0.01, 0.26); p = 0.02], followed by mild-myopic eyes [0.13 (0.02, 0.29); p = 0.004] and strongest in high-myopic eyes [0.40 (0.19, 0.60); p < 0.001]. No significant associations were found between photography- and OCT-based assessment of rotation angle with R 2 values ranging from 0.00 (0.00, 0.08) in non-myopic eyes to 0.03 (0.00, 0.21) in high-myopic eyes (all associations p ≥ 0.33).ConclusionsAgreement between photograph-based and automated OCT-based ONH morphology measurements is limited, suggesting that these methods cannot be used interchangeably for characterizing myopic changes in the ONH.

This paper presents an in-depth review of software-defined named data networking (SD-NDN), a transformative approach in network architectures poised to deliver substantial benefits. By addressing the limitations inherent in traditional host-centric network architectures, SD-NDN offers improvements in network performance, scalability, and efficiency. The paper commences with an overview of named data networking (NDN) and software-defined networking (SDN), the two fundamental building blocks of SD-NDN. It then explores the specifics of integrating NDN with SDN, illustrating examples of various SD-NDN models. These models are designed to leverage SDN for NDN routing, caching, and forwarding. The paper concludes by proposing potential strategies for further integration of SDN and NDN and some open research questions. These proposed strategies aim to stimulate further exploration and innovation in the field of SD-NDN.

Introduction The abdomen is the most prevalent site of primarily unidentified fatal injury after blunt trauma, and represents the third major affected part. However, follow-up strategies of intra-abdominal injuries remain controversial. The aim of this study is to describe the characteristics of children with blunt abdominal trauma (BAT) and who presented radiographic amendments in re-imaging studies and predict factors that could identify patients group requiring control in this population. Methods A retrospective study was conducted in the department of pediatric surgery and the intensive care unit of Monastir involving all patients under the age of 14 years old who were admitted for blunt abdominal trauma between January 2010 and December 2021.An analysis of epidemiological, clinical, radiological and therapeutic management characteristics were performed. Results A total of 151 patients were included in this study with a mean age of six years. The sex ratio (m/f) was 2.7. Most trauma circumstances were related to road accidents (43.7%) and falls (32.4%). The liver, the spleen, and the kidneys were the most frequently affected organs. A combination of intra-abdominal lesions was recorded in 32 cases. 84% of the cases (110/131) of the reimaged patients had no complications, compared to 16% (21/131) who developed intra-abdominal complications. The complicated reimaging findings were significantly associated with high grade hepatic and renal injuries (p=0.019 and 0.002, respectively), and patient symptom persistence or development (p=0.001). Conclusion It is safe to avoid performing reimaging studies if clinical progression remains uneventful in children with low-grade hepatic, splenic, and renal lesions. However, the clinical assessment is the most crucial consideration during BAT management in children.

Abstract Background Metanephric adenoma is a rare benign renal tumor of the kidney, uncommonly observed in children. It is often misdiagnosed preoperatively as a malignant neoplasm, leading to an unnecessary nephrectomy. The challenge is to make the right diagnosis preoperatively and therefore manage it with conservative surgery. We report a case of a child with metanephric adenoma who underwent nephron-sparing surgery. Case presentation A renal tumor was discovered fortuitously in an 18-month-old Caucasian girl with several congenital malformations. Investigations showed a 28 × 27 × 27 mm left renal mass centrally located, well defined, nonvascularized, with no calcifications and which compressed the adjacent renal tissue. Furthermore, there were no signs of metastasis. The decision of a multidisciplinary meeting was to perform a computed tomography (CT)-scan-guided biopsy. Histologic examination concluded it was a metanephric adenoma. We performed a left open partial nephrectomy via a flank retroperitoneal incision. The final histopathological examination confirmed the diagnosis. The postoperative course was uneventful. Conclusion Preoperative diagnosis of metanephric adenoma is challenging. Because of the high probability of unnecessary radical nephrectomy, preoperative biopsy can be safe and determining to guide a more conservative approach so nephron-sparing surgery can be performed.

PurposeTo compare change over time in eye-specific optical coherence tomography (OCT) retinal nerve fiber layer (RNFL)-based region-of-interest (ROI) maps developed using unsupervised deep-learning auto-encoders (DL-AE) to circumpapillary RNFL (cpRNFL) thickness for the detection of glaucomatous progression.MethodsForty-four progressing glaucoma eyes (by stereophotograph assessment), 189 nonprogressing glaucoma eyes (by stereophotograph assessment), and 109 healthy eyes were followed for ≥3 years with ≥4 visits using OCT. The San Diego Automated Layer Segmentation Algorithm was used to automatically segment the RNFL layer from raw three-dimensional OCT images. For each longitudinal series, DL-AEs were used to generate individualized eye-based ROI maps by identifying RNFL regions of likely progression and no change. Sensitivities and specificities for detecting change over time and rates of change over time were compared for the DL-AE ROI and global cpRNFL thickness measurements derived from a 2.22-mm to 3.45-mm annulus centered on the optic disc.ResultsThe sensitivity for detecting change in progressing eyes was greater for DL-AE ROIs than for global cpRNFL annulus thicknesses (0.90 and 0.63, respectively). The specificity for detecting not likely progression in nonprogressing eyes was similar (0.92 and 0.93, respectively). The mean rates of change in DL-AE ROI were significantly faster than for cpRNFL annulus thickness in progressing eyes (-1.28 µm/y vs. -0.83 µm/y) and nonprogressing eyes (-1.03 µm/y vs. -0.78 µm/y).ConclusionsEye-specific ROIs identified using DL-AE analysis of OCT images show promise for improving assessment of glaucomatous progression.Translational relevanceThe detection and monitoring of structural glaucomatous progression can be improved by considering eye-specific regions of likely progression identified using deep learning.

This study characterizes differences in glaucomatous eyes with and without high axial myopia using custom automated analysis of OCT images. 452 eyes of 277 glaucoma patients were stratified into non (n = 145 eyes), mild (n = 214 eyes), and high axial myopia (axial length (AL) > 26 mm, n = 93 eyes). Optic disc ovality index, tilt and rotation angle of Bruch´s membrane opening (BMO) and peripapillary choroidal thickness (PCT) were calculated using automated and deep learning strategies. High myopic optic discs were more oval and had larger BMO tilt than mild and non-myopic discs (both p

PurposeThe purpose of this study was to determine if the rate of change in the depth of the surface of the lamina cribrosa due to glaucomatous remodeling differs between glaucoma patients of African descent (AD) and European descent (ED).MethodsThere were 1122 images taken longitudinally over an average of 3 years (range = 0.9-4.1 years) from 122 patients with glaucoma followed in the African Descent and Glaucoma Evaluation Study (ADAGES) and Diagnostic Intervention and Glaucoma Study (DIGS) were automatically segmented to compute anterior lamina cribrosa surface depth (ALCSD). The rate of ALCSD change was compared across racial groups after adjusting for baseline characteristics known to be associated with ALCSD or disease progression (visual field, ALCSD, corneal thickness, optic disk size, and age).ResultsAfter adjusting for all other covariates, the ED group had significantly greater ALCSD posterior migration (deepening) than the AD group (difference = 2.57 µm/year, P = 0.035). There was a wider range of ALCSD change in the ED compared with the AD group, and more individuals had greater magnitude of both deepening and shallowing. No other covariates measured at baseline had independent effects on the longitudinal changes in ALCSD (baseline visual field severity, baseline ALCSD, corneal thickness, Bruch's membrane opening [BMO] area, or age).ConclusionsGlaucomatous remodeling of the lamina cribrosa differs between AD and ED patients with glaucoma. Unlike the cross-sectional associations seen with aging, in which a deeper ALCSD was seen with age in the ED group, glaucomatous remodeling in this longitudinal study resulted in more posterior migration of ALCSD in ED compared to AD patients.

The Cancel Culture Between Quest for Justice and Overflow Cancel culture has developed in the various arts in multiple facets in order to publicly denounce individuals, groups or institutions responsible for acts, behaviours or remarks considered inadmissible or despicable by human society. Even if the concept of "the cancel culture " appeared at the end of the 2010s. Thus, more than an awareness strategy, the objective of the cancel culture in the arts is part of a real confrontation and continual struggle to minimize the impact of groups and behaviours that threaten modern societies. Since September 11, 2001 and the attacks on the twin towers of the World Trade Center and the Pentagon, several directors have chosen to fight against religious fanaticism and terrorism by directing films that denounce Islamic fundamentalism and fanaticism in all its forms. The appearance of Daesh in Syria and Iraq has led to the proliferation of cinematographic works that denounce the brutality, religious fanaticism and terrorism of this terrorist group. However, we note that a large part of the cinematographic works and successful series have attached the label of terrorist to Muslims and to the inhabitants of the Arab-Muslim world. In other words, by fighting against certain behaviours and stereotypes, some of the filmmakers have contributed to creating new stereotypes and nourishing them through artistic creations that stigmatize a particular religious category. We will first seek to present the different social representations of the phenomenon of cancel culture in popular culture and on social networks. In this first part, we will demonstrate the role of the cancel culture in delivering justice to victims in new, less legal and more instantaneous forms. Secondly, we will illustrate through different cinematographic examples how the concept of the cancel culture has led to the appearance of films and series that denounce intolerance, religious fundamentalism and terrorism. Finally, we will focus on the limits of this concept which has overflowed in several artistic representations to turn into the stigmatization and lynching of a particular social and religious category.

In the description of the automated delineation algorithm, it was incorrectly stated that measurements were made 1.8 µm from the BMO centroid, whereas the correct value was 18 µm. The affected sentence is in the Image Acquisition and Processing section of the Methods and originally read: “The mean of ALCSD measurements from all 48 B-scans within 1.8 µm of the center of the BMO from each individual eye was used for the analysis after qualitative review of the accuracy of the BMO and ALCSD segmentation.” The error has been corrected in the article online and does not affect the conclusions of the article.

PurposeTo compare gradient-boosting classifier (GBC) analysis of optical coherence tomography angiography (OCTA)-measured vessel density (VD) and OCT-measured tissue thickness to standard OCTA VD and OCT thickness parameters for classifying healthy eyes and eyes with early to moderate glaucoma.DesignComparison of diagnostic tools.MethodsA total of 180 healthy eyes and 193 glaucomatous eyes with OCTA and OCT imaging of the macula and optic nerve head (ONH) were studied. Four GBCs were evaluated that combined 1) all macula VD and thickness measurements (Macula GBC), 2) all ONH VD and thickness measurements (ONH GBC), 3) all VD measurements from the macula and ONH (vessel density GBC), and 4) all thickness measurements from the macula and ONH (thickness GBC). ROC curve (AUROC) analyses compared the diagnostic accuracy of GBCs to that of standard instrument-provided parameters. A fifth GBC that combined all parameters (full GBC) also was investigated.ResultsGBCs had better diagnostic accuracy than standard OCTA and OCT parameters with AUROCs ranging from 0.90 to 0.93 and 0.64 to 0.91, respectively. The full GBC (AUROC = 0.93) performed significantly better than the ONH GBC (AUROC = 0.91; P = .036) and the vessel density GBC (AUROC = 0.90; P = .010). All other GBCs performed similarly. The mean relative influence of each parameter included in the full GBC identified a combination of macular thickness and ONH VD measurements as the greatest contributors.ConclusionsGBCs that combine OCTA and OCT macula and ONH measurements can improve diagnostic accuracy for glaucoma detection compared to most but not all instrument provided parameters.

PurposeTo compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models.MethodsTwo fundus photograph datasets from the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study and Matsue Red Cross Hospital were used to independently develop deep learning algorithms for detection of glaucoma at the University of California, San Diego, and the University of Tokyo. We compared three versions of the University of California, San Diego, and University of Tokyo models: original (no retraining), sequential (retraining only on new data), and combined (training on combined data). Independent datasets were used to test the algorithms.ResultsThe original University of California, San Diego and University of Tokyo models performed similarly (area under the receiver operating characteristic curve = 0.96 and 0.97, respectively) for detection of glaucoma in the Matsue Red Cross Hospital dataset, but not the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study data (0.79 and 0.92; P < .001), respectively. Model performance was higher when classifying moderate-to-severe compared with mild disease (area under the receiver operating characteristic curve = 0.98 and 0.91; P < .001), respectively. Models trained with the combined strategy generally had better performance across all datasets than the original strategy.ConclusionsDeep learning glaucoma detection can achieve high accuracy across diverse datasets with appropriate training strategies. Because model performance was influenced by the severity of disease, labeling, training strategies, and population characteristics, reporting accuracy stratified by relevant covariates is important for cross study comparisons.Translational relevanceHigh sensitivity and specificity of deep learning algorithms for moderate-to-severe glaucoma across diverse populations suggest a role for artificial intelligence in the detection of glaucoma in primary care.

PurposeTo investigate the relationship between corneal hysteresis (CH) and anterior lamina cribrosa surface (ALCS) displacement over time in a cohort of patients with glaucoma.DesignProspective observational case series.MethodsIn this study, 147 eyes from 96 glaucoma or glaucoma suspect patients were followed for a mean of 3.5 years and 7.9 visits. Baseline CH measurements were obtained using the Ocular Response Analyzer (ORA; Reichert Ophthalmic Instruments Inc, Depew, New York, USA). The mean anterior lamina cribrosa surface depth (ALCSD) and choroidal thickness were by automated segmentation of spectral-domain optical coherence tomography (SD-OCT) scans. The rate of change of ALCSD was calculated using linear mixed effects models. Relationship between baseline CH and follow-up ALCSD rate of change was adjusted for confounding factors, including age, intraocular pressure (IOP), and choroidal thickness.ResultsThe mean baseline CH was 9.4 mm Hg (95% confidence interval [CI] 9.1-9.7). Overall, the ALCS was displaced posteriorly at a rate of 0.78 μm/y (95% CI -1.82, 0.26). Seventeen eyes (11.5%) showed a significant posterior displacement of ALCS, whereas 22 eyes (15.0%) showed a significant anterior displacement of ALCS. The choroidal thickness thinned at a rate of -1.09 μm/y during the follow-up (P = .001). Multivariable mixed modeling showed that choroidal thinning, lower IOP change, and lower corneal hysteresis were significantly associated with posterior ALCS displacement over time (P = .034, P = .037, and P = .048). Each 1 mm lower CH was associated with 0.66 μm/y posterior displacement of the ALCS.ConclusionsLower corneal hysteresis was significantly associated with posterior displacement of the anterior lamina cribrosa over time. These data provide additional support for lower corneal hysteresis being a risk factor for glaucoma progression.

PurposeTo develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.DesignEvaluation of a diagnostic technology.ParticipantsA total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes).MethodsDeep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data.Main outcome measuresDeep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R2 and mean absolute error (MAE).ResultsIn the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R2 of 0.70 and MAE of 2.5 decibels (dB) compared with 0.45 and 3.7 dB for RNFL thickness measurements. In predicting mean VF sectoral PD, deep learning models achieved high accuracy in the inferior nasal (R2 = 0.60) and superior nasal (R2 = 0.67) sectors, moderate accuracy in inferior (R2 = 0.26) and superior (R2 = 0.35) sectors, and lower accuracy in the central (R2 = 0.15) and temporal (R2 = 0.12) sectors.ConclusionsDeep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.

The advancement of technology has paved the way for a new type of bullying, which often leads to negative stigma in the social setting. Cyberbullying is a cybercrime wherein one individual becomes the target of harassment and hatred. It has recently become more prevalent due to a rise in the usage of social media platforms, and, in some severe situations, it has even led to victims’ suicides. In the literature, several cyberbullying detection methods are proposed, but they are mainly focused on word-based data and user account attributes. Furthermore, most of them are related to the English language. Meanwhile, only a few papers have studied cyberbullying detection in Arabic social media platforms. This paper, therefore, aims to use machine learning in the Arabic language for automatic cyberbullying detection. The proposed mechanism identifies cyberbullying using the Support Vector Machine (SVM) classifier algorithm by using a real dataset obtained from YouTube and Twitter to train and test the classifier. Moreover, we include the Farasa tool to overcome text limitations and improve the detection of bullying attacks.