Optimal Control and Optimization of Grid-Connected PV and Wind Turbine Hybrid Systems Using Electric Eel Foraging Optimization Algorithms.

This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system's architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load's power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems.

Expanding Scope About Factors Influencing Seroma Formation After Breast Cancer Surgery.

Objectives: To determine the incidence of seroma formation after breast cancer surgery, and its association with common risk factors. Method: The correlationalstudy was conducted at the General Surgery department of Kafrelsheikh University Hospital, Egypt, from March 2020 to March 2022 and comprised patients having breast cancerstage I, II or III, as per the Tumour Node-Metastasis classification, who were scheduled to undergo modified radical mastectomy, breast conserving surgery or reconstructive surgery. Baseline, intraoperative and postoperative data was collected on a proforma. Data was analysed using SPSS 22. RESULTS: Of the 50 female patients with mean age 45±5.20 (range: 20-70 years), 30(60%) were in the elderly group aged >45 years, while 20(40%) were aged <45years. Overall, 12(24%) cases developed seroma; 9(30%) in the elderly group. There were 24(48%) cases of modified radical mastectomy, and 8(33.3%) had seroma. Electrocautery was used for breast dissection in 30(60%) cases, and, among them, seroma developed in 10(33.3%) patients. CONCLUSIONS: Age, body weight, afflicted breast side, site, and size of breast mass were not found to be significant predictors of seroma formation following breast cancer surgery.

Oncoplastic Volume Displacement Surgical Techniques For Early Primary Breast Cancer: A Case Series.

Oncoplastic breast surgery (OPS) is a new strategy for expanding breast-conserving surgical options, lowering mastectomies rates, and preventing deformities. OPS is based on the use of plastic surgical reconstruction after breast cancer removal. The study aims to assess volume displacement oncoplastic procedures for early primary breast cancer in terms of recurrence and cosmoses. A case series study was done on 20 patients with early breast cancer who underwent oncoplastic volume displacement techniques in the period from March 2019 to March 2021 in Kafrelsheikh University Hospital, Egypt. OPS techniques included were Racquet, Benelli, Batwing and Grisotti technique. The study concluded that OPS are oncologically safe (100%) with no recurrence and a better aesthetic outcome (90%).

Diagnostic Performance of 3-Dimensional Thickness of the Endothelium-Descemet Complex in Fuchs' Endothelial Cell Corneal Dystrophy.

PURPOSE: To describe the diagnostic accuracy of 3-dimensional (3D) endothelium-Descemet's membrane complex thickness (En-DMT) in Fuchs' endothelial corneal dystrophy (FECD) and determine its potential role as an objective index of disease severity. DESIGN: Observational case-control study. PARTICIPANTS: One hundred four eyes of 79 participants (64 eyes of 41 FECD patients and 40 eyes of 38 healthy controls). METHODS: All participants received high-definition OCT imaging (Envisu R2210; Bioptigen, Buffalo Grove, IL). Fuchs' endothelial corneal dystrophy was classified clinically into early-stage (without edema) and late-stage (with edema) disease. Automatic and manual segmentation of corneal layers was performed using a custom-built segmental tomography algorithm to generate 3D maps of total corneal thickness (TCT) and En-DMT of the central 6-mm cornea. Regional En-DMT, regional TCT, and central-to-peripheral total corneal thickness ratio (CPTR) were evaluated and correlated to the clinical severity of FECD. Intraclass correlation coefficients (ICCs) and Bland-Altman plots were used to assess the reliability of the repeated measurements in all eyes. MAIN OUTCOME MEASURES: Central-to-peripheral total corneal thickness ratio and average En-DMT and TCT of central, paracentral, and peripheral regions. RESULTS: In FECD, a significant increase in En-DMT, CPTR, and TCT was found compared to controls (P < 0.001). For identifying FECD, average En-DMT of paracentral and peripheral regions achieved 94% sensitivity and 100% specificity (cutoffs, 19 µm and 20 µm, respectively), whereas CPTR showed 94% sensitivity with a 73% specificity (cutoff, 0.97). Regarding early-stage FECD, average En-DMT of central zones achieved 92% sensitivity and 97% specificity (cutoff, 18 µm), whereas CPTR showed 90% sensitivity and 88% specificity (cutoff, 0.97). The average En-DMT of central, paracentral, and peripheral regions was correlated highly with FECD clinical stage (Spearman's ρ = 0.813, 0.793, and 0.721, respectively; all P < 0.001), compared with CPTR and mean TCT of paracentral zones (0.672 and 0.481, respectively; P < 0.001). The ICC values ranged from 0.98 (En-DMT) to 0.99 (TCT) with a good agreement between the automatic and manual measurements. CONCLUSIONS: Regional 3D En-DMT is a novel diagnostic tool of FECD that can be used to quantify the disease severity with excellent reliability.

Combined Colloid Preload and Crystalloid Coload Versus Crystalloid Coload During Spinal Anesthesia for Cesarean Delivery: A Randomized Controlled Trial.

BACKGROUND: The optimal strategy of fluid administration during spinal anesthesia for cesarean delivery is still unclear. Ultrasonography of the inferior vena cava (IVC) has been recently used to assess the volume status and predict fluid responsiveness. In this double-blind, randomized controlled study, we compared maternal hemodynamics using a combination of 500-mL colloid preload and 500-mL crystalloid coload versus 1000-mL crystalloid coload. We assessed the IVC at baseline and at subsequent time points after spinal anesthesia. METHODS: Two hundred American Society of Anesthesiologists physical status II parturients with full-term singleton pregnancies scheduled for elective cesarean delivery under spinal anesthesia were randomly allocated to receive either 500-mL colloid preload followed by 500-mL crystalloid coload (combination group) or 1000-mL crystalloid coload (crystalloid coload group) administered using a pressurizer. Ephedrine 3, 5, and 10 mg boluses were administered when the systolic blood pressure decreased below 90%, 80% (hypotension), and 70% (severe hypotension) of the baseline value, respectively. The IVC was assessed using the subcostal long-axis view at baseline, at 1 and 5 minutes after intrathecal injection, and immediately after delivery; the maximum and minimum IVC diameters were measured, and the IVC collapsibility index (CI) was calculated using the formula: IVC-CI = (maximum IVC diameter - minimum IVC diameter)/maximum IVC diameter. The primary outcome was the total ephedrine dose. RESULTS: Data from 198 patients (99 patients in each group) were analyzed. The median (range) of the total ephedrine dose was 11 (0-60) mg in the combination group and 13 (0-61) mg in the crystalloid coload group; the median of the difference (95% nonparametric confidence interval) was -2 (-5 to 0.00005) mg, P = .22. There were no significant differences between the 2 groups in the number of patients requiring ephedrine, the incidence of hypotension and severe hypotension, the time to the first ephedrine dose, and neonatal Apgar scores at 1 and 5 minutes. The maximum and minimum IVC diameters in each group increased after spinal anesthesia and after delivery, and they were larger in the combination group. The IVC-CI after delivery was higher in the crystalloid coload group. CONCLUSIONS: The combination of 500-mL colloid preload and 500-mL crystalloid coload did not reduce the total ephedrine dose or improve other maternal outcomes compared with 1000-mL crystalloid coload. The IVC was reliably viewed before and during cesarean delivery, and its diameters significantly changed over time and differed between the 2 groups.

Color image segmentation based on different color space models using automatic GrabCut.

This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied with RGB, HSV, CMY, XYZ, and YUV color spaces. The comparative study and experimental results using different color images show that RGB color space is the best color space representation for the set of the images used.

Ultrasonography versus palpation for spinal anesthesia in obese parturients undergoing cesarean delivery: a randomized controlled trial.

INTRODUCTION: Ultrasonography may facilitate neuraxial blocks in obstetrics. This randomized controlled trial aimed to compare preprocedural ultrasonography with landmark palpation for spinal anesthesia in obese parturients undergoing cesarean delivery. METHODS: 280 American Society of Anesthesiologists (ASA) physical status II-III parturients with body mass index ≥35 kg/m2, full-term singleton pregnancy, undergoing elective cesarean delivery under spinal anesthesia, were randomly assigned to two equal groups (ultrasonography and palpation); preprocedural systematic ultrasound approach and conventional landmark palpation were performed, respectively. Patients and outcome assessors were blinded to the study group. All ultrasound and spinal anesthetic procedures were performed by a single experienced anesthesiologist. The primary outcome was the number of needle passes required to obtain free cerebrospinal fluid (CSF) flow. Secondary outcomes were the number of skin punctures required to obtain free CSF flow, success rate at the first needle pass, success rate at the first skin puncture, duration of the spinal procedure, patient satisfaction and incidence of vascular puncture, paresthesia, failure to obtain CSF flow and failed spinal block. RESULTS: There were no significant differences in primary or secondary outcomes between the two groups. The median (IQR) of the number of needle passes required to obtain free CSF flow was 3 (1-7) in ultrasonography group and 3 (1-7) in palpation group; p=0.62. CONCLUSIONS: Preprocedural ultrasonography did not decrease the number of needle passes required to obtain free CSF flow or improve other outcomes compared with landmark palpation during spinal anesthesia performed by a single experienced anesthesiologist in obese parturients undergoing cesarean delivery. TRIAL REGISTRATION NUMBER: NCT03792191; : https://clinicaltrials.gov/ct2/show/NCT03792191.

Foreign body in lung: a case report.

Introduction: The presence of a metal foreign body (FB) in the lungs can be a major contributor to pulmonary embolism. FB pulmonary embolism is a rare condition and difficult to diagnose due to its variable clinical presentations. Case Presentation: This article presents a case of a 34-year-old male presented to our emergency department after getting injured with a FB fragment entering the upper right thigh. Plain X-ray proved the presence of the FB and patient was admitted to surgical extraction. However, the FB disappeared with the first attempt to reposition the patient. Then, the patient underwent a chest computed tomography (CT) without contrast that revealed the presence of the FB in the heart with a characteristic finding of a starburst appearance. Then the patient was transferred to the Catheter lab. But cardiologists did not find the FB inside the heart. Another chest CT scan was done that showed the FB in the right lower pulmonary artery. Then patient was transferred to the cardio-thoracic surgery department that decided not to remove the FB and just follow-up. Discussion: There are many cases of metal FB lodged in the vascular system. In this case, CT scan had been performed to the FB helped to take the appropriate decision before the FB causes damage to any vital organ. Conclusion: FB traveling through the vascular system can cause a pulmonary embolism. In our case, the time the FB took to reach the pulmonary arteries was merely few hours. However, appropriate management could help steer the patient away from danger.

A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power Systems.

The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching capabilities, this study proposes an improved SAO (ISAO) that incorporates a cooperative learning technique based on the leader solution. First, after considering testing on different standard mathematical benchmark functions, the proposed ISAOA is assessed in comparison to the standard SAOA. The simulation results declare that the proposed ISAOA establishes great superiority over the standard SAOA. Additionally, the proposed ISAOA is adopted to handle power system applications for Thyristor Controlled Series Capacitor (TCSC) allocation-based losses reduction in electrical power grids. The SAOA and the proposed ISAOA are employed to optimally size the TCSCs and simultaneously select their installed transmission lines. Both are compared to two recent algorithms, the Artificial Ecosystem Optimizer (AEO) and AQuila Algorithm (AQA), and two other effective and well-known algorithms, the Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO). In three separate case studies, the standard IEEE-30 bus system is used for this purpose while considering varying numbers of TCSC devices that will be deployed. The suggested ISAOA's simulated implementations claim significant power loss reductions for the three analyzed situations compared to the GWO, AEO, PSO, and AQA.

An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification.

The most dangerous disease in recent decades is lung cancer. The most accurate method of cancer diagnosis, according to research, is through the use of histopathological images that are acquired by a biopsy. Deep learning techniques have achieved success in bioinformatics, particularly medical imaging. In this paper, we present an innovative method for rapidly identifying and classifying histopathology images of lung tissues by combining a newly proposed Convolutional Neural Networks (CNN) model with a few total parameters and the enhanced Light Gradient Boosting Model (LightGBM) classifier. After the images have been pre-processed in this study, the proposed CNN technique is provided for feature extraction. Then, the LightGBM model with multiple threads has been used for lung tissue classification. The simulation result, applied to the LC25000 dataset, demonstrated that the novel technique successfully classifies lung tissue with 99.6% accuracy and sensitivity. Furthermore, the proposed CNN model has achieved the lowest training parameters of only one million parameters, and it has also achieved the shortest processing time of just one second throughout the feature extraction process. When this result is compared with the most recent state-of-the-art approaches, the suggested approach has increased effectiveness in the areas of both disease classification accuracy and processing time.

Application of modified artificial hummingbird algorithm in optimal power flow and generation capacity in power networks considering renewable energy sources.

Today's electrical power system is a complicated network that is expanding rapidly. The power transmission lines are more heavily loaded than ever before, which causes a host of problems like increased power losses, unstable voltage, and line overloads. Real and reactive power can be optimized by placing energy resources at appropriate locations. Congested networks benefit from this to reduce losses and enhance voltage profiles. Hence, the optimal power flow problem (OPF) is crucial for power system planning. As a result, electricity system operators can meet electricity demands efficiently and ensure the reliability of the power systems. The classical OPF problem ignores network emissions when dealing with thermal generators with limited fuel. Renewable energy sources are becoming more popular due to their sustainability, abundance, and environmental benefits. This paper examines modified IEEE-30 bus and IEEE-118 bus systems as case studies. Integrating renewable energy sources into the grid can negatively affect its performance without adequate planning. In this study, control variables were optimized to minimize fuel cost, real power losses, emission cost, and voltage deviation. It also met operating constraints, with and without renewable energy. This solution can be further enhanced by the placement of distributed generators (DGs). A modified Artificial Hummingbird Algorithm (mAHA) is presented here as an innovative and improved optimizer. In mAHA, local escape operator (LEO) and opposition-based learning (OBL) are integrated into the basic Artificial Hummingbird Algorithm (AHA). An improved version of AHA, mAHA, seeks to improve search efficiency and overcome limitations. With the CEC'2020 test suite, the mAHA has been compared to several other meta-heuristics for addressing global optimization challenges. To test the algorithm's feasibility, standard and modified test systems were used to solve the OPF problem. To assess the effectiveness of mAHA, the results were compared to those of seven other global optimization algorithms. According to simulation results, the proposed algorithm minimized the cost function and provided convergent solutions.

Comparison of Autonomous AS-OCT Deep Learning Algorithm and Clinical Dry Eye Tests in Diagnosis of Dry Eye Disease.

OBJECTIVE: To evaluate a deep learning-based method to autonomously detect dry eye disease (DED) in anterior segment optical coherence tomography (AS-OCT) images compared to common clinical dry eye tests. METHODS: In this study, 27,180 AS-OCT images were prospectively collected from 151 eyes of 91 patients. Images were used to train and test the deep learning model. Masked cornea specialist ophthalmologist diagnoses were used as the gold standard. Clinical dry eye tests were performed on patients in the DED group to compare the results of the model. The dry eye tests performed were tear break-up time (TBUT), Schirmer's test, corneal staining, conjunctival staining, and Ocular Surface Disease Index (OSDI). RESULTS: Our deep learning model achieved an accuracy of 84.62%, sensitivity of 86.36%, and specificity of 82.35% in the diagnosis of DED. The positive likelihood ratio was 4.89, and the negative likelihood ratio was 0.17. The mean DED probability score was 0.81 ± 0.23 in the DED group and 0.20 ± 0.27 in the healthy group (P < 0.01). The deep learning model accuracy in the diagnosis of DED was significantly better than that of corneal staining, conjunctival staining, and Schirmer's test (P < 0.05). There was no significant difference between the deep learning diagnostic accuracy and that of the OSDI and TBUT. CONCLUSION: Based on preliminary results, reliable autonomous diagnosis of DED with our deep learning model was achieved, when compared with standard dry eye clinical tests that correlated significantly more or similarly to diagnoses made by cornea specialist ophthalmologists.

YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms.

BACKGROUND AND OBJECTIVE: With the recent development in deep learning since 2012, the use of Convolutional Neural Networks (CNNs) in bioinformatics, especially medical imaging, achieved tremendous success. Besides that, breast masses detection and classifications in mammograms and their pathology classification are considered a critical challenge. Till now, the evaluation process of the screening mammograms is held by human readers which is considered very monotonous, tiring, lengthy, costly, and significantly prone to errors. METHODS: We propose an end to end computer-aided diagnosis system based on You Only Look Once (YOLO). The proposed system first preprocesses the mammograms from their DICOM format to images without losing data. Then, it detects masses in full-field digital mammograms and distinguishes between the malignant and benign lesions without any human intervention. YOLO has three different architectures, and, in this paper, the three versions are used for mass detection and classification in the mammograms to compare their performance. The use of anchors in YOLO-V3 on the original form of data and its augmented version is proved to improve the detection accuracy especially when the k-means clustering is applied to generate anchors corresponding to the used dataset. Finally, ResNet and Inception are used as feature extractors to compare their classification performance against YOLO. RESULTS: Mammograms with different resolutions are used and based on YOLO-V3, the best results are obtained through detecting 89.4% of the masses in the INbreast mammograms with an average precision of 94.2% and 84.6% for classifying the masses as benign and malignant respectively. YOLO's classification network is replaced with ResNet and InceptionV3 to get overall accuracy of 91.0% and 95.5%, respectively. CONCLUSION: The proposed system showed using the experimental results the YOLO impact on the breast masses detection and classification. Especially using the anchor boxes concept in YOLO-V3 that are generated by applying k-means clustering on the dataset, we can detect most of the challenging cases of masses and classify them correctly. Also, by augmenting the dataset using different approaches and comparing with other recent YOLO based studies, it is found that augmenting the training set only is the fairest and accurate to be applied in the realistic scenarios.

Multidisease Deep Learning Neural Network for the Diagnosis of Corneal Diseases.

PURPOSE: To report a multidisease deep learning diagnostic network (MDDN) of common corneal diseases: dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images. STUDY DESIGN: Development of a deep learning neural network diagnosis algorithm. METHODS: A total of 158,220 AS-OCT images from 879 eyes of 478 subjects were used to develop and validate a classification deep network. After a quality check, the network was trained and validated using 134,460 images. We tested the network using a test set of consecutive patients involving 23,760 AS-OCT images of 132 eyes of 69 patients. The area under receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and F1 score and 95% confidence intervals (CIs) were computed. RESULTS: The MDDN achieved eye-level AUROCs >0.99 (95% CI: 0.90, 1.0), AUPRCs > 0.96 (95% CI: 0.90, 1.0), and F1 scores > 0.90 (95% CI: 0.81, 1.0) for DES, FED, and KCN, respectively. CONCLUSIONS: MDDN is a novel diagnostic tool for corneal diseases that can be used to automatically diagnose KCN, FED, and DES using only AS-OCT images.

Electron microscopic comparison of the donor cut edges using femtosecond laser-assisted keratoplasty versus conventional keratoplasty.

PURPOSE: To describe and compare the histological changes in the cut edges of the remaining donor corneal rim using femtosecond laser-assisted keratoplasty (FAK) versus conventional penetrating keratoplasty (PK) via light and transmission electron microscopic examination. METHODS: This was a prospective observational study of 10 eyes; 5 FAK (top-hat technique) and 5 conventional PK. Main outcomes were histological findings at the cut edge of the donor corneal rim (at 3, 6, 9, and 12 o'clock). RESULTS: Cellular and ultra-cellular changes in the form of stromal edema, disorganized collagen fibers, and nuclear changes were more prominent in the FAK eyes as compared to the conventional PK ones. CONCLUSION: FAK induces more collateral damage in the cut edge of corneal donor graft at cellular and ultra-cellular levels, compared to conventional trephination. Further studies are required to investigate the clinical ramifications of this observation.

Ensemble-based bag of features for automated classification of normal and COVID-19 CXR images.

The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients' confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers.

Directed jaya algorithm for delivering nano-robots to cancer area.

In the last few years, it was proposed to deliver drugs using Nano-robots for treating cancer. This paper compares between two recent and efficient algorithms for delivering Nano-robots to cancer area. These algorithms are Jaya algorithm and Directed Particle Swarm Optimization (DPSO) algorithm. In this paper, we also propose a new hybrid algorithm that combines Jaya and DPSO to speed up the process of Nano-robots delivery. The proposed algorithm is called Directed Jaya (DJaya) algorithm. Experiments have proved that the efficiency of DJaya is higher than both Jaya and DPSO. We show experimentally that DJaya starts delivering Nano-robots earlier than DPSO to facilitate the initiation of the drug release. Also, DJaya finishes delivering Nano-robots earlier than Jaya to complete the drug dose. In addition to this, DJaya groups the Nano-robots together in the target area like DPSO to speed up the drug release process. We finally propose a new strategy for destroying cancer cells efficiently with relatively small number of Nano-robots. This strategy can save 40% of Nano-robots.

Mobility improvement of patients with peripheral visual field losses using novel see-through digital spectacles.

PURPOSE: To evaluate see-through Augmented Reality Digital spectacles (AR DSpecs) for improving the mobility of patients with peripheral visual field (VF) losses when tested on a walking track. DESIGN: Prospective Case Series. PARTICIPANTS: 21 patients with peripheral VF defects in both eyes, with the physical ability to walk without assistance. METHODS: We developed the AR DSpecs as a wearable VF aid with an augmented reality platform. Image remapping algorithms produced personalized visual augmentation in real time based on the measured binocular VF with the AR DSpecs calibration mode. We tested the device on a walking track to determine if patients could more accurately identify peripheral objects. MAIN OUTCOME MEASURES: We analyzed walking track scores (number of recognized/avoided objects) and eye tracking data (six gaze parameters) to measure changes in the kinematic and eye scanning behaviors while walking, and assessed a possible placebo effect by deactivating the AR DSpecs remapping algorithms in random trials. RESULTS: Performance, judged by the object detection scores, improved with the AR DSpecs (P<0.001, Wilcoxon rank sum test) with an average improvement rate of 18.81%. Two gaze parameters improved with the activated algorithm (P<0.01, paired t-test), indicating a more directed gaze on the central path with less eye scanning. Determination of the binocular integrated VF with the DSpecs correlated with the integrated standard automated perimetry (R = 0.86, P<0.001), mean sensitivity difference 0.8 ± 2.25 dB (Bland-Altman). CONCLUSIONS: AR DSpecs may improve walking maneuverability of patients with peripheral VF defects by enhancing detection of objects in a testing environment.