Brain stroke image dataset. The accuracy achieved by them was 93.

Brain stroke image dataset Jun 16, 2022 · Here we present ATLAS v2. 2 implementation details and performance measures are given. Data and Challenge. The models are trained and validated using an extensive dataset of labeled brain imaging scans, enabling thorough performance assessment. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires This major project, undertaken as part of the Pattern Recognition and Machine Learning (PRML) course, focuses on predicting brain strokes using advanced machine learning techniques. • Each deface “MRI” has a ground truth consisting of at least one or more masks. 2016; Hakim et al. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. [12] have proposed a new method for the segmentation and classification of brain stroke from MR images where they used expectation–maximization and random forest classifier. However, analyzing large rehabilitation-related datasets is problematic due to barriers Background & Summary. Explore and run machine learning code with Kaggle Notebooks | Using data from Brain Stroke CT Image Dataset Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Six realistic head phantom computed from MRI scans, is surrounded by an antenna array of 16 dipole antennas distributed uniformly around the head. 1 and, in sub Section 4. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. 94871-94879, 2020, Jan 1, 2021 · Subudhi et al. Learn more In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. Deep learning networks are commonly employed for medical image analysis because they enable efficient computer-aided diagnosis. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze Nov 8, 2017 · Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. The dataset details used in this study are given in sub Section 4. These images undergo preprocessing steps such as standardization and normalization to ensure consistency and remove any biases in pixel values. [14] Sook-Lei Liew, Bethany P Lo, Miranda R Donnelly, Artemis Zavaliangos-Petropulu, Jessica N Jeong, Giuseppe Barisano, Alexandre Hutton, Julia P Simon, Julia M Juliano, Anisha Suri, et al. , 2016) and were stored as compressed Neuroimaging Informatics Technology Initiative (NIFTI) files. , measures of brain structure) of long-term stroke recovery following rehabil … Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Published: 14 September 2021 Dec 8, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. It contains 6000 CT images. 0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability Dec 9, 2021 · Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction 5 days ago · The proposed work resolves these challenges and introduces a new model named an Enhanced Reduce Dimensionality Pattern Convolutional Neural Networks (ERDP-CNN) to improve stroke detection accuracy and efficiency in brain CT images. It is used to predict whether a patient is likely to get stroke based on the input parameters like age, various diseases, bmi, average glucose level and smoking status. However, CT has the disadvantages of exposure to ionizing radiation and the potential to misdiagnose certain diseases [42]. The Jupyter notebook notebook. Kniep, Jens Fiehler, Nils D. The input variables are both numerical and categorical and will be explained below. This suggested study uses a CT scan (computed tomography) image dataset to predict and classify strokes. Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. OASIS-3 and OASIS-4 are the latest releases in the Open Access Series of Imaging Studies (OASIS) that is aimed at making neuroimaging datasets freely available to the scientific community. Therefore, timely detection, diagnosis, and treatment of said medical emergency are urgent requirements to minimize life loss, which is not affordable in any sense. 3. To verify the excellent performance of our method, we adopted it as the dataset. Among the total 2501 images, 1551 belong to healthy individuals while the remainder represent stroke patients. deep-learning pytorch classification image-classification ct-scans image-transformer vision-transformer deit brain-stroke-prediction Jul 4, 2024 · The Brain MRI Segmentation and ISLES datasets are critical image datasets for training algorithms to identify and segment brain structures affected by strokes. 2 and This project firstly aims to classify brain CT images into two classes namely 'Stroke' and 'Non-Stroke' using convolutional neural networks. The available public brain stroke CT scan images are present in either NIFTI file, DICOM format, or JPEG and PNG file formats. 20210317) (Li et al. 4% on the dataset of 192 brain images. 02/20/2018 Stroke is the leading cause of disability in adults, affecting more than 15 million people worldwide each year. Ito1, In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. , 2016). In ischemic stroke lesion analysis, Praveen et al. 3: Sample CT images a) ischemic stroke b) hemorrhagic stroke c) normal II. In addition, three models for predicting the outcomes have been developed. proposed a stacked sparse autoencoder (SSAE) architecture for accurate segmentation of ischemic lesions from MR images and performed perfectly on the publicly available Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset, with an average precision of 0. 0, both featuring high-resolution T1-weighted MRI images accompanied by the corresponding lesion masks. Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. Medical image data is best analysed using models based on Convolutional Neural Networks (CNNs). serious brain issues, damage and death is very common in brain strokes. In addition, up to 2/3 of stroke survivors experience long-term disabilities that impair their participation in daily activities 2,3. Brain_Stroke CT-Images. In this work we present UniToBrain dataset, the very first open-source dataset for CTP. The image of a CT scan is shown in Figure 3. The key to diagnosis consists in localizing and delineating brain lesions. This was mitigated by data augmentation and appropriate evaluation metrics. Feb 20, 2018 · One of these datasets is the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset which includes T1-weighted images from hundreds of chronic stroke survivors with their manually traced lesions. Early prediction of stroke risk plays a crucial role in preventive healthcare, enabling timely interventions and reducing the severity of potential stroke-related complications. 8, pp. APIS was presented as a challenge at the 20th IEEE International Symposium on Biomedical Imaging 2023, where researchers were invited to propose new computational strategies that leverage paired data and deal with lesion Two datasets consisting of brain CT images were utilized for training and testing the CNN models. Apr 21, 2023 · Analyzed a brain stroke dataset using SQL. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% May 15, 2024 · When it comes to finding solutions to issues, deep learning models are pretty much everywhere. Open source computer vision datasets and pre-trained models. detecting strokes from brain imaging data. These antennas are deployed in a fixed circular array around the head, at a distance of approximately 2-3 mm from the head. Accordin g to the studies, it shows the accuracy result is more f or dense datasets . The proposed DCNN model consists of three main Library Library Poltekkes Kemenkes Semarang collect any dataset. Here we present ATLAS (Anatomical Tracings of Lesions Image classification dataset for Stroke detection in MRI scans Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Data Imbalance: The dataset was slightly imbalanced, which could lead to biased results. 1038/sdata. Oct 25, 2024 · This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. In this paper, a review of brain stroke CT images according to the segmentation technique used is presented. Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research 1,2. A Gaussian pulse covering the bandwidth from 0 Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Dec 10, 2022 · Inclusion criteria for the dataset: Subjects 18 years or older who had received MR imaging of the brain for previously diagnosed or suspected stroke were included in this study. This dataset contains over four million train images, a . The results of the experiments are discussed in sub Section 4. , measures of brain Brain stroke, also known as a cerebrovascular accident, is a critical medical condition that requires immediate attention. The images in the data set were as shown in Fig. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. The chapter is arranged as follows: studies in brain stroke detection are detailed in Part 2. 0 will lead to the development of improved lesion segmentation algorithms, facilitating large-scale stroke research. The methodology involves collecting a diverse and balanced dataset of brain scans, preprocessing the data to extract relevant features, training a deep learning model, tuning hyperparameters, and evaluating the Nov 19, 2022 · The proposed signals are used for electromagnetic-based stroke classification. The objective is to accurately classify CT scans as exhibiting signs of a stroke or not, achieving high accuracy in stroke detection based on radiological imaging. Standard stroke examination protocols include the initial evaluation from a non-contrast CT scan to discriminate between hemorrhage and ischemia. Ivanov et al. 0. The dataset consists of over $5000$ individuals and $10$ different input variables that we will use to predict the risk of stroke. In the second stage, the task is making the segmentation with Unet model. However, non-contrast CTs may Sep 14, 2021 · The data set has three categories of brain CT images named: train data, label data, and predict/output data. 1,2 Lesion location and lesion overlap with extant brain structures and networks of interest are consistently reported as key predictors of stroke Keywords: Medical image synthesis · Deep Learning · U-Net · Dataset · Perfusion Map · Ischemic Stroke · Brain CT Scan · DeepHealth 1 Introduction and Clinical Background The occlusion of a cerebral vessel causes a sudden decrease in blood flow in the surrounding vascular territory, in comparison to its centre. However, manual segmentation requires a lot of time and a good expert. Data on image acquisition was stored in an accompanying Oct 1, 2022 · A CNN-based deep learning method, which can detect and classify the type of brain stroke experienced by the patient in the CT images in the dataset obtained from the Ministry of Health of the Republic of Turkey, and also find and predict the location of the stroke by segmentation, has been proposed. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. The role and support of trained neural networks for segmentation tasks is considered as one of the best assistants The model is trained on a dataset of CT scan images to classify images as either "Stroke" or "No Stroke". read more Dec 12, 2022 · The data format and organization follows Brain Imaging Data Structure (BIDS) guidelines. as compar ed with Nov 18, 2024 · Among all the datasets, missing values has been spotted in the brain stroke dataset only. Better methods for early detection are crucial due to the concerning increase in the number of people suffering from brain stroke. The images in the dataset have a resolution of 650 × 650 pixels and are stored as JPEGs. Stroke is the leading cause of long-term disability which significantly changes the patient’s life. Description: Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. After the stroke, the damaged area of the brain will not operate normally. csv file containing images with the type of acute hemorrhage in a column and probability of the type present in the other column, and over four hundred thousand test images. g. The dataset includes: 955 T1-weighted MRI scans, divided into a training dataset (n=655 T1w MRIs with manually-segmented lesion masks) and a test dataset (n=300 T1w MRIs only; lesion masks not released) Dataset: • The "Brain Stroke CT Image Dataset," where the information from the hospital's CT or MRI scanning reports is saved, serves as the source of the data for the input. May 17, 2022 · This dataset contains the trained model that accompanies the publication of the same name: Anup Tuladhar*, Serena Schimert*, Deepthi Rajashekar, Helge C. Since the dataset is small, the training of the entire neural network would not provide good results so the concept of Transfer Learning is used to train the model to get more accurate resul Apr 3, 2024 · A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Feb 20, 2018 · Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Nowadays, with the advancements in Artificial . However, while doctors are analyzing each brain CT image, time is running Brain stroke prediction dataset. The proposed work explored the effectiveness of CNN models, including ResNet, DenseNet, EfficientNet, and VGG16, for the differentiation of stroke and no-stroke cases. Anglin1,*, Nick W. • •Dataset is created by collecting the CT or MRI Scanning reports from a multi-speaciality hospital from various branches like Mumbai, Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. These two tasks enable participants to start working on brain CTA, a modality rarely available in public datasets, combining imaging and clinical variables and addressing critical medical needs in stroke care. In this study, we present a novel DCNN model for the early detection of brain stroke using CT scan images. Jan 1, 2024 · The Brain Stroke CT Image Dataset (Rahman, 2023) includes images from stroke-diagnosed and healthy individuals. Both of this case can be very harmful which could lead to serious injuries. 2021) was to perform the segmentation of stroke lesions using computed tomography perfusion (CTP) images, guided by annotations derived from DWI images, which are considered the standard image modalities. The main topic about health. 2 and 2. When we classified the dataset with OzNet, we acquired successful performance. Add a description, image, and links to the brain-stroke topic page so that developers can more easily learn about it. Scientific Data , 2018; 5: 180011 DOI: 10. The accuracy achieved by them was 93. Mar 25, 2024 · The Anatomical Tracings of Lesions After Stroke (ATLAS) datasets are available in two versions: 1. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This challenge is divided into two tasks: (1) LVO detection and (2) Brain Reperfusion Prediction. Feb 20, 2018 · Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. The identification accuracy of stroke cases is further enhanced by applying transfer learning from pre-trained models and data augmentation techniques. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. The dataset was sourced from Kaggle, and the project uses TensorFlow for model development and Tkinter for a user-friendly interface. Aug 23, 2023 · To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. We systematically Sep 1, 2022 · The dataset collected for the study consisted of 300 normal brain, 300 hemorrhagic stroke, 300 ischemic stroke images collected from 74 patients. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. Our primary objective is to develop a robust predictive model for identifying potential brain stroke occurrences, a Mar 25, 2022 · Brain computed tomography (CT) is commonly used for evaluating the cerebral condition, but immediately and accurately interpreting emergent brain CT images is tedious, even for skilled neuroradiologists. However, existing DCNN models may not be optimized for early detection of stroke. Version 1 comprises a total of 304 cases, whereas version 2 is more extensive, containing 955 cases. The identification of Feb 20, 2018 · A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. The proposed method established a specific procedure of scratch training for a particular scanner, and the transfer learning succeeded in enabling Over the last few decades, a lot of databases/datasets including Brain Stroke CT scan image datasets were published in different publically available repositories for public use. Immediate attention and diagnosis play a crucial role regarding patient prognosis. The dataset used in the development of the method was the open-access Stroke Prediction dataset. The collection includes diverse metadata, comprised of demographic information, basic clinical profile (NIH Stroke Scale/Score (NIHSS), hospitalization duration, blood pressure at admission, BMI, and associated health conditions), and expert description of Jan 31, 2025 · To begin the process of early brain stroke detection, a dataset comprising brain images, including samples from both stroke-affected and normal brains, is gathered. An image such as a CT scan helps to visually see the whole picture of the brain. Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. Article CAS Google Scholar This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Brain Stroke Dataset Classification Prediction. This study proposed the use of convolutional neural network (CNN May 1, 2023 · The dataset was structured in line with the Brain Imaging Dataset Structure (BIDS) format (Gorgolewski et al. • Each 3D volume in the dataset has a shape of ( 197, 233, 189 ). Dec 11, 2021 · A larger dataset of stroke T1w MRIs and manually segmented lesion masks that includes training, test, and generalizability datasets are presented, anticipating that ATLAS v2. Among the several medical imaging modalities used for brain imaging Jan 1, 2021 · Experiments using our proposed method are analyzed on brain stroke CT scan images. A Convolutional Neural Network (CNN) is used to perform stroke detection on the CT scan image dataset. Motor imagery (MI) technology based on brain-computer Apr 27, 2024 · In recent years, deep convolutional neural network (DCNN) models have shown great promise in the automated detection of brain stroke from CT scan images. Scientific data, 5(1):1–11, 2018. The patients underwent diffusion-weighted MRI (DWI) within 24 hours after taking the CT. The Brain MRI Segmentation and ISLES datasets are critical image datasets for training algorithms to identify and segment brain structures affected by strokes. , measures of Contribute to ricardotran92/Brain-Stroke-CT-Image-Dataset development by creating an account on GitHub. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. source dataset of stroke anatomical brain images and manual lesion segmentations Sook-Lei Liew1,*, Julia M. Oct 12, 2017 · A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. The Cerebral Vasoregulation in Elderly with Stroke dataset provides valuable insights into cerebral blood flow regulation post stroke, useful for both tabular analysis and image-based We anticipate that ATLAS v2. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. RELEVANT WORK The majority of strokes are seen as ischemic stroke and hemorrhagic stroke and are shown in Fig. Acute ischemic stroke dataset contains 397 Non-Contrast-enhanced CT (NCCT) scans of acute ischemic stroke with the interval from symptom onset to CT less than 24 hours. A USC-led team has compiled and shared one of the largest open-source datasets of brain scans from stroke patients, the NIH-supported Anatomical Tracings of Jan 24, 2023 · This dataset was divided into three 80%/20% groups (train, validation, and test) and contained 993 healthy images and 610 stroke cases for the training category; 240 healthy images and 146 stroke cases; and 313 healthy images and 189 stroke cases for test. The Cerebral Vasoregulation in Elderly with Stroke dataset provides valuable insights into cerebral blood flow regulation post stroke, useful for both tabular analysis and image-based Sep 4, 2024 · A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. 2018. Future Direction: Incorporate additional types of data, such as patient medical history, genetic information, and clinical reports, to enhance the predictive accuracy and reliability of the model. 0 will lead to improved algorithms, facilitating large-scale stroke research. , measures of brain structure) of long-term stroke recovery following rehabilitation. In the study, 2 experiments were performed using image fusion and CNN. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation Jan 1, 2023 · In this chapter, deep learning models are employed for stroke classification using brain CT images. The models were trained and evaluated using a real-time dataset of brain MR Images. In the brain stroke dataset, the BMI column contains some missing values which could have been filled Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. As a result, early detection is crucial for more effective therapy. Sep 30, 2024 · The primary objective of the ISLES 2018 dataset (Cereda et al. Segmentation of the affected brain regions requires a qualified specialist. Oct 1, 2022 · The image dataset for the proposed classification model consists of 1254 grayscale CT images from 96 patients with acute ischemic stroke (573 images) and 121 normal controls (681 images). Moreover, the Brain Stroke CT Image Dataset was used for stroke classification. The dataset encompasses information from 103 acute ischemic Fig. This paper introduces the use of facial image dataset containing neutral and smiling expressions to classify Aug 7, 2022 · A precise and quick diagnosis, in a context of ischemic stroke, can determine the fate of the brain tissues and guide the intervention and treatment in emergency conditions. Lesion location and lesion overlap with extant brain structures and networks of interest are consistently reported as key predictors of stroke outcomes 3–6. Dec 22, 2023 · When vessels present in brain burst or the blood supply to the brain is blocked, brain stroke occurs in human body. Approximately 795,000 people in the United States suffer from a stroke every year, resulting in nearly 133,000 deaths 1. Banks1, Matt Sondag1, Kaori L. Forkert, "Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks," in IEEE Access, vol. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI As of today, the most successful examples of open-source collections of annotated MRIs are probably the brain tumor dataset of 750 patients included in the Medical Segmentation Decathlon (MSD) 17, used in the Brain Tumor Image Segmentation (BraTS) challenge, and the FastMRI+ 18, a collection of about 7 thousand brain MRIs, with diverse There is a dataset available online provided by Research Society of North America (RSNA). Feb 21, 2025 · Motor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Brain stroke prediction dataset. Large datasets are therefore imperative, as well as fully automated image post- … Sep 26, 2023 · Stroke is the second leading cause of mortality worldwide. Feature Dimensionality for SVM: Flattening images increased feature dimensionality, impacting SVM performance. Brain stroke is one of the global problems today. Images were converted using dcm2niix (version 1. Mar 8, 2024 · Here are three potential future directions for the "Brain Stroke Image Detection" project: Integration with Multi-Modal Data:. Sci. Using a dataset from Kaggle with labelled CT scans for 2,500 stroke cases and 2,500 non-stroke cases (each image Jan 10, 2025 · Through this study, a strategy for identifying brain stroke disease using deep learning techniques and image preprocessing is provided. Data 5, 1–11 (2018). 11 Cite This Page : Subject terms: Brain, Magnetic resonance imaging, Stroke, Brain imaging. The dataset used in this project is taken from Teknofest2021-AI in Medicine competition. Stroke is a medical emergency resulting from disruption of blood supply to different parts of the brain which leads to facial weakness and paralysis as the brain is the control center. The deep learning techniques used in the chapter are described in Part 3. By compiling and freely distributing this multimodal dataset generated by the Knight ADRC and its affiliated studies, we hope to facilitate future This work introduced APIS, the first paired public dataset with NCCT and ADC studies of acute ischemic stroke patients. Oct 16, 2023 · A brain stroke, commonly called as a cerebral vascular accident (CVA) is one of the deadliest diseases across the globe and may lead to various physical impairments or even death. Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. 968, average Dice coefficient (DC) of Nov 8, 2017 · The Anatomical Tracings of Lesions After Stroke (ATLAS) dataset [20] is a challenging 3D medical image dataset. Showing projects matching "class:stroke" by subject, page 1. Magnetic resonance imaging (MRI) techniques is a commonly available imaging modality used to diagnose brain stroke. Background & Summary. Aug 22, 2023 · We present a public dataset of 2,888 multimodal clinical MRIs of patients with acute and early subacute stroke, with manual lesion segmentation, and metadata. ipynb contains the model experiments. The obtained accuracies highlight the potential … Feb 20, 2018 · "MRI stroke data set released by USC research team" - EurekAlert!. Implementation of DeiT (Data-Efficient Image Transformer) for accurate and efficient brain stroke prediction using deep learning techniques. In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset. 3. Dec 1, 2021 · Brain stroke computed tomography images analysis using image processing: A review. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e. Apr 29, 2020 · This 874 035-image, multi-institutional, and multinational brain hemorrhage CT dataset is the largest public collection of its kind that includes expert annotations from a large cohort of volunteer neuroradiologists for classifying intracranial hemorrhages. jjpjo odzmn cflni agxax dwaf iaq saap giuf izajxs eyoe vuvutgvt hwqfpx vindy bhv uyffwen