Detection Of Abnormalities Research Articles

ADVANCED SHOPLIFTING PREVENTION AND ALERT STSYEM

Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using YOLOv7, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed. We can use camera for Human Motion Detection. The Camera is used to catch the live images of the area in which it is being implemented, if any object is moving. The captured images are stored for further work. If motion is found in this video, the computer will start recording, buzz an alarm and send SMS to people listed in its database. In this way the system will provide the security against any misdeed. Keywords: YOLOv7, SMS, E-mail, CCTV, SMTP, RFID

Ultra-low-dose vs. standard-of-care-dose CT of the chest in patients with post-COVID-19 conditions—a prospective intra-patient multi-reader study

Abstract Objectives To conduct an intrapatient comparison of ultra-low-dose computed tomography (ULDCT) and standard-of-care-dose CT (SDCT) of the chest in terms of the diagnostic accuracy of ULDCT and intrareader agreement in patients with post-COVID conditions. Methods We prospectively included 153 consecutive patients with post-COVID-19 conditions. All participants received an SDCT and an additional ULDCT scan of the chest. SDCTs were performed with standard imaging parameters and ULDCTs at a fixed tube voltage of 100 kVp (with tin filtration), 50 ref. mAs (dose modulation active), and iterative reconstruction algorithm level 5 of 5. All CT scans were separately evaluated by four radiologists for the presence of lung changes and their consistency with post-COVID lung abnormalities. Radiation dose parameters and the sensitivity, specificity, and accuracy of ULDCT were calculated. Results Of the 153 included patients (mean age 47.4 ± 15.3 years; 48.4% women), 45 (29.4%) showed post-COVID lung abnormalities. In those 45 patients, the most frequently detected CT patterns were ground-glass opacities (100.0%), reticulations (43.5%), and parenchymal bands (37.0%). The accuracy, sensitivity, and specificity of ULDCT compared to SDCT for the detection of post-COVID lung abnormalities were 92.6, 87.2, and 94.9%, respectively. The median total dose length product (DLP) of ULDCTs was less than one-tenth of the radiation dose of our SDCTs (12.6 mGy*cm [9.9; 15.5] vs. 132.1 mGy*cm [103.9; 160.2]; p < 0.001). Conclusion ULDCT of the chest offers high accuracy in the detection of post-COVID lung abnormalities compared to an SDCT scan at less than one-tenth the radiation dose, corresponding to only twice the dose of a standard chest radiograph in two views. Clinical relevance statement Ultra-low-dose CT of the chest may provide a favorable, radiation-saving alternative to standard-dose CT in the long-term follow-up of the large patient cohort of post-COVID-19 patients.

Method for Detecting Pathology of Internal Organs Using Bioelectrography

This article considers the possibility of using the bioelectrography method to identify the pathology of internal organs. It is shown that with the currently existing methods, there is no possibility of the automatic detection of diseases or abnormalities in the functioning of a particular organ, or of the definition of combined pathology. It has been revealed that the use of various classifiers makes it possible to expand the field of pathology and choose the most optimal method for determining a particular disease. Based on this, a method for detecting the pathology of internal organs is developed, as well as a software package that allows the detection of diseases of the internal organs based on the bioelectrography results. Machine-learning models such as logistic regression, decision tree, random forest, xgboost, KNN, SVM and HyperTab are used for this purpose. HyperTab, logistic regression and xgboost turn out to be the best among them for this task, achieving a performance according to the f1-score metric in the order of 60–70%. The use of the developed method will, in practice, allow us to switch to combining various machine-learning models for the identification of certain diseases, as well as for the identification of combined pathology, which will help solve the problem of detecting pathology during screening studies and lead to a reduction in the burden on the staff of medical institutions.

EyeXNet: Enhancing Abnormality Detection and Diagnosis via Eye-Tracking and X-ray Fusion

Integrating eye gaze data with chest X-ray images in deep learning (DL) has led to contradictory conclusions in the literature. Some authors assert that eye gaze data can enhance prediction accuracy, while others consider eye tracking irrelevant for predictive tasks. We argue that this disagreement lies in how researchers process eye-tracking data as most remain agnostic to the human component and apply the data directly to DL models without proper preprocessing. We present EyeXNet, a multimodal DL architecture that combines images and radiologists’ fixation masks to predict abnormality locations in chest X-rays. We focus on fixation maps during reporting moments as radiologists are more likely to focus on regions with abnormalities and provide more targeted regions to the predictive models. Our analysis compares radiologist fixations in both silent and reporting moments, revealing that more targeted and focused fixations occur during reporting. Our results show that integrating the fixation masks in a multimodal DL architecture outperformed the baseline model in five out of eight experiments regarding average Recall and six out of eight regarding average Precision. Incorporating fixation masks representing radiologists’ classification patterns in a multimodal DL architecture benefits lesion detection in chest X-ray (CXR) images, particularly when there is a strong correlation between fixation masks and generated proposal regions. This highlights the potential of leveraging fixation masks to enhance multimodal DL architectures for CXR image analysis. This work represents a first step towards human-centered DL, moving away from traditional data-driven and human-agnostic approaches.

Anomaly detection in wind turbine blades based on PCA and convolutional kernel transform models: employing multivariate SCADA time series analysis

With the widespread application of wind power technology, the detection of abnormalities in wind turbine blades has become a key research area. The use of data from monitoring and data acquisition (SCADA) systems for data-driven fault detection research presents new challenges. This study utilizes short-term SCADA data from wind turbine generators to classify the blade abnormal and normal operational states, thereby introducing a new method called PCABSMMR. This strategy integrates principal component analysis (PCA) and borderline-synthetic minority over-sampling technique (Borderline-SMOTE) for data processing and utilizes an improved multi-dimensional time series classification (MTSC) model. It combines one-dimensional convolution from deep learning with shallow learning’s rigid classifiers. PCA is used for dimensionality reduction, while Borderline-SMOTE expands the samples of minority class fault instances. Comparative analysis with various methods shows that the proposed method has an average F1-score of 0.98, outperforming many state-of-the-art MTSC models across various evaluation metrics.

Explainable AI for Abnormal Human Activity Detection in Surveillance Videos: A Review

This review paper examines the integration of Explainable AI (XAI) techniques into abnormal human activity detection from surveillance videos, emphasizing their significance in enhancing transparency, accountability, and trustworthiness in AI-powered surveillance systems. Through an exploration of XAI methods such as LIME, SHAP, and attention mechanisms, we discuss how these techniques provide insights into the decision-making process of AI models, enabling stakeholders to understand and interpret model predictions. Furthermore, we highlight the advantages of using XAI for debugging errors, identifying biases, and increasing trust among human operators in various security contexts. Additionally, we discuss ongoing research directions in XAI for abnormal human activity detection, including the development of advanced explanation techniques and the incorporation of user feedback into the explanation process. By fostering collaboration between humans and AI, XAI holds the potential to enhance the effectiveness and responsible deployment of surveillance systems, contributing to safer and more secure environments for all stakeholders.

Time-of-Flight PET/CT Imaging of Ga-68-Dotatate: Normal Pattern, SUV Quantification, and Differences from Non-Time-of-Flight Imaging

Purpose The biodistribution of gallium-68-dotatate (Ga-68-dotatate) and standardized uptake values (SUVs) using non-time-of-flight (TOF) positron emission tomography/computed tomography (PET/CT) cameras is well established. However, with the eventual retirement of older PET cameras and their replacement with newer, highly sensitive TOF PET/CT cameras, where SUVmax measurements are reportedly higher, updated knowledge of normal SUVmax range is needed and, to our knowledge, not previously reported. Our objectives are as follows: Methods Fifty consecutive patients referred routinely to our nuclear medicine service (20 men, 30 women; median age 55 years) with presumed neuroendocrine tumors underwent Ga-68-dotatate scans on a PET-CT camera having capability of reconstructing both TOF/non-TOF images. Region of interests (ROIs) were drawn around 24 normal structures as well as the primary lesion with abnormal radiotracer uptake and SUVmax was measured. The same ROI was analyzed using both algorithms simultaneously and both TOF and non-TOF SUVmax values were compared. Results Twelve hundred ROIs were evaluated. Non-TOF Ga-68-dotatate uptake in normal structures was in alignment with previously published studies. As compared to non-TOF, TOF images had better target to background ratios visually. TOF SUVmax was higher for all structures except for lung and brain. TOF SUVmax was more than double in adrenals/uncinate process of the pancreas; approximately 1.8 times in abnormal lesions, lymph nodes, pineal gland; and greater than 1.5 times in thyroid, breast, and pancreatic head. Conclusion Normal database of Ga-68-dotatate TOF SUVmax is provided for common structures to aid visual detection of abnormalities objectively. Overall, TOF SUVmax measures higher in identical ROIs, with abnormal lesions measuring approximately 1.8 times higher versus non-TOF technology. These findings need to be taken in consideration when comparing patient scans imaged on different PET/CT technologies.

PSIII-3 Evaluation of remote detection of heart rate, respiratory rate, and body temperature for improved detection of bovine respiratory disease in high-risk receiving calves

Abstract The objective of this study was to evaluate the effectiveness of the VetGuardian remote monitor to create data-driven detection of bovine respiratory disease (BRD) by identifying vital sign abnormalities for early disease detection. Male calves (n = 20, initial body weight = 213 ± 3 kg) were purchased from regional auction markets and shipped to the University of Arkansas Stocker Receiving Unit. Upon arrival, calves were weighed, branded, received an identification tag, vaccinated, dewormed, and bulls were castrated. On d 1, the remote sensor was used to monitor all calves while restrained in the chute system detecting their body temperatures, and heart and respiratory rates. The remote sensor was placed in 1 of 2 locations for recordings: 1) in front of chute to capture the head, or 2) on the side of chute capturing the torso. Each calf was assigned to a scan location (10 calves/scan location). For all calves, rectal temperatures were also recorded manually, and heart and respiratory rates were determined by thoracic auscultation. Before processing and for the following 28 d, cattle were observed daily for clinical BRD. If presenting symptoms of BRD and if rectal temperature was ≥ 40°C, cattle were deemed morbid and treated with an antibiotic according to a standard preplanned protocol. For any cattle that were examined for BRD the remote sensor was again used for a minimum of 5 min to compare with manual recordings. Statistical analyses were performed using the CORR, MIXED, and MEANS procedures of SAS 9.4 with sex as a random effect and individual calf specified as the subject. There was a positive correlation between manual recordings of rectal temperatures and remote recordings from the head (0.67; P = 0.04) and the side (0.88; P = 0.0008). Remote recordings did not correlate (P ≥ 0.21) with manual recordings for heart rates. However, there was a positive correlation (0.79; P = 0.01) between manual respiratory rates and the side scans, but no relationship (P = 0.16) between manually collected respiratory rates with head scans. LSMeans for temperature data collected manually or by the remote sensor at either location did not differ (P ≥ 0.34). LSMeans for heart rate differed between manual and remote measurements from either location (P ≤ 0.0008). Manual measurements for respiratory rate did not differ (P = 0.26) from remote recordings from the side; however, differed (P < 0.0001) from recordings from the head. In conclusion, the VetGuardian remote sensor varied from manual recordings for heart rates, regardless of sensor location. Scan location had an influence on accuracy of respiratory rate recordings, and body temperature recordings from either location did not differ from manual measurements. Preliminary data should be used for further investigation of this remote sensor.

Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: A comprehensive study

Background and ObjectiveThe thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-invasive imaging technique utilizing infrared cameras, has emerged as a diagnostic tool for thyroid-related conditions, offering advantages such as early detection and risk stratification. Artificial intelligence (AI) has demonstrated success in medical diagnostics, and its integration into thermal imaging analysis holds promise for improving diagnostic capabilities. This study aims to explore the potential of AI, specifically convolutional neural networks (CNNs), in enhancing the analysis of thyroid thermograms for the detection of nodules and abnormalities. MethodsArtificial intelligence (AI) and machine learning techniques are integrated to enhance thyroid thermal image analysis. Specifically, a fusion of U-Net and VGG16, combined with feature engineering (FE), is proposed for accurate thyroid nodule segmentation. The novelty of this research lies in leveraging feature engineering in transfer learning for the segmentation of thyroid nodules, even in the presence of a limited dataset. ResultsThe study presents results from four conducted studies, demonstrating the efficacy of this approach even with a limited dataset. It's observed that in study 4, using FE has led to a significant improvement in the value of the dice coefficient. Even for the small size of the masked region, incorporating radiomics with FE resulted in significant improvements in the segmentation dice coefficient. It's promising that one can achieve higher dice coefficients by employing different models and refining them. ConclusionThe findings here underscore the potential of AI for precise and efficient segmentation of thyroid nodules, paving the way for improved thyroid health assessment.

Unsupervised anomaly detection for power batteries: A temporal convolution autoencoder framework

Abstract To prevent potential abnormalities from escalating into critical faults, a rapid and precise algorithm should be employed for detecting power battery anomalies. An unsupervised model based on temporal convolutional autoencoder (TCAE) that can quickly and accurately identify abnormal power battery data was proposed. Its encoder utilized a temporal convolutional network (TCN) structure with residuals to parallelly process data while capturing time dependencies. A novel TCN structure with an effect–cause relationship was developed for the decoder. The same-time-scale connection was established between the encoder and decoder to improve the model performance. The validity of the proposed model was confirmed using a real-world car dataset. Compared with the GRU-AE model, the proposed approach reduced the parameter count and mean square error by 19.5% and 71.9%, respectively. This study provides insights into the intelligent battery pack abnormality detection technology.

Prenatal diagnosis of a trisomy 7 mosaic case: CMA, CNV-seq, karyotyping, interphase FISH, and MS-MLPA, which technique to choose?

ObjectiveThis study aims to perform a prenatal genetic diagnosis of a high-risk fetus with trisomy 7 identified by noninvasive prenatal testing (NIPT) and to evaluate the efficacy of different genetic testing techniques for prenatal diagnosis of trisomy mosaicism.MethodsFor prenatal diagnosis of a pregnant woman with a high risk of trisomy 7 suggested by NIPT, karyotyping and chromosomal microarray analysis (CMA) were performed on an amniotic fluid sample. Low-depth whole-genome copy number variation sequencing (CNV-seq) and fluorescence in situ hybridization (FISH) were used to clarify the results further. In addition, methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) was performed to analyze the possibility of uniparental disomy(UPD).ResultsAmniotic fluid karyotype analysis revealed a 46, XX result. Approximately 20% mosaic trisomy 7 was detected according to the CMA result. About 16% and 4% of mosaicism was detected by CNV-seq and FISH, respectively. MS-MLPA showed no methylation abnormalities. The fetal ultrasound did not show any detectable abnormalities except for mild intrauterine growth retardation seen at 39 weeks of gestation. After receiving genetic counseling, the expectant mother decided to continue the pregnancy, and follow-up within three months of delivery was normal.ConclusionIn high-risk NIPT diagnosis, a combination of cytogenetic and molecular genetic techniques proves fruitful in detecting low-level mosaicism. Furthermore, the exclusion of UPD on chromosome 7 remains crucial when NIPT indicates a positive prenatal diagnosis of trisomy 7.

Enhancing Yarn Quality Wavelength Spectrogram Analysis: A Semi-Supervised Anomaly Detection Approach with Convolutional Autoencoder

Abnormal detection plays a pivotal role in the routine maintenance of industrial equipment. Malfunctions or breakdowns in the drafting components of spinning equipment can lead to yarn defects, thereby compromising the overall quality of the production line. Fault diagnosis of spinning equipment entails the examination of component defects through Wavelet Spectrogram Analysis (WSA). Conventional detection techniques heavily rely on manual experience and lack generality. To address this limitation, this current study leverages machine learning technology to formulate a semi-supervised anomaly detection approach employing a convolutional autoencoder. This method trains deep neural networks with normal data and employs the reconstruction mode of a convolutional autoencoder in conjunction with Kernel Density Estimation (KDE) to determine the optimal threshold for anomaly detection. This facilitates the differentiation between normal and abnormal operational modes without the necessity for extensive labeled fault data. Experimental results from two sets of industrial data validate the robustness of the proposed methodology. In comparison to conventional Autoencoder and prevalent machine learning techniques, the proposed approach demonstrates superior performance across evaluation metrics such as Accuracy, Recall, Area Under the Curve (AUC), and F1-score, thereby affirming the feasibility of the suggested model.

MINM: Marine Intelligent Netting Monitoring Using Multi-Scattering Model and Multi-Space Transformation

Marine intelligent net tank aquaculture monitoring plays an important role in improving aquaculture efficiency, environmental monitoring efficiency, and environmental safety. The underwater environment has complex light, often with problems such as scattering and absorption, resulting in poor image quality, making it difficult to accurately analyze and judge the aquaculture environment. Improving marine intelligent net tank aquaculture monitoring has the following three advantages: 1) better observation and monitoring of the aquaculture process, timely detection of problems and abnormalities, to protect the benefits of aquaculture and product quality. 2) more convenient and rapid monitoring of the aquaculture environment, improving monitoring efficiency and reducing monitoring costs. 3) effective monitoring of the underwater environment around the farm, and timely detection of foreign pollution, harmful substances, and other problems, to protect the safety of the aquaculture environment. Therefore, in order to solve the two degradation problems of scattering and absorption in the process of marine smart net farm monitoring, we propose a marine smart net farm monitoring method using multiple scattering models and multiple spatial transformations, called MINM. Specifically, inspired by the image chromatic aberration correction method, we design a color correction method in the multicolor space, which is implemented by using the Lab and RGB color space by performing contrast-constrained adaptive histogram equalization and gray world assumptions, respectively, to correct color shifts in different color models. Based on this, we propose a de-scattering method using a multi-scattering model, which eliminates the effect of scattering on underwater imaging by embedding a complete multi-scattering underwater imaging model to guide the extraction of different features in the multi-scattering model. To obtain more qualified results, we also propose an efficient perceptual fusion to mix the output of the de-scattering and color correction. Thus, our method can take advantage of multiple scattering models and multiple spatial transformations to effectively improve the visual quality of underwater images, producing enhanced results that fit the complete underwater imaging model and have bio-visual characteristics. In extensive experimental demonstrations, our MINM method has shown higher performance than the state-of-the-art methods in terms of both visual quality and quantitative metrics. All experimental results and datasets in this paper are available from the following website: https://github.com/An-Shunmin/MINM.

Multi-Branch GAN-Based Abnormal Events Detection via Context Learning in Surveillance Videos

Multi-Branch GAN-Based Abnormal Events Detection via Context Learning in Surveillance Videos

Real-time Abnormal Detection of GWAC Light Curve based on Wavelet Transform Combined with GRU-Attention

Nowadays, astronomy has entered the era of Time-Domain Astronomy, and the study of the time-varying light curves of various types of objects is of great significance in revealing the physical properties and evolutionary history of celestial bodies. The Ground-based Wide Angle Cameras telescope, on which this paper is based, has observed more than 10 million light curves, and the detection of anomalies in the light curves can be used to rapidly detect transient rare phenomena such as microgravity lensing events from the massive data. However, the traditional statistically based anomaly detection methods cannot realize the fast processing of massive data. In this paper, we propose a Discrete Wavelet (DW)-Gate Recurrent Unit-Attention (GRU-Attention) light curve warning model. Wavelet transform has good effect on data noise reduction processing and feature extraction, which can provide richer and more stable input features for a neural network, and the neural network can provide more flexible and powerful output model for wavelet transform. Comparison experiments show an average improvement of 61% compared to the previous pure long-short-term memory unit (LSTM) model, and an average improvement of 53.5% compared to the previous GRU model. The efficiency and accuracy of anomaly detection in previous paper work are not good enough, the method proposed in this paper possesses higher efficiency and accuracy, which incorporates the Attention mechanism to find out the key parts of the light curve that determine the anomalies. These parts are assigned higher weights, and in the actual anomaly detection, the star is detected with 83.35% anomalies on average, and the DW-GRU-Attention model is compared with the DW-LSTM model, and the detection result f1 is improved by 5.75% on average, while having less training time, thus providing valuable information and guidance for astronomical observation and research.

Eye Abnormalities Detection Using Machine Learning

Abstract: The most common cause of irreversible visualimpairment and incapacity around the world is glaucoma. In any case, the larger part of patients are uninformed of their condition. Despite headways in innovation, diagnosing the movement of glaucoma remains a challengein clinical hone. Observing glaucoma movement ordinarilyincludes a manual examination of the retinal layer, which is time-consuming. This issue can be tended to by computerizing glaucoma conclusions utilizing profound learning and machine learning strategies. A comprehensive survey of various computerized glaucoma forecast and discovery strategies was conducted in this orderly audit. Over 100 papers on machine learning (ML) strategies were analyzed, covering outlines, strategies, goals, execution, benefits, and downsides, with clear charts and tables. Machine learning approaches such as the Resnet calculation and Convolutional Neural Organize are commonly utilized for diagnosing and foreseeing glaucoma. Through precise audits, the most solid strategy for glaucoma discovery and forecast can be distinguished to improve future treatments. Cataracts are the driving cause of visual This ponder givesa comprehensive outline of current headways in machine learning strategies for assessing and classifying cataracts utilizing ophthalmic pictures. The study highlights the qualities and shortcomings of existing investigations and addresses challenges related to independent cataract classification and evaluating utilizing machine learningprocedures, advertising potential arrangements forencourage examination

Abnormal Monitoring Data Detection Based on Matrix Manipulation and the Cuckoo Search Algorithm

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3-σ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3-σ method.

Idiopathic polyhydramnios and postnatal outcomes of children: The role of exome sequencing.

The objective of our study was to evaluate the long-term outcome of children born from a pregnancy complicated by idiopathic polyhydramnios. The secondary objective was to investigate factors associated with adverse outcomes. We conducted a retrospective study in two prenatal diagnosis centers between January 1, 2009 and December 31, 2020. Inclusion criteria were pregnancies with a diagnosis of idiopathic polyhydramnios, defined by a deepest pocket greater than 8cm, no detectable abnormality at ultrasound and a negative amniotic fluid assessment including karyotype, chromosomal microarray, biochemical assays (electrolytes and digestive enzymes), and viruses (parvovirus B19 and cytomegalovirus). One-year outcomes of these children were collected. The primary endpoint was adverse postnatal outcome, defined by at least one of the following criteria: stillbirth, neonatal death, or serious and incurable condition diagnosed in the first year of life. Of the 245 women referred for isolated polyhydramnios, 73 were diagnosed with idiopathic polyhydramnios after prenatal investigations. The mean age at follow-up of children was 28months (95% CI 20-36). An adverse outcome occurred in 25% of cases (18/73), with one stillbirth, two neonatal deaths, and 15 severe conditions diagnosed postnatally, including a rate of monogenic disorders of 8.2% (6/73). Pediatric follow-up was normal for 75% of the children (55/73), including a rate of 9% (5/55) of curable conditions. Repeated amnioreduction was independently associated with an adverse outcome. Pregnant women with polyhydramnios should be informed of the increased risk of 25% of perinatal mortality and serious conditions diagnosed after birth.

Cracking The Case of The Cryptic Coronary: Diagnostic Strategies for Anomalous RCA Originating from LAD

Background: Coronary artery anomalies are typically congenital, though they may remain undetected until routine cardiac catheterization is performed. With a detection rate of only 1.3%, these anomalies often go unnoticed. In rare cases, the right coronary artery may originate from the left coronary system, but this anomaly is generally benign. Case presentation: A 55-year-old male patient was diagnosed with NSTEMI and underwent coronary angiography. However, despite multiple attempts, the medical team could not cannulate into the right coronary ostium. The left coronary ostium was located in the left sinus of Valsalva. Selective left coronary arteriography revealed normal courses of the left main and LAD but identified a critical stenosis in the LCx and a possible CTO at the RCA ostium. The patient received a stent in the LCx and returned the following month for another angiography, during which he received a stent in the proximal LAD. However, during cine angiography, it was found that the patient had an anomalous RCA that originated from the mid-portion of LAD, coursing anteriorly to the pulmonary artery and down to the right atrioventricular groove. Subsequent CCTA confirmed the RCA's origin from the mid-LAD. Conclusion: The report finds a rare RCA origin in the mid-LAD. The heart and its complex arterial network may be examined thoroughly, appreciate to coronary CTA, a non-invasive imaging approach. This allows for effective interventional cardiovascular therapy and is helpful for medical cardiac abnormality detection and assessment. Keyword: Anomalous Right Coronary Artery, CT Coronary Angiography, RCA from LAD, Single Coronary Artery.

Video anomaly detection: A systematic review of issues and prospects

The increase in the deployment of surveillance camera in outdoor and indoor settings have resulted in a growing demand for intelligent systems that can accurately detect and recognize human actions as well as other entities of interest within the captured video data. Although, human action recognition is a well-established topic in computer vision, abnormal behaviour detection has recently received increased research attention. Several abnormal behaviour detection systems have been proposed over the years to ensure human safety. However, only a few comprehensive and systematic reviews report on the current state and future direction of video anomaly detection(VAD) research. This present effort aims to contribute a systematic and detailed review of current research and advances in the detection of anomalous actions and entities in videos. The review focuses on studies published between 2003 and 2023. During the literature selection process, 530 scholarly articles were identified and evaluated to showcase prevalent research trends, techniques, datasets, and frameworks within the realm of VAD This review aims to offer a comprehensive understanding of key areas of focus among researchers, provide resources for commonly used public datasets for evaluation and experimentation, and examine advancements and integration of network design to accommodate the needs for handling multimedia information. To sum up, this study highlights various potential opportunities and obstacles about the VAD domain.VAD has gained significant interest in recent years due to its potential applications in various domains such as security, surveillance, traffic monitoring, medical imaging among others. Areas of further research include, Anomaly detection in real-time, multi-camera anomaly detection, privacy preserving in VAD to mention a few. The study revealed some key trends and challenges in VAD that can guide future research direction.