Classification Of Anomalies Research Articles

Comparing the Diagnostic Efficacy of 3D Ultrasound and MRI in the Classification of Müllerian Anomalies.

Müllerian anomalies significantly impact female reproductive health. This study aims to compare the diagnostic efficacy of three-dimensional ultrasound (3D-US) and magnetic resonance imaging (MRI) in detecting and classifying these anomalies. A retrospective analysis of 150 patients with Müllerian anomalies was conducted at Saveetha Medical College and Hospital from March 2018 to March 2024. MRI and 3D-US examinations were performed and analyzed independently by two radiologists. Anomalies were classified according to European Society of Human Reproduction and Embryology (ESHRE)/European Society for Gynaecological Endoscopy (ESGE) and American Society for Reproductive Medicine (ASRM) guidelines. The septate uterus was the most prevalent anomaly, observed in 53 patients (35.3%). MRI demonstrated superior diagnostic accuracy (AUC 0.92) compared to 3D-US (AUC 0.88). Significant associations were found between presenting symptoms and specific anomaly types (p < 0.05). Inter-rater reliability between the two radiologists, with respect to classification of anomalies, was high (Cohen's kappa 0.85). MRI's superior soft-tissue contrast and multiplanar capabilities make it the gold standard for evaluating complex uterine malformations. 3D-US offers valuable real-time imaging and is particularly effective in assessing septum characteristics. The combined use of MRI and 3D-US enhances diagnostic precision and facilitates tailored management strategies. Integrating MRI and 3D-US in clinical practice improves diagnostic accuracy and treatment planning for Müllerian anomalies, ultimately enhancing patient outcomes.

Coronary Artery Anomalies: Diagnosis & Management.

Coronary artery anomalies encompass a spectrum of congenital abnormalities affecting the origin, course, or termination of the major epicardial coronary arteries. Despite their rarity, coronary artery anomalies represent a significant burden on cardiovascular health due to their potential to disrupt myocardial blood flow and precipitate adverse cardiac events. While historically diagnosed postmortem, the widespread availability of imaging modalities has led to an increased recognition of coronary artery anomalies, particularly in adults. This review synthesizes current knowledge on the classification, mechanisms, and clinical implications of coronary anomalies, focusing on prevalent variants with significant clinical impact. We discuss strategies for medical and surgical management, as well as contemporary screening recommendations, acknowledging the evolving understanding of these anomalies. Given the breadth of possible variants and the limited data on some presentations, this review provides a framework to aid clinicians in the recognition and management of coronary anomalies, with a particular emphasis on their stratification by anatomical location. By consolidating existing knowledge and highlighting areas of uncertainty, this review aims to enhance clinical decision-making and improve outcomes for individuals with coronary anomalies.

Solving the imbalanced dataset problem in surveillance image blur classification

Surveillance videos taken in unconstrained environments can be tampered with due to different environmental factors and malicious human activities. They often blur the video content and introduce difficulty in identifying the events in the scene. The problem is particularly acute for smart surveillance systems that need to make real-time decisions based on the video. Automatic detection and classification of the blur anomalies in the video are crucial to these systems. Traditional learning-based classification methods often face imbalance problems in the sample numbers and distributions among the data classes in the dataset that severely affect their training and hence the classification performance. In this paper, a new learning-based approach for surveillance image blur classification is proposed. The imbalanced dataset problem is tackled both at the data and algorithm levels. At the data level, two synthesizers are developed to generate the required negative surveillance images to balance the sample numbers for all classes. At the algorithm level, an attention-based structure making use of the special feature of the minority class is proposed to improve the classification accuracy. Our experiment results show that the proposed approach significantly outperforms state-of-the-art methods for blur classification while keeping the model size small for edge applications.

Comprehensive management and classification of first branchial cleft anomalies: An International Pediatric Otolaryngology Group (IPOG) consensus statement

ObjectiveFirst branchial cleft anomalies are rare congenital head and neck lesions. Literature pertaining to classification, work up and surgical treatment of these lesions is limited and, in some instances, contradictory. The goal of this work is to provide refinement of the classification system of these lesions and to provide guidance for clinicians to aid in the comprehensive management of children with first branchial cleft anomalies. Materials and methodsDelphi method survey of expert opinion under the direction of the International Pediatric Otolaryngology Group (IPOG) was conducted to generate recommendations for the definition and management of first branchial cleft anomalies. The recommendations are the result of expert consensus and critical review of the literature. ResultsConsensus recommendations include evaluation and diagnostic considerations for children with first branchial cleft anomalies as well as recommendations for surgical management. The current Work classification system was reviewed, and modifications were made to it to provide a more cogent categorization of these lesions. ConclusionThe mission of the International Pediatric Otolaryngology Group (IPOG) is to develop expertise-based recommendations based on review of the literature for the management of pediatric otolaryngologic disorders. These consensus recommendations are aimed at improving care of children presenting with first branchial cleft anomalies. Here we present a revised classification system based on parotid gland involvement, with a focus on avoiding stratification based on germ layer, in addition to guidelines for management.

Enhancing acceleration data cleaning through time–frequency feature integration using deep learning

In recent years, the safety issues of bridge has garnered significant attention from various sectors of society. Bridge structural health monitoring (SHM) systems have emerged as essential tools for real-time monitoring and assessing the operational safety of bridges. Despite the substantial implementation and operation of monitoring systems, challenges have arisen from abnormal data caused by equipment malfunctions, power outages, and environmental interference. These anomalies are challenging to manually remove within the vast dataset. To address the challenge of automated detection and classification of anomalies, this paper focuses on acceleration data and proposes a data cleaning method that integrates time–frequency features and one-dimensional convolutional neural network (1D CNN). The method utilizes extreme value envelopes and wavelet scattering transforms to extract time–frequency features from acceleration data, thereby achieving data dimensionality reduction and feature extraction goals. Subsequently, these features are employed to train and test a 1D CNN. The proposed method’s validation is conducted using acceleration monitoring data obtained from a large-span arch bridge. Through extensive testing, the model demonstrates a classification accuracy exceeding 98% on both the test set and unseen data, showcasing high classification precision and generalization capabilities. This approach provides an effective solution for automated data cleaning and anomaly identification within the context of bridge health monitoring systems.

Coronary Artery Anomalies: A Computed Tomography Angiography Pictorial Review.

Coronary arteries have a wide range of anatomical variability, and their spectrum ranges from asymptomatic cases to those predisposed to hemodynamic compromise or even sudden cardiac death. This paper aims to review the classification of coronary artery anomalies (CAAs) and illustrate their imaging characteristics by highlighting the important role of CT coronary angiography. Some of the coronary anomalies usually met in current practice are the high origin coronary artery, multiple ostia, aberrant origin from the opposite/non-coronary Valsalva sinus, single coronary artery, ALCAPA syndrome, duplications of the left anterior descending artery, coronary fistulas, and extracardiac terminations. CT coronary angiography is a non-invasive diagnostic modality for CAAs. The complex anatomy of these anomalies can be accurately described by employing 3D reconstructions and post-processing techniques. Knowledge of the imaging characteristics and potential functional impact of these anomalies is essential for accurate diagnosis and therapeutic planning of patients.

Reviewing hemangiomas and vascular malformations: An in-depth analysis

This narrative review delves into the intricacies of hemangiomas and vascular malformations (VMs), exploring their characteristics, diagnostic approaches, and therapeutic interventions. Hemangiomas, characterized by an abnormal growth of blood vessels, and VMs, arising from developmental abnormalities in blood vessels, present multifaceted challenges in clinical practice. The review navigates through the clinical manifestations and classifications of these vascular anomalies, emphasizing the importance of accurate diagnosis based on clinical evaluation, imaging techniques, and histopathological assessment. Variations in presentation, ranging from asymptomatic lesions to severe complications impacting vital organs, underscore the necessity for comprehensive understanding and tailored management strategies. Moreover, this review examines current therapeutic modalities encompassing medical, interventional, and surgical interventions. It synthesizes the evolving landscape of treatment options and highlighting the benefits, limitations, and potential adverse effects associated with each approach. In addition, the review underscores the significance of a multidisciplinary approach, integrating expertise from dermatology, radiology, and surgery, to optimize patient care and outcomes. The challenges posed by these vascular anomalies necessitate ongoing research endeavors aimed at refining diagnostic criteria and advancing therapeutic innovations. This review amalgamates existing knowledge and providing clinicians and researchers with a comprehensive understanding of hemangiomas and VMs, thereby fostering continued exploration and innovation in this complex field.

New classification of the penoscrotal positional anomalies.

The aspect of sexual differentiation and the mechanism controlling the position of genitalia, which represents one of the most substantial differences between the sexes, is still poorly understood. Minor cases and some variants of penoscrotal transposition (PST) are unreported, and obvious cases were classified broadly and confused with other unrelated anomalies. Relevant literature published till 2022 were reviewed then organized, recapitulated, and presented in comparison with the findings and data of 65 child diagnosed with PST. So, an integrated comprehensive approach to this uncommon condition enabled a new classification including few unreported variant cases, which were complemented. PST is classified herein into a cephalic or caudal scrotal migration, the cephalic type subdivided into major and minor subtypes the latter type subdivided into bilateral, unilateral or central subtypes. Cases of caudal scrotal regression is an unreported anomaly in which the scrotum located caudally, as constant association with epispadias/exstrophy anomalies leaving a wide distance between the fixed penis and the scrotal sacs. PST is not rare as it was believed, it occurs in two directions; cephalic and caudal directions. Scrotal caudal regression anomaly was not described before, as well the PST presented as an inguinal hernia.

Optimizing anomaly detection in 3D MRI scans: The role of ConvLSTM in medical image analysis

The analysis of Medical Images (MI), particularly the detection and classification of anomalies in 3D MRI (Magnetic Resonance Imaging) scans, plays a critical part in timely intervention and personalized therapy plans. In our paper, a comprehensive methodology for anomaly detection in 3D MRI scans of the brain is proposed that combines advanced Deep Learning (DL) techniques, including Convolutional Long Short-Term Memory (ConvLSTM) model, with efficient statistical feature extraction from segmented anomaly regions. The data collection phase utilizes three main datasets namely the Brain Tumor Segmentation Challenge (BRATS), the Federated Tumor Segmentation Challenge (FETS), and the Medical Segmentation Decathlon (MSD). The research begins with preprocessing steps including image resizing, intensity normalization, and alignment of anomaly region segmentation masks. The targeted anomaly regions within the samples are segmented using U-Net architecture and then follow statistical feature extraction procedure. Dimensionality reduction methods such as Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) are utilized to streamline the feature space. The ConvLSTM is then used to classify the anomalies using both convolution and recurrent layers to capture spatiotemporal patterns in MRI data. The model is fine-tuned and iterated for better classification performance using Adam optimizer. The statistical evaluation results with accuracy of 98.9 % showed that the designed ConvLSTM method is more suitable for clinical diagnosis and treatment design in detecting anomalies in 3D MRI images.

Diagnostic approach to hepatic vascular lesions: a paediatric perspective.

The pathological evaluation of hepatic vascular lesions in children requires special consideration. Inconsistent terminology, rarity of pathology specimens and overlapping pathological features between various lesions may pose a serious diagnostic challenge. In this review, we highlight the importance of using the International Society for the Study of Vascular Anomalies (ISSVA) classification scheme to characterise these lesions. Selected entities are discussed, including hepatic vascular tumours exclusively seen in the paediatric age group, hepatic infantile haemangioma and hepatic congenital haemangioma. Vascular malformations, with emphasis on their syndromic associations (venous malformation in blue rubber bleb naevus syndrome) and complications (hepatocellular nodules in Abernethy malformation) are also covered.

Impact of Congenital Uterine Anomalies on Ectopic Pregnancy: A Cross-Sectional Observational Study of 510 Cases

Abstract Background: Congenital uterine abnormalities may affect a woman’s ability to reproduce, leading to negative pregnancy-related consequences. One to two percent of all pregnancies are said to carry the risk of an ectopic pregnancy. Objective: The purpose of this study was to evaluate the relationships between reproductive, obstetric, and natal outcomes and various subtypes of congenital uterine abnormalities. Materials and Methods: This cross-sectional observational study included 510 infertile women with a mean age of 36.72 ± 6.99 years. All were submitted to a thorough medical, gynecological, and obstetrical history, and 3D sonography. As well, hysteroscopy examinations were also performed for all females to reveal uterine contours, and detect any Mullerian system anomalies. Uterine anomalies when diagnosed were classified according to the “ESHRE/ESGE consensus on the classification of female genital tract congenital anomalies.” Results: The prevalence of congenital uterine anomalies was 44.9% among patients with ectopic pregnancy. The most common uterine anomaly was uterine septum (962.4%), followed by T-shape uterus (25.8%), then unicornuate (9.2%), and the least incidence was the bicornuate uterus (2.6%). Those with congenital uterine abnormalities had a significantly higher (OR = 1.82) overall risk of developing secondary infertility (P = 0.001). Those with congenital uterine abnormalities had a significantly increased overall risk (OR = 1.476) of experiencing an abortion (P = 0.043). Those with congenital uterine abnormalities had a much higher probability of not giving birth to a live child (OR = 1.72, P = 0.005). Conclusion: When compared to women without anomalies, those with congenital uterine abnormalities had a markedly higher overall risk of developing secondary infertility, abortion, ectopic pregnancy, and a much higher probability of not giving birth to a live child.

Reproducibility of MRI Diagnosis of Female Genital Anomalies.

Purpose: To assess and compare intrareader and interreader reproducibility of magnetic resonance imaging (MRI) diagnosis of female genital anomalies (FGAs) using the American Society for Reproductive Medicine-Mullerian anomalies classification 2021 (ASRM-MAC 2021) and European Society of Human Reproduction and Embryology-European Society for Gynecological Endoscopy (ESHRE-ESGE) 2016 classification. Methods: In this retrospective study, we searched our electronic MRI database from April 2021 to September 2023, selecting MRI studies with FGAs. Seventy-six consecutive studies were included and reviewed by 4 independent radiologists using both classifications. Studies were re-evaluated after 1 month. Reproducibility was assessed using kappa (κ) scores with 95% confidence intervals (CI). Results: Intrareader agreement for MRI diagnosis of FGAs was substantial to excellent, with κ scores ranging from 0.684 (95% CI, 0.534-0.834) to 0.985 (95% CI, 0.963-1.01) using the ASRM-MAC 2021 and from 0.743 (95% CI, 0.621-0.865) to 0.846 (95% CI, 0.719-0.973) using the ESHRE-ESGE 2016 classification. Pairwise interreader agreement was higher with the ASRM-MAC 2021, ranging from moderate (κ = 0.491; 95% CI, 0.341-0.642) to substantial (κ = 0.709; 95% CI, 0.597-0.821), compared to the ESHRE-ESGE 2016 classification, with weak (κ = 0.080; 95% CI, 0.068-0.228) to moderate (κ = 0.511; 95% CI, 0.344-0.678) agreement. Overall interreader agreement was moderate for both classifications (κ = 0.599; 95% CI, 0.562-0.638 for ASRM-MAC 2021 and κ = 0.429; 95% CI, 0.396-0.463 for ESHRE-ESGE 2016 classification), but with significant differences (non-overlapping CIs). Conclusion: The intrareader reproducibility was high for both classifications, whereas the interreader reproducibility was higher using the ASRM-MAC 2021, highlighting the impact of classification criteria on the reproducibility of MRI diagnosis of FGAs.

Concomitant extragenital malformations of female reproductive tract anomalies: analysis of 444 cases in Peking Union Medical College Hospital

Objective: To analyze the incidence and clinical phenotype of the concomitant extragenital malformations in the patients with female reproductive tract anomalies. Methods: A retrospective study was conducted using clinical data of hospitalized patients diagnosed with uterine, cervical, or vaginal malformations from January 2003 to December 2022 in Peking Union Medical College Hospital. The malformations were classified according to American Society for Reproductive Medicine müllerian anomalies classification 2021, and in each type, the incidence and specific manifestations of concomitant extragnital malformations were analyzed. Results: A total of 444 patients were included. The overall incidence of concomitant extragenital malformations was 43.5% (193/444), including urinary system, skeletal system, and other system malformations. Renal malformations on the obstructed side were present in all patients with oblique vaginal septum syndrome (100.0%, 78/78). The total incidence of concomitant extragnital malformations was as high as 8/11 in uterus didelphys, 43.5% (10/23) in unicornuate uterus, 33.6% (79/235) in Mayer-Rokitansky-Küster-Hauser syndrome, 18.8% (6/32) in septate uterus and 18.5% (12/65) in cervical agenesis. Urinary system malformations (30.6%, 136/444) and skeletal system malformations (13.5%, 60/444) were the most common concomitant malformations in all types, in which, unilateral renal agenesis and scoliosis were the most common. Conclusions: Urinary and skeletal system malformations are important features of female reproductive tract anomalies. Urologic ultrasonography and spinal roentgenogram are recommended for all patients with female reproductive tract anomalies.

Leveraging YOLOv5s with optimization‐based effective anomaly detection in pedestrian walkways

AbstractCurrently, video surveillance is generally used to safeguard safety in public places like railway stations, traffic signals, malls, and so on. Video anomaly recognition and localization are the main components of the intelligent video surveillance method. Video anomaly recognition refers to the procedure of spatiotemporal localization of the abnormal design existing in the video. A main task in video surveillance is the classification of anomalies that occur in it like thefts, crimes, and so forth. Also, anomaly recognition in pedestrian walkways has enlarged major attention among the computer vision (CV) groups to improve pedestrian protection. The current developments in Deep Learning (DL) methods have great attention to dissimilar procedures like image classification, object recognition, and so forth. This study designs an Optimal Deep Learning for Effective Anomaly Detection in Pedestrian Walkways (ODL‐EADPW) model. The ODL‐EADPW technique employs a fine‐tuned DL model for the identification of pedestrians and anomalies in the walkways. In the ODL‐EADPW technique, the image pre‐processing is primarily involved in two stages median filtering (MF) based noise removal and adaptive histogram equalization (AHE)‐based contrast enhancement. For anomaly detection in pedestrian walkways, the ODL‐EADPW technique uses the YOLOv5s model with EfficientRep as a backbone network. To enhance the detection results of the ODL‐EADPW technique, a stochastic gradient descent (SGD) optimizer was employed to perfect the hyperparameters of the EfficientRep model. The performance evaluation of the ODL‐EADPW methodology is implemented on the UCSD Anomaly detection dataset. An extensive comparison study stated that the ODL‐EADPW technique gains effectual detection results over other DL models in terms of different measures.

Soft Tissue Vascular Anomalies of the Extremities: A Proposed Diagnostic Approach.

This narrative review aims to summarise the classification of vascular anomalies, their clinical presentation, and their radiological features to propose a diagnostic algorithm to approach patients with suspected soft tissue vascular anomalies of the extremities. The management of vascular anomalies necessitates a multidisciplinary approach. Clinical presentation and physical examination are sufficient in most cases to achieve a correct diagnosis. This is especially true for small congenital lesions of the skin and subcutaneous tissue. Imaging is used for accurate characterization of these lesions, especially in cases of atypical or vague clinical presentation, and to assess extension in cases of lesions that are larger and localized in deeper tissues.

Anomalies of Midbrain/Hindbrain Development: Malformations of Cerebellum: Diagnosis, Classification, and Rehabilitative Hypothesis

AbstractExtensive research has been conducted on the cerebellum, making it one of the most thoroughly investigated regions of the brain. It plays a fundamental role not only in motor control but also in motor learning and cognition. The development of the cerebellum is a lengthy process, beginning during the embryonic period up to the first years of life. This slow and protracted process makes it a vulnerable organ liable to different insults, responsible for many developmental disorders such as Dandy–Walker syndrome, medulloblastoma, dystroglicanopathy, pontocerebellar hypoplasia, thubulinopathies, and Jubert syndrome. Due to several factors, the true prevalence of cerebellar malformations is not known in most cases. The cerebellum undergoes development through following four fundamental stages:(1) Identification of the cerebellar region at the boundary between the midbrain and hindbrain.(2) Establishment of two cell proliferation compartments: firstly, Purkinje cells and deep cerebellar nuclei emerge from the ventricular zone of the metencephalic alar plate; secondly, granule cell precursors are generated from a separate proliferation compartment known as the upper rhombic lip.(3) Migration of granule cells toward the interior: granule precursor cells constitute the external granular layer (EGL), and during the initial postnatal year, granule cells migrate inward to their final position in the internal granular layer.(4) Formation of cerebellar circuitry and subsequent differentiation.Based on different types of involvement of the structures detected in the brain magnetic resonance, the classification of brainstem and cerebellar anomalies is divided into three categories: (1) mainly the cerebellum, (2) mainly the brain stem, and (3) both involved. This review will outline the developmental processes of the cerebellum and delve into common developmental disorders associated with it, including the Dandy–Walker syndrome, cerebellar hypoplasia, rhomboencephalosynapsis, lissencephaly, and gray matter heterotopias.

Detection and Classification of Anomalies in Power Distribution System Using Outlier Filtered Weighted Least Square

Detection and Classification of Anomalies in Power Distribution System Using Outlier Filtered Weighted Least Square

SecureNet: Network Intrusion Detection using Machine Learning and Deep Learning Techniques

Abstract: In the ever-evolving landscape of cybersecurity, the need for robust intrusion detection systems has become paramount. This paper introduces a cutting-edge intrusion detection algorithm designed to enhance network security through the integration of advanced machine learning and deep learning methodologies. The proposed algorithm capitalizes on the strengths of both paradigms to achieve a comprehensive and adaptive approach to identifying malicious activities within a network. This research focuses on enhancing network security through the development and evaluation of a novel intrusion detection system leveraging both deep learning and traditional machine learning approaches. Utilizing the NSL-KDD dataset, we employ the Long Short-Term Memory (LSTM) model, a superior version of Recurrent Neural Networks (RNNs), and the K-Nearest Neighbors (KNN) algorithm for binary and multi-class classification of network intrusion anomalies. The LSTM model excels in capturing temporal dependencies, enabling the detection of nuanced sequential patterns, while the KNN algorithm contributes to a comprehensive classification framework. Experimental results demonstrate the effectiveness of the hybrid methodology, showcasing improved accuracy, precision, and recall compared to traditional methods. This research underscores the potential of integrating deep learning and classical machine learning techniques to bolster the capabilities of intrusion detection systems in safeguarding against evolving cyber threats

Dermato-Radiological Evaluation of Congenital Limb Overgrowth Vascular Syndromes

Abstract International Society for the Study of Vascular Anomalies classification defines Congenital Limb Overgrowth Vascular Syndromes (CLOS) as a subset of vascular syndromes with other abnormalities that present with unilateral limb overgrowth. It includes Klippel–Trenaunay Syndrome, Parkes–Weber Syndrome, CLOVES (Congenital Lipomatous Overgrowth, Vascular Malformations, Epidermal Nevi, Spinal/Skeletal Anomalies/Scoliosis) Syndrome, Proteus Syndrome, PTEN Hamartomatous Syndrome, and Fibroadipose Vascular Anomaly. Due to their rare and complex nature, a multidisciplinary approach to diagnosis and treatment is required. A thorough clinical and radiological workup can go miles in reflecting on the patient’s outcome. Here we report five cases of CLOS with their detailed dermato-radiological profiles.

A Novel Classification System and Surgical Strategies of First Branchial Cleft Anomalies.

To define a novel classification of first branchial cleft anomalies (FBCAs) based on the relationship between lesions and the facial nerve in terms of radiographic imaging findings and to introduce the corresponding surgical managements. The clinical data were retrospectively reviewed. Novel classification was proposed according to the head-neck MRI findings and surgical records. FBCAs limited in the cartilaginous segment of external auditory canal (EAC) or superficial parotid gland capsule were classified as type A. Lesions close to the FN and(or) involved into the parotid gland with no scar formation and no previous parotidectomy were classified as type B. FBCAs adhered to the FN and(or) invaded the parotid gland with scar formation due to previous surgery were classified as type C. Appropriate surgery approaches was also described, which was correlated with classification. Fifty-one patients were included, and one patient suffered from bilateral lesions. Thirty-one, twelve, and nine lesions were classified as type A, type B, and type C FBCAs, respectively. One type A patient (1.92%) suffered from recurrence during follow-up. Temporary facial palsy (House-Brackmann II) was identified in 2 type C patients (3.85%) after surgery and recovered to normal within 2 months. One type B patient (1.92%) suffered from facial paralysis due to the FN injury and underwent facial nerve graft simultaneously. No patients with type C complained of hearing loss postoperatively. This novel classification clearly illustrates definitely relationship between lesion and the facial nerve and appropriate surgical strategies were proposed based on the novel classification. 4 Laryngoscope, 134:4246-4251, 2024.