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A TACAF-based classification system for thoracic posterior longitudinal ligament ossification.

BACKGROUND AND OBJECTIVES: To evaluate the reliability and clinical applicability of a novel classification system for thoracic posterior longitudinal ligament ossification (OPLL) and its utility in guiding surgical approach selection for anterior controllable ante-displacement fusion (TACAF). METHODS: Based on anatomical and clinical characteristics, thoracic OPLL was classified according to: Grades 1-4 (severity), Zones A-B (location), and Arc morphology (kyphotic curvature). Twenty surgeons independently assessed 50 cases to evaluate system reliability. Fleiss kappa coefficients determined…

Mesenchymal tumor-induced Fanconi syndrome: A novel classification of Fanconi syndrome.

RATIONALE: Fanconi syndrome (FS) caused by mesenchymal tumors is rarely reported, and these tumors are usually small and difficult to detect. However, if found and removed, the FS can be cured. PATIENT CONCERNS: Five patients were included in this study, and their clinical features, treatments, and prognoses of tumor-induced FS were reviewed and summarized. DIAGNOSES: Mesenchymal tumor-induced FS was diagnosed based on laboratory, imaging, and pathological examinations. INTERVENTIONS: We performed tumor resection surgery and administered a symptomatic treatment regimen that included neutral

Multimodal Masked Autoencoder Based on Adaptive Masking for Vitiligo Stage Classification.

Vitiligo, a prevalent skin condition characterized by depigmentation, presents challenges in staging due to its inherent complexity. Multimodal skin images can provide complementary information, and in this study, the integration of clinical images of vitiligo and those obtained under Wood's lamp is conducive to the classification of vitiligo stages. However, difficulties in annotating multimodal data and the scarcity of multimodal data limit the performance of deep learning models in related classification tasks. To address these issues, a Multimodal Masked Autoencoder (Multi-MAE) based on ad…

Lumbosacral transitional vertebrae in small-breed dogs: Prevalence, classification, and characteristics.

The lumbosacral transitional vertebra (LTV) is an abnormally formed vertebra that can be associated with cauda equina syndrome, lumbosacral (LS) degeneration, and pelvic rotation. However, there is little information on LTV in small-breed dogs, and no studies have investigated the association between LTV and LS diseases or degenerative changes in the coxofemoral joints. The purpose of this multicenter, retrospective, and cross-sectional study was to determine the prevalence of LTV in small-breed dogs (<10 kg), classify the type of LTV, and identify its association with degenerative changes in

Classification and Genotype-Phenotype Relationships of GBA1 Variants: MDSGene Systematic Review.

Depending on zygosity and the specific change, different variants in the GBA1 gene can cause Parkinson's disease (PD, PARK-GBA1) with reduced penetrance, act as genetic risk factors for PD or parkinsonism, and/or lead to Gaucher's disease (GD). This MDSGene systematic literature review covers 27,963 patients carrying GBA1 variants from 1082 publications with 794 variants, including 13,342 patients with PD or other forms of parkinsonism. It provides a comprehensive overview of demographic, clinical, and genetic findings from an ethnically diverse sample originating from 82 countries across five

Deep learning-based classification of acute scrotum using single ultrasound images.

OBJECTIVE: To develop a deep learning model for differential diagnosis of acute scrotum using single ultrasound (US) images. PATIENTS AND METHODS: We analysed 1172 patients with acute scrotal pain evaluated by Doppler US at four hospitals. From each case, we selected a representative axial colour Doppler US image. We trained a binary classification model to distinguish torsion from non-torsion using an EfficientNet architecture. The dataset was split 70% for training and 30% for validation. We addressed class imbalance with data augmentation and class weighting. Class Activation Mapping was u…

Multi-class machine learning-based classification of SCID-related genetic variants.

BACKGROUND: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results … were excluded. In silico functional prediction scores were collected for each variant. Multi-class classification models were developed using six machine learning algorithms: Random Forest, XGBoost, Gradient Boosting, AdaBoost, Support Vector Machine and Logistic Regression. Performance was evaluated using five-fold cross-validation with five repeats (25 folds) …

Fractal analysis for cognitive impairment classification in DAVF using machine learning.

Background. Intracranial dural arteriovenous fistula (DAVF) is an acquired vascular condition involving abnormal connections between dural a … on fractal, nonfractal, and Pearson correlation components were generated and used as features for classification. Machine learning classifiers, including SVM and decision trees, were optimized via cross-validation in MATLAB, with performance assessed by accuracy, sensitivity, specificity, and AUC.Results. Nonfractal connectivity outperformed fractal and Pearso …

Exploring salivary gland lesions: Unveiling malignancy risks using the Milan classification.

INTRODUCTION: Salivary gland tumours (SGTs) are characterized by exceptional heterogeneity, diversity, and frequent morphological overlap among different entities. Fine needle aspiration cytology (FNAC) plays a pivotal role in evaluating salivary gland lesions (SGLs), facilitating prompt and accurate diagnosis and treatment. The Milan System of Reporting Salivary Gland Cytology (MSRSGC) is a six-tiered reporting system that aims to standardize SG reporting, fostering consistency in clinical management. AIMS/OBJECTIVES: To assess the cytomorphological patterns of various SGLs and categorize th

Diabetic foot ulcer classification assessment employing an improved machine learning algorithm.

BackgroundDiabetic foot ulcers (DFU) are a severe consequence of diabetes that, if left untreated, can lead to amputation, blindness, renal failure, and other serious complications. The high treatment expense and length of treatment for this therapeutic technique are both disadvantages. Despite the effectiveness of this strategy, a distant, cost-effective, and comfortable DFU diagnostic therapy is necessary.ObjectiveThis study proposed the Advanced Machine Learning Practical Method for Diabetic Foot Ulcer Classification.MethodsThis unique and cost-effective healthcare solution uses Practical M…

Глубокое обучение для классификации множественных склерозных повреждений и стратификации с использованием МРТ.

Множественный склероз (MS) — хроническое неврологическое заболевание, характеризующееся воспалением, обезвоживанием и нейродегенерацией в центральной нервной системе. Обычные методы магнитно-резонансной съемки (MRI) часто с трудом поддаются обнаружению небольших или тонких повреждений, особенно в таких проблемных регионах, как кортикальная серая материя и стержень мозга. В этом исследовании предлагается новый подход, основанный на глубоком обучении, в сочетании с надежной системой предварительной обработки и оптимизированными протоколами МРТ, с тем чтобы повысить точность классификации и страт

Совпадение в аннотации оригинала
BACKGROUND AND OBJECTIVE: Multiple sclerosis (MS) is a chronic neurological disease characterized by inflammation, demyelination, and neurod … a robust preprocessing pipeline and optimized MRI protocols, to improve the precision of MS lesion classification and stratification. METHODS: We designed a convolutional neural network (CNN) architecture specifically tailored for high-resolution T2-weighted imaging (T2WI), augmented by deep learning-based reconstruction (DLR) techniques. The model incorporate …

Unveiling EMG semantics: a prototype-learning approach to generalizable gesture classification.

Objective.Upper limb loss can profoundly impact an individual's quality of life, posing challenges to both physical capabilities and emotional well-being. To restore limb function by decoding electromyography (EMG) signals, in this paper, we present a novel deep prototype learning method for accurate and generalizable EMG-based gesture classification. Existing methods suffer from limitations in generalization across subjects due to the diverse nature of individual muscle responses, impeding seamless applicability in broader populations.Approach.By leveraging deep prototype learning, we introdu…

Multi-Label Chest X-Ray Image Classification With Single Positive Labels.

Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, and prone to noisy labels. Therefore, we introduce a weakly supervised learning problem called Single Positive Multi-label Learning (SPML) into CXR images classification (abbreviated as SPML-CXR), in which only one positive label is annotated per image. A simple solution to SPML-CXR problem is to assume that all the unannotated pathological labels are negative, however, it might introduce false n…

Uterine adenosarcoma: Clinical significance of histological classification and SNP array analysis.

Mullerian adenosarcoma is a rare malignant biphasic tumor. The mesenchymal component may be low or high grade, with or without sarcomatous overgrowth (SO). Little is known about the molecular heterogeneity of these tumors. In this study, we aim to reclassify a large retrospective monocentric cohort of uterine adenosarcomas according to tumor grade and SO, to evaluate the clinical significance of pathological classification and to correlate with copy-number variations inferred from single nucleotide polymorphism array. Of the 67 uterine adenosarcomas, 18 (26.9%) were low grade without SO, 7 (10…

Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction.

GB (Glioblastoma WHO Grade 4) is the most aggressive type of brain tumor in adults that has a short survival rate even after aggressive trea … th similar radiological and clinical characteristics on MRI. Moreover, learning-based rBT versus RN classification using MRI may suffer from class imbalance due to a lack of patient data. While synthetic data generation using generative models has shown promise to address class imbalances, the underlying data representation may be different in synthetic or augme …

Epileptic Seizure Classification with Patient-level and Video-level Contrastive Pretraining.

Accurate classification of epileptic seizure types through seizure semiology analysis demands significant clinical expertise. While previous studies have employed various action recognition modules, the scarcity of labeled clinical videos has hindered the deployment of larger models. In this study, we explore unlabeled data to pretrain a transformer-based model with contrastive loss, taking advantage of the information that circumvents the need for additional annotation from medical professionals. We maximize the similarity between embeddings from the same patient and video while minimizing th…

Lightweight neural network for Alzheimer's disease classification using multi-slice sMRI.

Alzheimer's disease (AD) is a progressive neurodegenerative disease. Early detection and intervention are crucial in preventing the progress … of distances between different classes in the learned features. Experimental results show that the classification accuracies of our method for AD vs. CN, AD vs. MCI, and MCI vs. CN classification tasks are 95.00%, 87.50%, and 85.62% respectively. Our method utilizes only 3.42 M parameters and 6.08G FLOPs, while maintaining a comparable level of performance comp …

Improved neural network with multi-task learning for Alzheimer's disease classification.

Alzheimer's disease(AD) poses a significant challenge due to its widespread prevalence and the lack of effective treatments, highlighting th … rate that compared to the baseline VGG16, ADNet achieves a 4.18% accuracy improvement for AD vs. CN classification and a 6% improvement for MCI vs. CN classification. These findings highlight the effectiveness of ADnet in classifying Alzheimer's disease, providing crucial support for early diagnosis and intervention by medical professionals. The proposed enhance …

Classification of H. pylori Infection from Histopathological Images Using Deep Learning.

Helicobacter pylori (H. pylori) is a widespread pathogenic bacterium, impacting over 4 billion individuals globally. It is primarily linked to gastric diseases, including gastritis, peptic ulcers, and cancer. The current histopathological method for diagnosing H. pylori involves labour-intensive examination of endoscopic biopsies by trained pathologists. However, this process can be time-consuming and may occasionally result in the oversight of small bacterial quantities. Our study explored the potential of five pre-trained models for binary classification of 204 histopathological images, dist…

Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment.

Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnormal gait classification algorithm considering joint impairments using inertial measurement units (IMUs) and walkway systems. Ten healthy male participants simulated normal walking, walking with knee impairment, and walking with ankle impairment under three conditions: without joint braces, with a knee brace, and with an ankle brace. Based on these simulated gaits, we developed classification models: distinguishing abnormal gait due to joint impair…