Classification system

INTRODUCTION: Vascular diseases, such as stroke and heart failure (HF), are associated with cognitive decline. Vascular cognitive impairment (CI) is commonly found in patients who had a stroke and with HF, ranging from mild CI to dementia. Early detection of CI is crucial for effective management and rehabilitation. This study aimed to develop the VasCog Screen test, a screening tool to detect CI in patients who had a stroke and with HF. METHOD: 427 patients who had a stroke and with HF were assessed using cognitive measures including Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and a formal neuropsychological battery...

BACKGROUND: The extraction of horizontally impacted mandibular third molars (HM3) can be a complicated surgery. Appropriate tooth sectioning methods can reduce the operation time and postoperative complications. PURPOSE: The current study compares operative time and postoperative pain between HM3 removed using the three-piece or T-shaped tooth sectioning techniques. STUDY DESIGN, SETTING, SAMPLE: A randomized single-blind prospective clinical trial on HM3 extraction was carried out between June and December 2022 in the Department of Oral and Maxillofacial Surgery, the Affiliated Stomatological Hospital, Southwest Medical University...


Brain-Computer Interfaces (BCIs) have the potential to reinstate lost communication faculties. Results from speech decoding studies indicate that a usable speech BCI based on activity in the sensorimotor cortex (SMC) can be achieved using subdurally implanted electrodes. However, the optimal characteristics for a successful speech implant are largely unknown. We address this topic in a high field blood oxygenation level dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) study, by assessing the decodability of spoken words as a function of hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal-axis...

PURPOSE: Preoperative prediction of postoperative complications (PCs) in inpatients with cancer is challenging. We developed an explainable machine learning (ML) model to predict PCs in a heterogenous population of inpatients with cancer undergoing same-hospitalization major operations. METHODS: Consecutive inpatients who underwent same-hospitalization operations from December 2017 to June 2021 at a single institution were retrospectively reviewed. The ML model was developed and tested using electronic health record (EHR) data to predict 30-day PCs for patients with Clavien-Dindo grade 3 or higher (CD 3+) per the CD classification system...

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions to BCIs degrade the classification accuracy on benign samples. This phenomenon greatly affects the applicability of adversarial defense approaches to EEG-based BCIs...

Areas of interest (AOIs) are well-established means of providing semantic information for visualizing, analyzing, and classifying gaze data. However, the usual manual annotation of AOIs is time-consuming and further impaired by ambiguities in label assignments. To address these issues, we present an interactive labeling approach that combines visualization, machine learning, and user-centered explainable annotation. Our system provides uncertainty-aware visualization to build trust in classification with an increasing number of annotated examples...

Applying affective computing techniques to recognize fear and combining them with portable signal monitors makes it possible to create real-time detection systems that could act as bodyguards when users are in danger. With this aim, this paper presents a fear recognition method based on physiological signals obtained from wearable devices. The procedure involves creating twodimensional feature maps from the raw signals, using data augmentation and feature selection algorithms, followed by deep learning-based classification models, taking inspiration from those used in image processing...

In this article, we propose a set of transform-based neural network layers as an alternative to the [Formula: see text] Conv2D layers in convolutional neural networks (CNNs). The proposed layers can be implemented based on orthogonal transforms, such as the discrete cosine transform (DCT), Hadamard transform (HT), and biorthogonal block wavelet transform (BWT). Furthermore, by taking advantage of the convolution theorems, convolutional filtering operations are performed in the transform domain using elementwise multiplications...

PURPOSE: We studied a pediatric group of patients with sellar-suprasellar tumors, aiming to develop a convolutional deep learning algorithm for radiological assistance to classify them into their respective cohort. METHODS: T1w and T2w preoperative magnetic resonance images of 226 Chilean patients were collected at the Institute of Neurosurgery Dr. Alfonso Asenjo (INCA), which were divided into three classes: healthy control (68 subjects), craniopharyngioma (58 subjects) and differential sellar/suprasellar tumors (100 subjects)...

Communication is challenging for disabled individuals, but with advancement of brain-computer interface (BCI) systems, alternative communication systems can be developed. Current BCI spellers, such as P300, SSVEP, and MI, have drawbacks like reliance on external stimuli or conversation irrelevant mental tasks. In contrast to these systems, Imagined speech based BCI systems rely on directly decoding the vowels/words user is thinking, making them more intuitive, user friendly and highly popular among Brain-Computer-Interface (BCI) researchers...

Brain tumour can be cured if it is initially screened and given timely treatment to the patients. This proposed idea suggests a transform- and windowing-based optimization strategy for exposing and segmenting the tumour region in brain pictures. The processes of image processing that are included in the proposed idea include preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation. In order to convert the pixels connected to the spatial domain into a multi-resolution domain, the Gabor transform is first applied to the brain test image...

OBJECTIVE/ SUMMARY BACKGROUND DATA: We propose the first classification scheme for macroglossia in patients with Beckwith-Wiedemann Syndrome (BWS), the BWS Index of macroGlossia (BIG). METHODS: Patients with molecularly confirmed BWS seen from 2004-2023 were included to develop this system. Relationships among BIG scores, tongue reduction surgery, BWS clinical score, percent mosaicism, and polysomnography findings were examined. RESULTS: Patients were classified from BIG0 to BIG3...

INTRODUCTION: Control attenuation parameters (CAP) can detect nonalcoholic fatty liver disease (NAFLD). Our previous study found that miR-192-5p could screen for acute pancreatitis (AP) in NAFLD patients. This study focused on the role of CAP and miR-192-5p in NAFLD of acute AP. MATERIAL AND METHODS: AP patients and controls were enrolled. Classification of AP patients into NAFLD/AP patients and non-NAFLD/AP was made based on the CAP value. CAP was measured by liver transient elastography...

INTRODUCTION: The latest World Health Organization (WHO) classification from 2022 distinguishes the division of low-risk thyroid neoplasms such as non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), follicular tumour of uncertain malignant potential (FT-UMP), and well-differentiated tumour of uncertain malignant potential (WDT-UMP). The final diagnosis is made postoperatively according to histopathologic results. The aim of the study was the assessment of ultrasonographic and cytopathological features of borderline lesions to predict low-risk tumours preoperatively and plan the optimal treatment for that group of patients...

The proportion and area of ratoon rice planting in China have been substantially increased, due to continuous improvement of rice breeding methods and consecutive innovation of cultivation technology, which has developed into one of rice planting modes with significant production efficiency. Combining the experience in research and practice, from the perspective of crop physiology and ecology, we reviewed the current situation and prospects of high-yielding formation and physiological mechanisms of ratoon rice...

BACKGROUND: Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI. METHODS: A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC)...

BACKGROUND: Many rectovaginal fistulas(RVF), especially low RVF, do not involve/penetrate the RV-septum, but due to lack of proper nomenclature, such fistulas are also managed like RVF (undertaking repair of RV-septum) and inadvertently lead to the formation of a high RVF (involving RV-septum) in many cases. Therefore, REctovaginal Fistulas, Not Involving the Rectovaginal Septum, should be Treated like Anal fistulas(RENISTA) to prevent any risk of injury to the RV septum. This concept(RENISTA) was tested in this study...

In the dynamic landscape of glioblastoma, the 2021 World Health Organization Classification of Central Nervous System tumours endeavoured to establish biological homogeneity, yet isocitrate dehydrogenase-wild-type (IDH-wt) glioblastoma persists as a tapestry of clinical and molecular diversity. Intertumoural heterogeneity in IDH-wt glioblastoma presents a formidable challenge in treatment strategies. Recent strides in genetics and molecular biology have enhanced diagnostic precision, revealing distinct subtypes and invasive patterns that influence survival in patients with IDH-wt glioblastoma...

Accurate and automatic segmentation of individual cell instances in microscopy images is a vital step for quantifying the cellular attributes, which can subsequently lead to new discoveries in biomedical research. In recent years, data-driven deep learning techniques have shown promising results in this task. Despite the success of these techniques, many fail to accurately segment cells in microscopy images with high cell density and low signal-to-noise ratio. In this paper, we propose a novel 3D cell segmentation approach DeepSeeded, a cascaded deep learning architecture that estimates seeds for a classical seeded watershed segmentation...

Acute kidney injury (AKI) is one of the most common organ dysfunctions impacting ICU (intensive care unit) patients. Early diagnosis using the various classification systems and interventions that can be aided by use of biomarkers are key in improving outcomes. Once the patient meets criteria of AKI, many patient specific factors determine the optimal timing for and mode of renal replacement therapy. There are several special considerations in surgical ICU patients with AKI including management of intracranial hypertension in those with cerebral edema, anticoagulation in high-risk bleeding patients, and use of contrast imaging...