Integrating immune multi-omics and machine learning to improve prognosis, immune landscape, and sensitivity to first- and second-line treatments for head and neck squamous cell carcinoma.

In recent years, immune checkpoint inhibitors (ICIs) has emerged as a fundamental component of the standard treatment regimen for patients with head and neck squamous cell carcinoma (HNSCC). However, accurately predicting the treatment effectiveness of ICIs for patients at the same TNM stage remains a challenge. In this study, we first combined multi-omics data (mRNA, lncRNA, miRNA, DNA methylation, and somatic mutations) and 10 clustering algorithms, successfully identifying two distinct cancer subtypes (CSs) (CS1 and CS2).

Predicting executive functioning from walking features in Parkinson's disease using machine learning.

Parkinson's disease is characterized by motor and cognitive deficits. While previous work suggests a relationship between both, direct empirical evidence is scarce or inconclusive. Therefore, we examined the relationship between walking features and executive functioning in patients with Parkinson's disease using state-of-the-art machine learning approaches.

Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language.

Gesture recognition has become a significant part of human-machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.

Machine Learning-Based Gesture Recognition Glove: Design and Implementation.

In the evolving field of human-computer interaction (HCI), gesture recognition has emerged as a critical focus, with smart gloves equipped with sensors playing one of the most important roles. Despite the significance of dynamic gesture recognition, most research on data gloves has concentrated on static gestures, with only a small percentage addressing dynamic gestures or both. This study explores the development of a low-cost smart glove prototype designed to capture and classify dynamic hand gestures for game control and presents a prototype of data gloves equipped with five flex sensors, five force sensors, and one inertial measurement unit (IMU) sensor.

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition.

Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses.

A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients.

Background: Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients.

PHE-SICH-CT-IDS: A benchmark CT image dataset for evaluation semantic segmentation, object detection and radiomic feature extraction of perihematomal edema in spontaneous intracerebral hemorrhage.

Background And Objective: Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH) typically presents acutely, prompt and expedited radiological examination is crucial for diagnosis, localization, and quantification of the hemorrhage. Early detection and accurate segmentation of perihematomal edema (PHE) play a critical role in guiding appropriate clinical intervention and enhancing patient prognosis.

Role of artificial intelligence in digital pathology for gynecological cancers.

The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide.

ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions.

Background: Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis and reducing the workload of doctors.

CAISHI: A benchmark histopathological H&E image dataset for cervical adenocarcinoma identification, retrieval and few-shot learning evaluation.

A benchmark histopathological Hematoxylin and Eosin (H&E) image dataset for Cervical Adenocarcinoma (CAISHI), containing 2240 histopathological images of Cervical Adenocarcinoma (AIS), is established to fill the current data gap, of which 1010 are images of normal cervical glands and another 1230 are images of cervical AIS. The sampling method is endoscope biopsy. Pathological sections are obtained by H&E staining from Shengjing Hospital, China Medical University.

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data.

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g.

Symmetric Projection Attractor Reconstruction Analysis as a Method to Assess Seismocardiogram Quality in a Healthy Population.

Signal quality significantly affects the processing, analysis, and interpretation of biomedical signals. There are many procedures for assessing signal quality that use averaged numerical values, thresholding, analysis in the time or frequency domain, or nonlinear approaches. An interesting approach to the assessment of signal quality is using symmetric projection attractor reconstruction (SPAR) analysis, which transforms an entire signal into a two-dimensional plot that reflects the waveform morphology.

Publicly available signal databases containing seismocardiographic signals - the state in early 2023.

The development of information and communication technologies (ICT) changed many aspects of our lives, including cardiovascular research. This area of research is affected by the availability of open databases that can help conduct basic and applied research. In this study, we summarize the current state of knowledge in publicly available signal databases with seismocardiographic (SCG) signals in January 2023.

Non-Invasive Wearable Devices for Monitoring Vital Signs in Patients with Type 2 Diabetes Mellitus: A Systematic Review.

Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the need to explore non-invasive alternatives for monitoring vital signs in patients with T2D. This systematic review is the first that explores the current literature and critically evaluates the use and reporting of non-invasive wearable devices for monitoring vital signs in patients with T2D.

OII-DS: A benchmark Oral Implant Image Dataset for object detection and image classification evaluation.

In recent years, there is been a growing reliance on image analysis methods to bolster dentistry practices, such as image classification, segmentation and object detection. However, the availability of related benchmark datasets remains limited. Hence, we spent six years to prepare and test a bench Oral Implant Image Dataset (OII-DS) to support the work in this research domain.

Study protocol to investigate the efficacy of confocal laser endomicroscopy-based selective single-elimination diet over standard fivefold elimination diet in patients with endomicroscopically proven food intolerance: app-assisted, monocentric, double-blind, randomised and controlled trial in Germany.

Introduction: Imprecise nutritional recommendations due to a lack of diagnostic test accuracy are a frequent problem for individuals with adverse reactions to foods but no precise diagnosis. Consequently, patients follow very broad and strict elimination diets to avoid uncontrolled symptoms such as diarrhoea and abdominal pain. Dietary limitations and the uncertainty of developing gastrointestinal symptoms after the inadvertent ingestion of food have been demonstrated to reduce the quality of life (QoL) of affected individuals and subsequently might increase the risk of malnutrition and intestinal dysbiosis.

A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable Sensors.

Machine learning with deep neural networks (DNNs) is widely used for human activity recognition (HAR) to automatically learn features, identify and analyze activities, and to produce a consequential outcome in numerous applications. However, learning robust features requires an enormous number of labeled data. Therefore, implementing a DNN either requires creating a large dataset or needs to use the pre-trained models on different datasets.

Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study.

Multiple attempts to quantify pain objectively using single measures of physiological body responses have been performed in the past, but the variability across participants reduces the usefulness of such methods. Therefore, this study aims to evaluate whether combining multiple autonomic parameters is more appropriate to quantify the perceived pain intensity of healthy subjects (HSs) and chronic back pain patients (CBPPs) during experimental heat pain stimulation. HS and CBPP received different heat pain stimuli adjusted for individual pain tolerance via a CE-certified thermode.