Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning.

A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To address these issues, we investigated machine learning (ML) approaches for glucose monitoring using data from non-invasive wearables.

Predicting the early response to neoadjuvant chemotherapy in high-grade serous ovarian cancer by intratumoral habitat heterogeneity based on F-FDG PET/CT.

Objective: To explore the predictive value of intratumoral habitat heterogeneity for the early therapeutic response to neoadjuvant chemotherapy (NACT) in patients with high-grade serous ovarian cancer (HGSOC).

Materials And Methods: A total of 258 patients with HGSOC receiving [F]fluorodeoxyglucose ([F]FDG) PET/CT followed by NACT were enrolled and classified into a response group and a non-response group according to RECIST 1.1.

Evaluation of the linear and nonlinear classifiers for distinguishing between healthy subjects and patients with valvular heart diseases based on electrocardiograms, seismocardiograms, and gyrocardiograms.

Background And Objective: Heart rate variability (HRV) is a prognostic marker in numerous cardiovascular and non-cardiovascular conditions. Valvular heart disease (VHD) is a cardiovascular disease that affects the heart valves (aortic valve, mitral valve, pulmonic valve and tricupsid valve) and is the third most common cardiovascular disease. Traditional methods, such as echocardiography, computed tomography, and magnetic resonance imaging, are effective, but their limitations in outpatient monitoring have led to the exploration of alternative techniques, such as electrocardiography (ECG), seismocardiography (SCG) and gyrocardiography (SCG).

A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis.

Diffusion models, a class of deep learning models based on probabilistic generative processes, progressively transform data into noise and then reconstruct the original data through an inverse process. Recently, diffusion models have gained attention in microscopic image analysis for their ability to process complex data, extract valuable information, and enhance image quality. This review provides an overview of diffusion models in microscopic images and micro-alike images, focusing on three commonly used models: DDPM, DDIM, and SDEs.

Effectiveness of fermentation broth of for primary insomnia: a randomized clinical trial with digital health tool.

Background: is widely used in Traditional Chinese Medicine and dietary supplements to tonify the kidney, lung, and heart, as well as to calm the mind. The fermentation broth of (FBCS), containing cordycepin, has shown potential in various healthcare applications.

Methods: Ninety patients with primary insomnia were divided into two groups: the FBCS group ( = 45) and the control group ( = 45).

ParkProReakt-evaluation of a proactive approach to healthcare in Parkinson's disease: a study protocol for a randomised controlled trial.

Introduction: The complexity of Parkinson's disease (PD) symptoms and the necessity for individualised, multidisciplinary and digital health technology-based care are widely acknowledged; however, access to specialist care remains limited, particularly in rural areas. Current healthcare systems are frequently ill-equipped to deliver timely, personalised interventions. In response to these challenges, the ParkProReakt project aims to enhance PD care through a proactive, technology-enabled, multidisciplinary approach designed to improve patient health-related quality of life (HRQoL) and alleviate caregiver burden.

Investigating the Causative Effects of Tinnitus of Different Severity and Stages on the White Matter Microstructural Integrity.

There is increasing evidence that white matter fibres play an important role in tinnitus. A directed bilateral Mendelian randomization (MR) analysis based on genome-wide association studies (GWAS) has been implemented to explore the impact of idiopathic tinnitus on the brain white matter (WM) integrity of different severity and stages at a causal level. The tinnitus-related GWAS is derived from the research of 117,882 European participants, which contains accounts of tinnitus at different severities and stages.

COVID-19CT+: A public dataset of CT images for COVID-19 retrospective analysis.

Background and objectiveCOVID-19 is considered as the biggest global health disaster in the 21st century, and it has a huge impact on the world.MethodsThis paper publishes a publicly available dataset of CT images of multiple types of pneumonia (COVID-19CT+). Specifically, the dataset contains 409,619 CT images of 1333 patients, with subset-A containing 312 community-acquired pneumonia cases and subset-B containing 1021 COVID-19 cases.

Causal relationship between bulimia nervosa and microstructural white matter: evidence from Mendelian randomization.

Purpose: Observational studies suggest white matter (WM) microstructural anomalies are linked to bulimia nervosa (BN), but a direct causal relationship remains unestablished. This study aimed to investigate the causal impact of BN on WM microstructure.

Methods: We analyzed genome-wide association study (GWAS) summary data from 2442 individuals to identify genetically predicted BN.

Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.

Background: Artificial intelligence (AI) has the potential to transform cancer diagnosis, ultimately leading to better patient outcomes.

Objective: We performed an umbrella review to summarize and critically evaluate the evidence for the AI-based imaging diagnosis of cancers.

Methods: PubMed, Embase, Web of Science, Cochrane, and IEEE databases were searched for relevant systematic reviews from inception to June 19, 2024.

A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing.

Recent advancements in hardware technology have spurred a surge in the popularity and ubiquity of wearable sensors, opening up new applications within the medical domain. This proliferation has resulted in a notable increase in the availability of Time Series (TS) data characterizing behavioral or physiological information from the patient, leading to initiatives toward leveraging machine learning and data analysis techniques. Nonetheless, the complexity and time required for collecting data remain significant hurdles, limiting dataset sizes and hindering the effectiveness of machine learning.

WP-FSCIL: A Well-Prepared Few-shot Class-incremental Learning Framework for Pill Recognition.

Few-shot Class-incremental Pill Recognition (FSCIPR) aims to develop an automatic pill recognition system that requires only a few training data and can continuously adapt to new classes, providing technical support for applications in hospitals, portable apps, and assistance for visually impaired individuals. This task faces three core challenges: overfitting, fine-grained classification problems, and catastrophic forgetting. We propose the Well-Prepared Few-shot Class-incremental Learning (WP-FSCIL) framework, which addresses overfitting through a parameter-freezing strategy, enhances the robustness and discriminative power of backbone features with Center-Triplet (CT) loss and supervised contrastive loss for fine-grained classification, and alleviates catastrophic forgetting using a multi-dimensional Knowledge Distillation (KD) strategy based on flexible Pseudo-feature Synthesis (PFS).

Construction of brain age models based on structural and white matter information.

Brain aging is an inevitable process in adulthood, yet there is a lack of objective measures to accurately assess its extent. This study aims to develop brain age prediction model using magnetic resonance imaging (MRI), which includes structural information of gray matter and integrity information of white matter microstructure. Multiparameter MRI was performed on two population cohorts.

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.