Artificial intelligence-enabled obesity prediction: A systematic review of cohort data analysis.

Background: Obesity, now the fifth leading global cause of death, has seen a dramatic rise in prevalence over the last forty years. It significantly increases the risk of diseases such as type 2 diabetes and cardiovascular disease. Early identification of obesity risk allows for preventative actions against obesity-related factors.

Digital redesign of the radiotherapy course for medical students with a blended learning approach.

Purpose: Due to the need for high-quality teaching, we present a new blended learning concept combining digital modules, interactive seminars, and clinical experience. Furthermore, we evaluated its acceptance among students.

Methods: A new concept for teaching was applied to the radiotherapy module and made available via the Hannover Medical School (MHH) learning management system as part of a blended learning concept with educational films, multimedia learning modules, online seminars, and onsite practical training.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

MeTEor: an R Shiny app for exploring longitudinal metabolomics data.

Motivation: The availability of longitudinal omics data is increasing in metabolomics research. Viewing metabolomics data over time provides detailed insight into biological processes and fosters understanding of how systems react over time. However, the analysis of longitudinal metabolomics data poses various challenges, both in terms of statistical evaluation and visualization.

Monitoring individualized glucose levels predicts risk for bradycardia in type 2 diabetes patients with chronic kidney disease: a pilot study.

Patients with diabetes mellitus (DM) and chronic kidney disease (CKD) exhibit an elevated risk for cardiac arrhythmias, such as bradycardia, which may potentially lead to sudden cardiac death (SCD). While hypoglycemia, defined as a critical drop in glucose levels below the normal range, has long been associated with adverse cardiovascular events, recent studies have highlighted the need for a comprehensive reevaluation of its direct impact on cardiovascular outcomes, particularly in high-risk populations such as those with DM and CKD. In this study, we investigated the association between glucose levels and bradycardia by simultaneously monitoring interstitial glucose (IG) and ECG for 7 days in insulin-treated patients with DM and CKD.

Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations.

Introduction: Over the past few decades, numerous researchers have explored the application of machine learning for assessing children's neurological development. Developmental changes in the brain could be utilized to gauge the alignment of its maturation status with the child's chronological age. AI is trained to analyze changes in different modalities and estimate the brain age of subjects.

Beyond Nyquist: A Comparative Analysis of 3D Deep Learning Models Enhancing MRI Resolution.

High-spatial resolution MRI produces abundant structural information, enabling highly accurate clinical diagnosis and image-guided therapeutics. However, the acquisition of high-spatial resolution MRI data typically can come at the expense of less spatial coverage, lower signal-to-noise ratio (SNR), and longer scan time due to physical, physiological and hardware limitations. In order to overcome these limitations, super-resolution MRI deep-learning-based techniques can be utilised.

Spatial Cellular Networks from omics data with SpaCeNet.

Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which are received by other cells, where they can subsequently initiate context-specific gene regulatory responses. These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment.

Robust In-Vehicle Signal Quality Assessment Using Multimodal Signal Fusion.

Continuous monitoring of physiological signals such as electrocardiogram (ECG) in driving environments has the potential to reduce the need for frequent health check-ups by providing real-time information on cardiovascular health. However, capturing ECG from sensors mounted on steering wheels creates difficulties due to motion artifacts, noise, and dropouts. To address this, we propose a novel method for reliable and accurate detection of heartbeats using sensor fusion with a bidirectional long short-term memory (BiLSTM) model.

Multimodal Signal Fusion for Heartbeat Monitoring on eScooters.

Integrating continuous monitoring into everyday objects enables the early detection of diseases. This paper presents a novel approach to heartbeat monitoring on eScooters using multi-modal signal fusion. We explore heartbeat monitoring using electrocardiography (ECG) and photoplethysmography (PPG) and evaluate four signal fusion approaches based on convolutional neural network (CNN) and long short-term memory (LSTM) architectures.

Deep Learning-Based Prediction of Daily COVID-19 Cases Using X (Twitter) Data.

Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer.

An Open Information Model-Based Repository for Sustainable Re-Use of Heterogeneous Pandemics Research Data.

The COVID-19 Research Network Lower Saxony (COFONI) is a German state network of experts in Coronavirus research and development of strategies for future pandemics. One of the pillars of the COFONI technology platform is its established research data repository (Available at https://forschungsdb.cofoni.

Creating Digital Rescue Sheets.

Rescue sheets enable rescue personnel to timely extricate trapped victims of road traffic accidents and increase their chance of survival. However, in the year 2024, these rescue sheets are still paper based DIN A4 documents. The digital transformation of the rescue process through new reporting technologies, such as eCall or the International Standard Accident Number (ISAN) facilitates digital rescue sheets, providing benefits for availability and functionality.

Unobtrusive Monitoring of Clinical Deterioration in Smart Homes.

Clinical deterioration (CD) is the physiological decompensation that incurs care escalation, protracted hospital stays, or even death. The early warning score (EWS) calculates the occurrence of CD based on five vital signs. However, there are limited reports regarding EWS monitoring in smart home settings.

DR.BEAT: Rule-Based Algorithm for SCG Analysis Without ECG Reference.

The DR.BEAT project aims to develop an accelerometer-based, wearable sensor system for measuring ballistocardiographic (BCG) signals, coupled with signal processing and visualization, to support cardiac health monitoring. A rule-based heartbeat detection was developed to enable the derivation of health parameters independent of an existing reference.

Quality of Electrocardiography Recorded with Consumer Smart Shirts.

Smart wearables support continuous monitoring of vital signs for early detection of deteriorating health. However, the devices and sensors require sufficient quality to produce meaningful signals, in particular, if data is acquired in motion. In this study, we equipped 48 subjects with smart shirts recording one-lead electrocardiography (ECG), thoracic and abdominal respiratory inductance plethysmography, and three-axis acceleration.

Enhancing the Value of the BfArM's DiGA Directory: Strategies for Improved Usability and Stakeholder Satisfaction.

Germany's Digital Healthcare Act allows doctors to prescribe digital health applications (DiGAs) for reimbursement. DiGAs must demonstrate safety, data security, and a "positive impact on care" to be listed in the official directory. Previously, data for permanently listed DiGAs was analyzed.

Challenges of Integrating ICD 11 into Automatic Alerting Systems.

Automatic alerting systems (AASs) can identify adverse health events but emergency communication relies on human operators and natural languages. For complete automation, we need to code the diversity of adverse events in a granularity that supports optimal dispatches. Hence, AAs shall integrate with the International Classification of Diseases (ICD).

Semantic Mapping of Named-Entities in openEHR Templates and Ad-hoc Generation of Compositions.

Integration of free texts from reports written by physicians to an interoperable standard is important for improving patient-centric care and research in the medical domain. In the context of unstructured clinical data, NLP Information Extraction serves in finding information in unstructured text. To our best knowledge, there is no efficient solution, in which extracted Named-Entities of an NLP pipeline can be ad-hoc inserted in openEHR compositions.

Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs.

Deciphering the needs of patients with hereditary breast and ovarian Cancer in the Process of Genetic Counseling to Inform the Development of a Mobile Support App: a qualitative study in Germany.

Patients with hereditary breast and ovarian cancer (HBOC) are not only concerned about their own health but also about that of their children, grandchildren, and other relatives. Therefore, they have specific needs for information and support. During genetic counseling guidance is provided to HBOC patients and other individuals who may be at risk for familial cancer.

Barcamps or unconferences as an emerging paradigm in medical education: Insights from a pilot and feasibility mixed methods study.

Medical education is experiencing a paradigm shift towards more interactive and collaborative pedagogical approaches. Barcamps, also known as unconferences, offer an interactive, participant-driven learning approach. This study aims to evaluate the feasibility of using barcamps as an educational model in medical education.

Practical Evaluation of ChatGPT Performance for Radiology Report Generation.

Rationale And Objectives: The process of generating radiology reports is often time-consuming and labor-intensive, prone to incompleteness, heterogeneity, and errors. By employing natural language processing (NLP)-based techniques, this study explores the potential for enhancing the efficiency of radiology report generation through the remarkable capabilities of ChatGPT (Generative Pre-training Transformer), a prominent large language model (LLM).

Materials And Methods: Using a sample of 1000 records from the Medical Information Mart for Intensive Care (MIMIC) Chest X-ray Database, this investigation employed Claude.

Allocation Rules and Age-Dependent Waiting Times for Kidney Transplantation.

Background: Rigid age limits in the current allocation system for post-mortem donor kidneys in Germany may have problematic effects. The new German national transplantion registry enables data analysis with respect to this question.

Methods: Using anonymized data from the German national transplantion registry, we extracted and evaluated information on the recipients and postmortem donors of kidneys that were allocated in Germany through Eurotransplant over the period 2006-2020.

Fully Automated MRI-based Analysis of the Locus Coeruleus in Aging and Alzheimer's Disease Dementia using ELSI-Net.

Introduction: The Locus Coeruleus (LC) is linked to the development and pathophysiology of neurodegenerative diseases such as Alzheimer's Disease (AD). Magnetic Resonance Imaging based LC features have shown potential to assess LC integrity in vivo.

Methods: We present a Deep Learning based LC segmentation and feature extraction method: ELSI-Net and apply it to healthy aging and AD dementia datasets.