Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation.

The integration of machine learning into the domain of radiomics has revolutionized the approach to personalized medicine, particularly in oncology. Our research presents RadTA (RADiomics Trend Analysis), a novel framework developed to facilitate the automatic analysis of quantitative imaging biomarkers (QIBs) from time-series CT volumes. RadTA is designed to bridge a technical gap for medical experts and enable sophisticated radiomic analyses without deep learning expertise.

Raw photoplethysmogram waveforms versus peak-to-peak intervals for machine learning detection of atrial fibrillation: Does waveform matter?

Background: Machine learning-based analysis can accurately detect atrial fibrillation (AF) from photoplethysmograms (PPGs), however the computational requirements for analyzing raw PPG waveforms can be significant. The analysis of PPG-derived peak-to-peak intervals may offer a more feasible solution for smartphone deployment, provided the diagnostic utility is comparable.

Aims: To compare raw PPG waveforms and PPG-derived peak-to-peak intervals as input signals for machine learning detection of AF.

Total Body Surface Area Adjusted Daily Diagnostic Blood Loss May Be Higher in Minor Burns-Are Our Patients the Victims of Daily Routine?

Burns are common and devastating injuries, often necessitating intensive care treatment and long-term hospitalisation, making burn patients susceptible to hospital-acquired anaemia and blood transfusion. The purpose of this study was to assess diagnostic blood loss in burn patients at the burn intensive care unit (BICU) at Uppsala University Hospital between 1 September 2016 and 30 June 2019. Medical records were screened; age, gender, mechanism, % total body surface area (TBSA), Baux score, length of stay, days on the respirator, days of continuous renal replacement therapy, number of operations, and number of blood tests per patient were assessed.

Effectiveness of Sensors-Based Augmented Feedback in Ergonomics to Reduce Adverse Biomechanical Exposure in Work-Related Manual Handling-A Rapid Review of the Evidence.

Manual handling is a major risk factor for work-related musculoskeletal disorders and one of the leading causes of disability-adjusted life years globally, necessitating multifaceted risk reduction measures. One potential intervention for manual handling tasks is work technique training assisted by augmented feedback on biomechanical exposures. However, there is a research gap regarding its effectiveness specifically for manual handling tasks in both real work environments and controlled settings, as well as its ability to induce retained reductions in biomechanical exposure.