Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.
The shift from the traditional empirical approach to a more data-driven method in the diagnosis and treatment of GI cancers is significant due to advancements in overall precision medicine and digital healthcare. Artificial intelligence(AI),the driving force behind the technological revolution,is increasingly being used in screening,diagnosis,treatment,and rehabilitation in gastrointestinal surger...
Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects. Efficient coding must preserve high fidelity in blade regions while aggressively compressing the background. In this work, we propose an end-to-end deep learning framework that jointly performs segmentation and dual-mode (lossy and lossless) compre...
BACKGROUND: Artificial intelligence (AI) integrated with point-of-care imaging is a promising approach to expand access in settings with limited speci...
Peptide-spectrum match (PSM) rescoring is critical for accurate peptide identification in data-dependent acquisition (DDA)-based proteomics. Existing ...
RATIONALE AND OBJECTIVES: Human papillomavirus (HPV) status is a critical biomarker for treatment planning and patient management in oropharyngeal can...
BACKGROUND: Intervertebral disc degeneration (IVDD) is a prominent etiology of lower back pain. Type 2 diabetes (T2D), the most prevalent metabolic di...
Three-dimensional (3D) imaging captures spatial depth and multidimensional attributes, enabling precise scene reconstruction for diverse applications ...
Urinary tract infections (UTIs) are diagnosed based on symptoms and confirmed by urine culture, despite its limitations in sensitivity. False-negative...
Traditional radiology education is constrained by a restricted apprenticeship model and a scarcity of datasets structured for building artificial inte...
Protein S-palmitoylation, a dynamic lipid modification, is essential for protein stability, trafficking, and signaling; dysregulated palmitoyltransfer...
BACKGROUND AND OBJECTIVE: Current deep learning approaches for predicting ejection fraction primarily rely on end-to-end regression. While effective i...
Liver transplantation is the definitive treatment for end-stage liver disease; however, postoperative acute kidney injury (AKI) affects 30%-70% of rec...
PURPOSE: To determine whether a high-quality, prospectively curated dataset can, by itself, enable the development of robust and clinically effective ...
BACKGROUND: The rapid evolution of digital technologies has transformed health, mental health, and social care, offering new modalities of digital car...
Gait is a key indicator for assessing an individual's mobility and overall health, and accurate detection of gait cycle phases is essential for precis...
OBJECTIVE: This study contributes to the limited literature on applying machine learning (ML) to telemonitoring data for timely decision support syste...
BACKGROUND: The integration of robotic systems into nursing practice is increasingly discussed as a potential strategy to alleviate workload and suppo...
BACKGROUND: Prognostic information is essential for decision-making in breast cancer management. In recent years, trials and clinical practice have em...
Computational in silico methods offer a powerful alternative to animal-based toxicity testing, which remains time-consuming, expensive, and ethically ...
BACKGROUND: AI-generated images can support or impede health communication efforts and influence perceptions of health-related topics, making it impor...