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Full fine-tuning strategy for endoscopic foundation models with expanded learnable offset parameters.
Biomed Phys Eng Express
January 2025
Shandong Normal University, Jinan, Jinan, Shandong, 250014, CHINA.
In the medical field, endoscopic video analysis is crucial for disease diagnosis and minimally invasive surgery. The Endoscopic Foundation Models (Endo- FM) utilize large-scale self-supervised pre-training on endoscopic video data and leverage video transformer models to capture long-range spatiotemporal dependencies. However, detecting complex lesions such as gastrointestinal metaplasia (GIM) in endoscopic videos remains challenging due to unclear boundaries and indistinct features, and Endo-FM has not demonstrated good performance.
View Article and Find Full Text PDFLeveraging Artificial Intelligence to Develop a Decision-Support Tool for Visual Symptom Assessment of Grapevine Leafroll and Red Blotch Diseases.
Plant Dis
January 2025
University of California Davis, Cooperative Extension, Napa, California, United States;
The timely detection of viral pathogens in vineyards is a critical aspect of management. Diagnostic methods can be labor-intensive and may require specialized training or facilities. The emergence of artificial intelligence (AI) has the potential to provide innovative solutions for disease detection but requires a significant volume of high-quality data as input.
View Article and Find Full Text PDFMultimodal Pain Recognition in Postoperative Patients: Machine Learning Approach.
JMIR Form Res
January 2025
Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems.
View Article and Find Full Text PDFEnhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis.
J Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
View Article and Find Full Text PDFFactors Influencing Telemedicine Adoption Among Health Care Professionals: Qualitative Interview Study.
JMIR Form Res
January 2025
Center for Management, University of Münster, Münster, Germany.
Background: Telemedicine is transforming health care by enabling remote diagnosis, consultation, and treatment. Despite rapid adoption during the COVID-19 pandemic, telemedicine uptake among health care professionals (HCPs) remains inconsistent due to perceived risks and lack of tailored policies. Existing studies focus on patient perspectives or general adoption factors, neglecting the complex interplay of contextual variables and trust constructs influencing HCPs' telemedicine adoption.
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