98%
921
2 minutes
20
Profound economic and social forces are challenging healthcare organizations to deliver higher quality care that is more patient-centered and evidence-based. We describe a novel way in which organizations can respond to the challenge of patient-centered, evidence-based innovation--an in-house learning laboratory for healthcare delivery services and processes. Mayo Clinic's SPARC Innovation Program, initiated in 2002 and fully operational in 2005, facilitates the generation of new ideas, tests prototypes, and disseminates the knowledge required for systemic, repeatable organizational innovation. Results from the innovation program suggest that healthcare organizations can successfully develop and realize value from such learning laboratories.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/j.1945-1474.2009.00004.x | DOI Listing |
IEEE Trans Med Imaging
September 2025
Mammography is a primary method for early screening, and developing deep learning-based computer-aided systems is of great significance. However, current deep learning models typically treat each image as an independent entity for diagnosis, rather than integrating images from multiple views to diagnose the patient. These methods do not fully consider and address the complex interactions between different views, resulting in poor diagnostic performance and interpretability.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
September 2025
Vanderbilt University, Data Science Institute, Nashville, Tennessee, United States.
Purpose: Recent developments in computational pathology have been driven by advances in vision foundation models (VFMs), particularly the Segment Anything Model (SAM). This model facilitates nuclei segmentation through two primary methods: prompt-based zero-shot segmentation and the use of cell-specific SAM models for direct segmentation. These approaches enable effective segmentation across a range of nuclei and cells.
View Article and Find Full Text PDFJ Microbiol Methods
September 2025
Department of Microbiology and Immunology, Faculty of Medicine, Fukuoka University, Japan.
The Microscopic Agglutination Test (MAT) is widely recognized as the gold standard for diagnosing zoonosis leptospirosis. However, the MAT relies on subjective evaluations by human experts, which can lead to inconsistencies and inter-observer variability. In this study, we aimed to emulate expert assessments using deep learning and convert them into reproducible numerical outputs to achieve greater objectivity.
View Article and Find Full Text PDFEur Radiol Exp
September 2025
Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
Background: Lung lobe segmentation is required to assess lobar function with nuclear imaging before surgical interventions. We evaluated the performance of open-source deep learning-based lung lobe segmentation tools, compared to a similar nnU-Net model trained on a smaller but more representative clinical dataset.
Materials And Methods: We collated and semi-automatically segmented an internal dataset of 164 computed tomography scans and classified them for task difficulty as easy, moderate, or hard.
Front Oncol
August 2025
Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Background: Immunotherapy has been used in the clinical management of TNBC. While BRCA1 mutations are associated with immunotherapy response, the therapeutic outcomes in TNBC patients are not promising.
Methods: This study integrated spatial, single-cell, and bulk RNA-seq data to explore the role of BRCA1 in reshaping the TNBC microenvironment.