Objective: To determine if accurate documentation of bladder cancer risk was associated with a clinician surveillance recommendation that is concordant with AUA guidelines among patients with nonmuscle invasive bladder cancer (NMIBC).
Methods: We prospectively collected data from cystoscopy encounter notes from four Department of Veterans Affairs (VA) sites to ascertain whether they included accurate documentation of bladder cancer risk and a recommendation for a guideline-concordant surveillance interval. Accurate documentation was a clinician-recorded risk classification matching a gold standard assigned by the research team. Clinician recommendations were guideline-concordant if the clinician recorded a surveillance interval that was in line with the AUA guideline.
Results: Among 296 encounters, 75 were for low-, 98 for intermediate-, and 123 for high-risk NMIBC. 52% of encounters had accurate documentation of NMIBC risk. Accurate documentation of risk was less common among encounters for low-risk bladder cancer (36% vs 52% for intermediate- and 62% for high-risk, P < .05). Guideline-concordant surveillance recommendations were also less common in patients with low-risk bladder cancer (67% vs 89% for intermediate- and 94% for high-risk, P < .05). Accurate documentation was associated with a 29% and 15% increase in guideline-concordant surveillance recommendations for low- and intermediate-risk disease, respectively (P < .05).
Conclusion: Accurate risk documentation was associated with more guideline-concordant surveillance recommendations among low- and intermediate-risk patients. Implementation strategies facilitating assessment and documentation of risk may be useful to reduce overuse of surveillance in this group and to prevent unnecessary cost, anxiety, and procedural harms.
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http://dx.doi.org/10.1016/j.urology.2023.08.014 | DOI Listing |
BMC Bioinformatics
January 2025
Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Background: Imaging-based spatial transcriptomics technologies allow us to explore spatial gene expression profiles at the cellular level. Cell type annotation of imaging-based spatial data is challenging due to the small gene panel, but it is a crucial step for downstream analyses. Many good reference-based cell type annotation tools have been developed for single-cell RNA sequencing and sequencing-based spatial transcriptomics data.
View Article and Find Full Text PDFFront Robot AI
January 2025
Interactive Robotics Laboratory, School of Computing and Augmented Intelligence (SCAI), Arizona State University (ASU), Tempe, AZ, United States.
We present WearMoCap, an open-source library to track the human pose from smartwatch sensor data and leveraging pose predictions for ubiquitous robot control. WearMoCap operates in three modes: 1) a Watch Only mode, which uses a smartwatch only, 2) a novel Upper Arm mode, which utilizes the smartphone strapped onto the upper arm and 3) a Pocket mode, which determines body orientation from a smartphone in any pocket. We evaluate all modes on large-scale datasets consisting of recordings from up to 8 human subjects using a range of consumer-grade devices.
View Article and Find Full Text PDFCommun Med (Lond)
January 2025
Division of Pulmonary Medicine, Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland.
Background: Bronchiolitis Obliterans Syndrome (BOS), a fibrotic airway disease that may develop after lung transplantation, conventionally relies on pulmonary function tests (PFTs) for diagnosis due to limitations of CT imaging. Deep neural networks (DNNs) have not previously been used for BOS detection. This study aims to train a DNN to detect BOS in CT scans using an approach tailored for low-data scenarios.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, No. 800 Dong Chuan Road, Shanghai 200240, China.
Machine learning has emerged as a transformative tool for elucidating cellular heterogeneity in single-cell RNA sequencing. However, a significant challenge lies in the "black box" nature of deep learning models, which obscures the decision-making process and limits interpretability in cell status annotation. In this study, we introduced scGO, a Gene Ontology (GO)-inspired deep learning framework designed to provide interpretable cell status annotation for scRNA-seq data.
View Article and Find Full Text PDFAnn Transl Med
December 2024
Division of Advanced Gastrointestinal and Bariatric Surgery, Mayo Clinic, Jacksonville, FL, USA.
Background: Addressing language barriers through accurate interpretation is crucial for providing quality care and establishing trust. While the ability of artificial intelligence (AI) to translate medical documentation has been studied, its role for patient-provider communication is less explored. This review evaluates AI's effectiveness in clinical translation by assessing accuracy, usability, satisfaction, and feedback on its use.
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