Results 141 to 150 of about 195,059 (253)

Photoacoustic device fingerprints induce bias in deep learning models. [PDF]

Sci Rep
Bender CJ, Knopp M, Holzwarth N, Rix T, Nölke JH, Dreher KK, Li Y, Kempf J, Caranovic M, Schneider F, Schellenberg M, Boland L, Haney B, Knieling F, Rother U, Seitel A, Maier-Hein L.   +16 more
europepmc   +1 more source

Short‐term response to biologic therapy in psoriatic arthritis (PsA) is not influenced by disease duration at initiation of therapy

Arthritis Care &Research, Accepted Article.
Objectives We aimed to determine whether the short‐term response to biologics in biologic‐naïve psoriatic arthritis (PsA) is better in patients initiating biologic treatment early in the disease course. Methods PsA patients who started biologic therapy from the year 2000 to 2025 were included for analysis. Patients considered in the early treatment (ET)
Sung Min Cho, Shangyi Gao, Daniel Pereira, Richard J. Cook, Dafna D. Gladman   +4 more
wiley   +1 more source

Visible through touch: open-source 3D-printed tools for inclusive microbiology education. [PDF]

Access Microbiol
Price CL, Seeley A, Pincott H, Morse DJ, Harris LG, Noble JG.   +5 more
europepmc   +1 more source

Beyond Visual Scoring: Computational CT‐analysis for HRCT based quantification of Interstitial Lung Disease in Inflammatory Rheumatic Disease

Arthritis Care &Research, Accepted Article.
Interstitial lung disease (IRD‐ILD) is a significant cause of morbidity and mortality in patients with inflammatory rheumatic disorders (IRD). High‐resolution computed tomography (HRCT) is widely considered the gold standard for the non‐invasive assessment of ILD; however, its interpretation is constrained by substantial inter‐observer variability and ...
Alexander Pfeil, Tobias Hoffmann, Ulf Teichgräber, Diane Renz, Denise Beckmann, Gunter Wolf, Nicole Schadewaldt, Bianca Lassen‐Schmidt   +7 more
wiley   +1 more source

DREAMER-S: Deep leaRning-Enabled Attention-based Multiple-instance approaches with Explainable Representations for Spatial biology. [PDF]

PLoS Comput Biol
Rafsanjani MR, Dooney A, Suresh R, O'Farrell AC, Jarzabek MA, Shiels L, Byrne AT, Prehn JHM, Meade AD.   +8 more
europepmc   +1 more source

Extending Service Beyond Active Duty: Generalizability of Rheumatoid Arthritis Research Conducted Among U.S. Veterans

Arthritis Care &Research, Accepted Article.
Biomedical research involving United States Veterans continues to advance healthcare beyond the Veterans Health Administration. This is particularly true in rheumatoid arthritis (RA), where Veteran‐centric research has uncovered novel insights into pathogenesis, risk factors, and disease manifestations, informing clinical care and research across both ...
Austin M. Wheeler, Joshua F. Baker, Jennifer L. Barton, Maria I. Danila, Tate M. Johnson, Kamini Kuchinad, Kaleb Michaud, Amir A. Razmjou, J. Steuart Richards, Namrata Singh, Isaac D. Smith, Katherine D. Wysham, Grant W. Cannon, Bryant R. England, Amy Joseph, Deana Lazaro, Katherine P. Liao, Courtney Loecker, Andreas Reimold, Beth I. Wallace, Ted R. Mikuls   +20 more
wiley   +1 more source

Comparative Analysis of Deep Learning Approaches for Histopathology-Based Survival Prediction in Hepatocellular Carcinoma. [PDF]

Cancers (Basel)
Lee SH, Yim K, Jang HJ.
europepmc   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

AI and the transformation of clinical reasoning: A classroom perspective. [PDF]

PLOS Digit Health
Nguyen NP.
europepmc   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga   +2 more
wiley   +1 more source