Results 221 to 230 of about 753,913 (307)

Subjective time distortion in a puzzle task

, 2019
Daisuke Tanaka, Yuka Mizuuji
openalex   +2 more sources

Educational Impact of Artificial Intelligence‐Navigation Surgery on Anatomical Landmark Recognition in Medical Students

Annals of Gastroenterological Surgery, EarlyView.
This study evaluated the educational impact of artificial intelligence (AI)‐navigation surgery that provides real‐time anatomical landmark recognition during laparoscopic cholecystectomy for medical students. Thirty students were randomized into surgeon‐guided, self‐learning, and AI‐learning groups, and their performance was assessed using Dice ...
Shigeo Ninomiya, Yusuke Matsunobu, Tabito Oyama, Hiroomi Takayama, Takashi Masuda, Teijiro Hirashita, Yuichi Endo, Tatsushi Tokuyasu, Masafumi Inomata   +8 more
wiley   +1 more source

Staying With the Trouble in Nursing 12.5 Hours at a Time. [PDF]

Nurs Philos
Smith JB.
europepmc   +1 more source

The Tohoku Earthquake and critical incident stress of journalists (3): Coverage and news reporting activities and subjective symptoms from Questionnaire survey with newspaper companies in the disaster area

, 2012
Yoshiharu Fukuoka, Naoya Takahashi, Yutaka Matsui, Kiyoshi Ando, Kako Inoue, Miho Hatanaka   +5 more
openalex   +2 more sources

Named Entity Recognition Models for Machine Learning Interatomic Potentials: A User‐Centric Approach to Knowledge Extraction from Scientific Literature

Advanced Intelligent Discovery, EarlyView.
Named entity recognition pipeline for knowledge extraction from scientific literature. Machine learning interatomic potential (MLIP) is an emerging technique that has helped achieve molecular dynamics simulations with unprecedented balance between efficiency and accuracy. Recently, the body of MLIP literature has been growing rapidly, which propels the
Bowen Zheng, Grace X. Gu
wiley   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Assessment of Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer: Interobserver Variability and Contributing Factors. [PDF]

Diagnostics (Basel)
Baharun NB, Zailani MAH, Adam A, Xu Q, Mustangin M, Md Zin RR.   +5 more
europepmc   +1 more source

Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of image quality metrics in combination with machine learning enables automatic image quality assessment for fluorescence microscopy images. The method can be integrated into the experimental pipeline for optical microscopy and utilized to classify artifacts in experimental images and to build quality rankings with a reference‐free approach ...
Elena Corbetta, Thomas Bocklitz
wiley   +1 more source

"Schizophrenia, Consciousness, and the Self" Twenty Years Later: Revisiting the Ipseity-Disturbance Model and the Developmental Nature of Self-Disorder in the Schizophrenia Spectrum. [PDF]

Schizophr Bull
Raballo A, Henriksen MG, Poletti M, Parnas J.   +3 more
europepmc   +1 more source

IAR‐Net: Tabular Deep Learning Model for Interventionalist's Action Recognition

Advanced Intelligent Systems, EarlyView.
This study presents IAR‐Net, a deep‐learning framework for catheterization action recognition. To ensure optimality, this study quantifies interoperator similarities and differences using statistical tests, evaluates the distribution fidelity of synthetic data produced by six generative models, and benchmarks multiple deep‐learning models.
Toluwanimi Akinyemi, Olatunji Omisore, Wenjing Du, Wenke Duan, Chen Bailang, Liu Kun, Lei Wang, Minxin Wei   +7 more
wiley   +1 more source