Results 211 to 220 of about 739,829 (293)

Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?

Annals of Gastroenterological Surgery, EarlyView.
Deep learning has shown promise in predicting postoperative complications, particularly when using image or time‐series data. However, on tabular clinical data such as the NCD, it often underperforms compared to conventional machine learning. Integrating multimodal data may enhance predictive accuracy and interpretability in surgical care.
Ryosuke Fukuyo, Masanori Tokunaga, Hiroyuki Yamamoto, Hideki Ueno, Yusuke Kinugasa   +4 more
wiley   +1 more source

Artificial intelligence-driven clinical auxiliary diagnosis of benign paroxysmal positional vertigo. [PDF]

Front Neurol
Dai S, Wu Y, Kang X, Shen Z, Zhong P.
europepmc   +1 more source

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

Evaluation of Experienced Manipulation Characteristics in Robotic Surgery Using Log Data From the Hinotori Surgical System

Annals of Gastroenterological Surgery, EarlyView.
This study analyzed log data from the Japanese hinotori surgical robot to characterize manipulation performed by experienced surgeons in robotic surgery. Compared with less‐experienced surgeons, the experienced group demonstrated shorter task durations, reduced travel distances with the right instrument (Arm3), faster and more dynamically modulated ...
Masaki Saito, Shunsuke Hayakawa, Hiroyuki Sagawa, Tamiaki Kondo, Masanao Ohashi, Sunao Ito, Reo Sato, Hajime Ushigome, Kenta Saito, Ryo Ogawa, Hiroki Takahashi, Shuji Takiguchi   +11 more
wiley   +1 more source

Assessing the usefulness of educational videos on endodontic irrigation for dental students: a pilot study. [PDF]

BMC Med Educ
Kock JW, Yeap SKS, Chhabra N, Chien PY, Bhatia S.   +4 more
europepmc   +1 more source

Essential Updates 2024–2025: Surgical Strategy for Esophageal Cancer Toward a New Paradigm in the Era of Immunotherapy and Personalization

Annals of Gastroenterological Surgery, EarlyView.
This review summarizes key advances from 2024 to 2025 that are reshaping esophageal cancer surgery toward a strategy‐oriented, personalized paradigm through the integration of immunotherapy, population aging, and intelligent technologies. Adjuvant nivolumab after neoadjuvant chemoradiotherapy remains the only perioperative approach with durable benefit,
Shuichiro Oya, Koichi Yagi, Yoshifumi Baba   +2 more
wiley   +1 more source

YouTube Viewing and Content Quality in Toddlers. [PDF]

Infancy
Woods M, McClure M, Schaller A, Weeks HM, Suh B, Chaudhry S, Tibbitts A, Kirkorian H, Barr R, Coyne SM, Radesky J.   +10 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

The role of digital teaching methods in supporting practical skills training in the academic training of health professions - a scoping review. [PDF]

BMC Med Educ
Tjorven S, Nora M, Jost S, Mathilde G, Kristina F.   +4 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source