Artificial intelligence-based phase recognition for workflow analysis in robotic choledochal cyst excision: a proof-of-concept study - Top_Commentaries - MDSpire
Coming Soon: Introducing MDSpire News. Learn more
Conexiant’s news site is now MDSpire News. Learn more

Utilizing Artificial Intelligence for Workflow Assessment in Robotic Excision of Choledochal Cysts: A Preliminary Investigation

  • By

  • Akihiro Yasui

  • Yuichiro Hayashi

  • Chiyoe Shirota

  • Takahisa Tainaka

  • Satoshi Makita

  • Masamune Okamoto

  • Aitaro Takimoto

  • Shunya Takada

  • Kaito Hayashi

  • Daiki Kato

  • Hiroki Ishii

  • Hajime Asai

  • Kazuki Ota

  • Masahiro Oda

  • Kensaku Mori

  • Hiroo Uchida

  • October 5, 2026

Share

3 Topic Commentaries

Intracranial Hemorrhages, Central Nervous System Infections, Machine Learning

  • Dr. Jane Smith, MD, Neurocritical Care Physician, MD

    Assistant Professor of Neurology

    •

    University Hospital of Critical Care Medicine

    “While high internal AUCs like 0.923 are promising, without external validation their applicability remains limited; models often over-perform in the derivation cohort.”

    [Source]
  • Dr. Li Wei, PhD, Data Scientist & Neuroscience Researcher, PhD

    Senior Research Fellow

    •

    Institute for Brain Health Research

    “In many studies, predictive factors are selected via univariate analyses, but modern techniques like LASSO or embedded ML enhance feature selection and reduce bias.”

    [Source]
  • Dr. Maria Gonzalez, MPH, Infectious Disease Epidemiologist, MPH

    Public Health Policy Advisor

    •

    National Stroke & Infection Control Coalition

    “Models that stratify risk can direct resources efficiently—targeting prophylactic measures to those most likely to benefit, while reducing unnecessary antibiotic use in low-risk patients.”

    [Source]

Original Source(s)

Related Content