Evaluation of the Usefulness of an Automated Navigation System for Laparoscopic Cholecystectomy Based on Tokyo Guidelines 2018

Usefulness of Automated Navigation for Laparoscopic Cholecystectomy Based on Tokyo Guidelines 2018 - AI Navigation for LapC Based on TG2018

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-jRCT1030250220

Enrollment

100

Registered

2025-07-08

Start date

2022-01-01

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Gallbladder stone disease, acute cholecystitis, and chronic cholecystitis Gallbladder stone disease, acute cholecystitis, and chronic cholecystitis

Interventions

None listed

Eligibility

Inclusion criteria

Inclusion criteria: A consecutive cohort of patients who underwent LC at the Keio University Hospital (Tokyo, Japan) between January 2022 and March 2024 were included.

Exclusion criteria

Exclusion criteria: Patients who underwent full-thickness cholecystectomy or had moderate or severe cholecystitis based on the TG 2018 criteria were excluded.

Design outcomes

Primary

Measure	Time frame
AI model performance evaluation: The Intersection-over-Union (IoU) score, which assesses the agreement between the AI-generated segmentation mask and the ground truth data provided by both the developer and an external expert surgeon.	—

Secondary

Measure	Time frame
Usability evaluation of AI navigation: The rate at which trainee surgeons correctly select a safe incision point and recognize the contour of the gallbladder surface when comparing performance with and without AI assistance.	—

Contacts

Public ContactKeita Sonoda

Keio University School of Medicine

ksonoda622@keio.jp+81-3-3353-1211

Outcome results