Development of an artificial intelligence system (Version2) using deep learning to indicate anatomical landmarks during laparoscopic gastrectomy

Development of an artificial intelligence system (Version2) using deep learning to indicate anatomical landmarks during laparoscopic gastrectomy - Development of an artificial intelligence system (Version2) using deep learning to indicate anatomical landmarks during laparoscopic gastrectomy

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000049977

Enrollment

Registered

2023-01-16

Start date

2023-01-17

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

gastric cancer

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: 1) Histologically proven gastric carcinoma (pap, tub1, tub2, por1, por2, sig, or muc). 2) Clinical Stage I to III gastric carcinoma according to the Japanese classification system. 3) Regardless of occupation and number of tumors. 4) PS (ECOG) 0 or 1. 5) Sufficient organ function. 6) Provided written informed consent.

Exclusion criteria

Exclusion criteria: 1)BMI over 30 2)History of gastrectomy 3)Severe mental disease.

Design outcomes

Primary

Measure	Time frame
Improved accuracy of laparoscopic gastrectomy with intraoperative indicating system assisted by artificial intelligence	—

Secondary

Measure	Time frame
Evaluation of landmark indicating ability by the external review committee	—

Countries

Japan

Contacts

Public ContactYoshimasa Aoyama

Oita University Faculty of Medicine Gastroenterological and Pediatric Surgery

y-aoyama@oita-u.ac.jp097-586-5843

Outcome results