Clinical impact of artificial intelligence (AI) on endoscopic diagnosis for esophageal achalasia

Study on the Improvement of Esophageal Achalasia Diagnosis Accuracy by Artificial Intelligence in Endoscopy - AI Esophageal Diagnosis Accuracy Study

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000053047

Enrollment

138

Registered

2025-01-01

Start date

2023-01-01

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

Esophageal achalasia

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients ?Individuals diagnosed with achalasia. ?Among those diagnosed with non-achalasia during the same period, those with video records available, and for whom multiple endoscopists have confirmed the diagnosis of non-achalasia based on the recorded videos,and who have been deemed suitable for AI training. ?Age is not a criterion. Doctors Physicians who wish to read images and have obtained consent (age 20 and above).

Exclusion criteria

Exclusion criteria: Individuals deemed ineligible by the principal investigator or researchers.

Design outcomes

Primary

Measure	Time frame
The additional effect of diagnosis by Achalasia CAD among non-experienced doctors	—

Countries

Japan,Asia(except Japan),North America,Europe

Contacts

Public ContactHironari Shiwaku

Fukuoka University Faculty of Medicine Department of Gastroenterological Surgery

hiro.shiwaku@gmail.com092-801-1011

Outcome results