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Artificial intelligence based on deep learning for auxiliary diagnosis of early-stage esophageal cancer

Artificial intelligence based on deep learning for auxiliary diagnosis of early-stage esophageal cancer

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR1900024529
Enrollment
Unknown
Registered
2019-07-14
Start date
2020-01-01
Completion date
Unknown
Last updated
2020-11-30

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

Conditions

early-stage upper gastrointestinal cancer

Interventions

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Sponsors

Zhongshan Hospital of Fudan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: (1) the age is greater than 18 years old, and less than or equal to 80 years old; (2) failure of esophageal endoscopy in the past 1 year (except for treatment study); (3) suitable for esophageal endoscopy (treatment); (4) informed consent.

Exclusion criteria

Exclusion criteria: 1. patients undergoing emergency examination (treatment) of upper gastrointestinal due to hematemesis and abdominal pain; 2. With history of other malignant tumors; 3. American society of anesthesiologists (ASA) classification is greater than III; 4. pregnancy and lactation; 5. patients or family members cannot understand the conditions and objectives of this study.

Design outcomes

Primary

MeasureTime frame
true positive rate;false negative rate;SPE, SEN, ACC, AUC of ROC;

Secondary

MeasureTime frame
Hematoplastic index;CT;

Countries

China

Contacts

Public ContactZhong Yunshi

Endoscopy center, Zhongshan Hospital of Fudan University

zhongamy2002@163.com+86 13564623481

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026