NCT06342622

Young-onset Colorectal Cancer Screening Based on Artificial Intelligence

Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 11,000 (actual)
• Sponsor: Renmin Hospital of Wuhan University · Academic / Other
• Sex: All
• Age: 18 Years – 49 Years
• Healthy volunteers: Accepted

Summary

In this study, we aimed to develop, internally and temporally validate the machine learning models to help screen YOCRC bansed on the retrospective extracted Electronic Medical Records (EMR) data.

Detailed description

Diagnosis of young-onset colorectal cancer (YOCRC) has become more common in recent decades. Screening CRC among younger adults still remains a challenge. In this study, We plan to retrospectively extracte the relevant clinical data of young individuals who underwent colonoscopy from 2013 to 2022 using Electronic Medical Record (EMR). Multiple supervised machine learning techniques will be applied to distinguish YOCRC and non-YOCRC individuals, the above classifiers will be trained and internally validated in the training dataset and internal validation dataset admitted between 2013 and 2021, respectively. We will also assess the temporal external validity of the classifiers based on the admissions from 2022.

Conditions

• Colorectal Cancer

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Using routine clinical data and machine learning models.	This study used clinical data and machine learning model to screen young-onset colorectal cancer.

Timeline

• Start date: 2023-12-01
• Primary completion: 2024-01-10
• Completion: 2024-01-25
• First posted: 2024-04-02
• Last updated: 2024-04-02

Locations

1 site across 1 country: China