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Brief Title: Young-onset Colorectal Cancer Screening Based on Artificial Intelligence
Official Title: Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records
Study ID: NCT06342622
Brief 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.
Minimum Age: 18 Years
Eligible Ages: ADULT
Sex: ALL
Healthy Volunteers: Yes
Renmin Hospital of Wuhan University, Wuhan, Hubei, China
Name: Dong Weiguo, PhD
Affiliation: Renmin Hospital of Wuhan University
Role: STUDY_CHAIR