Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
NCT ID: NCT04551287
Last Updated: 2023-08-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
16164 participants
OBSERVATIONAL
2019-07-01
2020-12-14
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
OTHER
Study Groups
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Training dataset
11,468 eligible individuals' slides for the cervical cytology screening collected from the Sun Yat-sen Memorial Hospital (SYSMH, Guangzhou, China) between January 2016 and January 2020, were randomly assigned to the training dataset (n = 9,316) and the internal validation dataset (n = 2,152) in order to train and validate the Artificial Intelligence Cervical Cancer Screening (AICS).
No interventions assigned to this group
SYSMH internal validation dataset
11,468 eligible individuals' slides for the cervical cytology screening collected from the Sun Yat-sen Memorial Hospital (SYSMH, Guangzhou, China) between January 2016 and January 2020, were randomly assigned to the training dataset (n = 9,316) and the internal validation dataset (n = 2,152) in order to train and validate the Artificial Intelligence Cervical Cancer Screening (AICS).
No interventions assigned to this group
TAHGMU external validation dataset
600 slides from 600 eligible individuals were obtained in the Third Affiliated Hospital of Guangzhou Medical University (TAHGMU, Guangzhou, China) between January 2016 and January 2020, which was used to validate the Artificial Intelligence Cervical Cancer Screening (AICS).
No interventions assigned to this group
GWCMC external validation dataset
600 slides from 600 eligible individuals were obtained in Guangzhou Women and Children Medical Center (GWCMC, Guangzhou, China) between January 2016 and January 2020, which was used to validate the Artificial Intelligence Cervical Cancer Screening (AICS).
No interventions assigned to this group
Prospective validation dataset
A prospective validation dataset was conducted to distinguish the diagnostic performance of the cytopathologists, AICS, and AICS-assisted cytopathologists, in which 2,780 eligible slides from 2,780 individuals were obtained and prospectively labeled between August 28, 2020 and October 16, 2020 at SYSMH.
No interventions assigned to this group
Randomized controlled trial
A prospective randomized controlled trial was conducted to compare the performance of the cytopathologists, AICS, and AICS-assisted cytopathologists in SYSMH. Here, 618 slides were collected between August 13, 2020, and December 14, 2020, to build the SYSMH randomized controlled trial. The remaining 608 slides after quality control were randomly assigned (1:1:1) to the AICS group (n = 201), the cytopathologists group (n = 203), and the AICS-assisted cytopathologists group (n = 204).
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
2. Availability of confirmed diagnostic results of the cervical liquid-based cytological examination, and satisfactory digital images from the liquid-based cytology pap test: at least 5000 uncovered and observable squamous epithelial cells, samples with abnormal cells (atypical squamous cells or atypical glandular cells and above).
Exclusion Criteria
2. Women diagnosed with other malignant tumors other than cervical cancer.
25 Years
65 Years
FEMALE
Yes
Sponsors
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Guangzhou Women and Children's Medical Center
OTHER
The Third Affiliated Hospital of Guangzhou Medical University
OTHER
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
OTHER
Responsible Party
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Herui Yao
Principal Investigator
Principal Investigators
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Herui Yao, PhD
Role: PRINCIPAL_INVESTIGATOR
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Locations
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Guangzhou Women and Children's Medical Center
Guangzhou, Guangdong, China
The Third Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, China
Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, China
Countries
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Other Identifiers
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2020-KY-114
Identifier Type: -
Identifier Source: org_study_id
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