Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer

Completed

• Conditions: Cervical Cancer; Diagnosing Cervical Cytology Grades and Cancer; Diagnostic Platform; Artificial Intelligence

• Registration Number: NCT04551287
• Lead Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Brief Summary

Cervical cancer, the fourth most common cancer globally and the fourth leading cause of cancer-related deaths, can be effectively prevented through early screening. Detecting precancerous cervical lesions and halting their progression in a timely manner is crucial. However, accurate screening platforms for early detection of cervical cancer are needed. Therefore, it is urgent to develop an Artificial Intelligence Cervical Cancer Screening (AICS) system for diagnosing cervical cytology grades and cancer.

Detailed Description

Not available

Study Timeline

• Study Start: 2019-07-01
• Study First Submitted: 2020-09-06
• Study First Submitted QC: 2020-09-10
• Study First Posted: 2020-09-16
• Primary Completion: 2020-12-14
• Study Completion: 2020-12-14
• Status Verified: 2023-08
• Last Update Submitted: 2023-08-06
• Last Update Posted: 2023-08-08

Recruitment & Eligibility

• Status: COMPLETED
• Sex: Female
• Target Recruitment: 16164

Inclusion Criteria

1. Women Aged 25-65 years old.
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

1. Unsatisfactory samples of cervical liquid-based cytological examination: less than 5000 uncovered, observable squamous epithelial cells, and more than 75% of squamous epithelial cells affected because of blood, inflammatory cells, epithelial cells over-overlapping, poor fixation, excessive drying, or contamination of unknown components.
2. Women diagnosed with other malignant tumors other than cervical cancer.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Area under ROC curve (AUC)	Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained	Area under the curve

Secondary Outcome Measures

Name	Time	Method
Sensitivity	Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained	The true positive rate (TPR) of the diagnostic platform, which is the ratio between the number of positive individuals correctly categorized by platform and the total number of actual positive individuals (%).
Specificity	Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained	The true negative rate (TNR) of the diagnostic platform, which is the ratio between the number of negative individuals correctly categorized by platform and the total number of actual negative individuals (%).
Accuracy	Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained	The quantity of true positive (TP) plus true negative (TN) over the quantity of (TP) plus true negative (TN) plus false positive (FP) plus false negative (FN).

Trial Locations

Locations (3)

Sun Yat-Sen Memorial Hospital of Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China

Guangzhou Women and Children's Medical Center; 🇨🇳 Guangzhou, Guangdong, China

The Third Affiliated Hospital of Guangzhou Medical University; 🇨🇳 Guangzhou, Guangdong, China