Artificial Intelligence, Cervical Cancer, Diagnosing Cervical Cytology Grades and Cancer, Diagnostic Platform
Conditions
Keywords
Diagnostic Platform, Cervical Cytology Grade Diagnosis, Thinprep Cytologic Test, Artificial Intelligence Deep Learning Algorithm
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.
Interventions
None listed
Sponsors
Guangzhou Women and Children's Medical Center
The Third Affiliated Hospital of Guangzhou Medical University
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
FEMALE
Age
25 Years to 65 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area under ROC curve (AUC) | Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained | Area under the curve |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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 (%). |
| 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 (%). |
| 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). |
Countries
China
Outcome results
None listed