Clinical trial · Observational
Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence | — | UNRESOLVED | — |
| Cervical Cancer | Malignant Cervical Neoplasm | CURATED_EXACT | 0.92 |
| Diagnosing Cervical Cytology Grades and Cancer | — | UNRESOLVED | — |
| Diagnostic Platform | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (6)
- label
- Training dataset
- description
- 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).
- label
- SYSMH internal validation dataset
- description
- 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).
- label
- TAHGMU external validation dataset
- description
- 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).
- label
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 25 Years
- Maximum age
- 65 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available