Clinical trial · Observational
AI Model for Cervical Cancer Detection From Colposcopy Images
Development and Evaluation of an Artificial Intelligence Model for Cervical Cancer Detection From Colposcopic Images
- 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 is a significant health issue, particularly in low-income countries, where late diagnosis and limited access to screenings contribute to high mortality rates. This study aims to develop and evaluate an artificial intelligence (AI) model to analyze colposcopic images for detecting cervical cancer more accurately and efficiently. Colposcopy, a procedure used to examine the cervix for signs of cancer, relies heavily on doctors' expertise, leading to inconsistent results. The current gold standard, colposcopy-directed biopsy, is invasive and can cause complications. The hypothesis is that an AI model can outperform traditional methods in identifying cervical abnormalities, providing a reliable and scalable solution for early detection, especially in underserved areas. By automating the analysis process, the AI model aims to reduce reliance on trained personnel, making cervical cancer screening more accessible and improving early diagnosis and treatment outcomes. The study will create a diverse dataset of colposcopy images from various sources and develop the AI model. The model's performance will be validated in clinical settings, assessing its accuracy in classifying cancer stages and identifying transformation zones. The impact on early detection, patient outcomes, and model usability will be evaluated, as well as its generalizability across different healthcare environments. The goal is to enhance the accuracy and efficiency of cervical cancer screening, ultimately reducing mortality rates and improving patient care.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Uterine Cervical Neoplasms | Cervical Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Colposcopy | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Self reported uterine distress undergoing colposcopic evaluation
- description
- The study group consists of women undergoing cervical cancer screening. This includes those with suspected cervical abnormalities identified through initial cytology or HPV tests and referred for colposcopy. The study group also includes women without apparent cervical abnormalities to ensure a comprehensive dataset. The aim is to develop an AI model capable of accurately diagnosing cervical cancer and identifying transformation zones in colposcopic images. This diverse group allows for the evaluation of the AI model across a wide range of conditions and exposures, enhancing its generalizability and clinical utility.
- interventionNames
- Diagnostic Test: Colposcopy
Primary outcomes (1)
- measure
- Swede Score Evaluation
- timeFrame
- 12 hours
- description
- The Swede score will be used to evaluate the severity of cervical abnormalities identified in colposcopic images. This scoring system assesses various colposcopic findings, including acetowhiteness, lesion size, and vascular patterns, to determine the effectiveness of the AI model in evaluating cervical abnormalities.
Secondary outcomes (1)
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Female patients of age 18 years or older can be selectedas subjects. * Individuals willing to participate in cervical cancerscreening. * Availability for colposcopic examination. * Women with no history of hysterectomy (total removalof the uterus). * Women with no current or prior diagnosis of cervicalcancer. * Availability of relevant medical records forconfirmation and comparison purposes. Exclusion Criteria: * Pregnant women, given the potential impact onscreening results and the need for specialconsiderations during pregnancy. * Individuals with severe medical conditions orcircumstances that may make colposcopic examinationinappropriate or unsafe. * Patients with conditions that could interfere with theaccuracy of the screening results, such as severevaginal bleeding. * Follow-up screenings.
References
Publications (0)
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