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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05281939
Other study ID # AICC2203
Secondary ID
Status Recruiting
Phase N/A
First received
Last updated
Start date August 1, 2021
Est. completion date September 1, 2024

Study information

Verified date November 2023
Source Fujian Maternity and Child Health Hospital
Contact Binhua Dong
Phone +8613599071900
Email dbh18-jy@126.com
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial Summary

The application of artificial intelligence in image recognition of cervical lesions diagnosis has become a research hotspot in recent years. The analysis and interpretation of colposcopy images play an important role in the diagnosis,prevention and treatment of cervical precancerous lesions and cervical cancer. At present, the accuracy of colposcopy detection is still affected by many factors. The research on the diagnosis system of cervical lesions based on multimodal deep learning of colposcopy images is a new and significant research topic. Based on the large database of cervical lesions diagnosis images and non-images, the research group established a multi-source heterogeneous cervical lesion diagnosis big data platform of non-image and image data. Research the lesions segmentation and classification model of colposcopy image based on convolutional neural network, explore the relevant medical data fusion network model that affects the diagnosis of cervical lesions, and realize a multi-modal self-learning artificial intelligence cervical lesion diagnosis system based on colposcopy images. The application efficiency of the artificial intelligence system in the real world was explored through the cohort, and the intelligent teaching model and method of cervical lesion diagnosis were further established based on the above intelligent system.


Description:

Based on previous studies and clinical practice, this study carried out a multi center application in Fujian Province, China. In this study, Fujian Maternity and Child Health Hospital and Mindong Hospital of Ningde City were included, with a total of 10000 participants who have undergone colposcopy examination were enrolled. In the first place, the investigators will build a multimodal artificial intelligence diagnostic system by combining colposcopy images with other non-image data, such as the results of HPV tests and Thinprep cytologic test (TCT) and so on. And then, use standardized colposcopy images and non-image medical data of cervical lesions in different medical institutions to verify the efficacy of the multimodal intelligent diagnostic system for cervical lesions. What's, more, the investigators will establish artificial intelligence cohorts (assisted by intelligent systems) and traditional physician cohorts (assisted by expert, senior and primary physicians) to contrast the diagnosis results of the multimodal artificial intelligence diagnostic system and different levels of colposcopy doctors. And can also bidirectionally analyse the diagnostic efficacy and differences of the system and colposcopy physicians of different levels, and evaluate the performance of this diagnostic system for real-world applications.


Recruitment information / eligibility

Status Recruiting
Enrollment 10000
Est. completion date September 1, 2024
Est. primary completion date August 1, 2024
Accepts healthy volunteers Accepts Healthy Volunteers
Gender Female
Age group 18 Years and older
Eligibility Inclusion Criteria: - Married woman - Woman aged 18 and over - Woman with an intact cervix - Patients with abnormal results in cervical cancer screening - Be able to understand this study and have signed a written informed consent Exclusion Criteria: - Woman with acute reproductive tract inflammation - History of pelvic radiotherapy surgery - Woman with mental disorder - Patients with history of other malignant tumors - Refuse to participate in this study

Study Design


Intervention

Diagnostic Test:
Artificial intelligence diagnosis
Participants were divided into the intervention group and the control group using a random number table. The intervention group participants' cervical colposcopic image data and non-image data as follow:age, the infection of high-risk human papillomavirus (HR-HPV),the type of HR-HPV infection,the duration of HR-HPV infection, cervical cytology (TCT) results, HIV/sexually transmitted infection history, marriage and childbearing history,first sexual life history, sexual partner history, smoking history,oral contraceptives history,the use of immune drug and possible clinical symptoms of cervical lesions such as postcoital bleeding, abnormal vaginal secretions, vaginal bleeding symptoms, etc.

Locations

Country Name City State
China Fujian Maternity and Child Health Hospital Fuzhou Fujian
China Jianou Maternal and child Health Care Hospital Nanping
China Mindong Hospital of Ningde City Ningde Fujian
China Ningde Hospital affiliated to Ningde Normal University Ningde
China Quanzhou First Hospital Quanzhou

Sponsors (1)

Lead Sponsor Collaborator
Fujian Maternity and Child Health Hospital

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary HPV testing Cervical exfoliated cells were collected for HPV testing o month
Primary Cervical cytology testing Cervical exfoliated cells were collected for cytological and pathological examination. 0 month
Primary Cervical histopathological examination Cervical tissue was collected for histopathological examination 0 month
Primary Accuracy of CIN2+ diagnosis Accuracy in the diagnosis of cervical intraepithelial neoplasia grade 2 or worse. 0 month
Primary Accuracy of CIN3+ diagnosis Accuracy in the diagnosis of cervical intraepithelial neoplasia grade 3 or worse. 0 month
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