Multi-center Application of an AI System for Diagnosis of Cervical Lesions Based on Colposcopy Images

Artificial Intelligence Clinical Trial

Official title:

Multi-center Application of an Artificial Intelligence System for Automatic Real-time Diagnosis of Cervical Lesions Based on Colposcopy Images

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05281939
• Other study ID #: AICC2203
• Status: Recruiting
• Phase: N/A
• 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
• 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

Related Conditions & MeSH terms

• Artificial Intelligence
• Cervical Lesions
• Colposcopy
• Image
• Uterine Cervical Diseases

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