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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Artificial Intelligence, Colposcopy, Cervical Lesions, Image

Intervention / Treatment
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* Diagnostic Test: Artificial intelligence diagnosis


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: Participants were randomly assigned to an Artificial intelligence (AI) group or a control group.
Masking: Triple


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: Artificial intelligence diagnostic group<br>Women who show abnormalities in cervical cancer screening and require referral for colposcopy. Colposcopy was performed with the aid of an Artificial intelligence (AI) system. | Diagnostic Test: Artificial intelligence diagnosis<br>* 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.<br>|
| No Intervention: Gynecologist diagnostic Group<br>Women who show abnormalities in cervical cancer screening and require referral for colposcopy. Colposcopy is performed independently by a gynecologist without any external assistance. | |

What is the study measuring?
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Primary Outcome Measures

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