Artificial IntelligenceNCT05281939
Multi-center Application of an AI System for Diagnosis of Cervical Lesions Based on Colposcopy Images
A study testing Artificial intelligence diagnosis in people with Artificial Intelligence and Colposcopy, run by Fujian Maternity and Child Health Hospital at 5 sites in China.
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. Read more about this study
- Who is it for?
- Ages 18 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Colposcopy, Cervical Lesions
- What is being tested?
- Artificial intelligence diagnosis
- Type of treatment
- Pill or oral medicine
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 10,000 participants
- Where
- 5 sitesChina
- Study dates
- 1 Aug 2021to 1 Sep 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
5 sites in 1 country.
5 sites in 1 country
China5 sites, 5 recruiting
- Recruiting
Fujian Maternity and Child Health Hospital
Fuzhou, Fujian, 350001
Binhua Dong, +8613599071900, dbh18-jy@126.com
- Recruiting
Jianou Maternal and child Health Care Hospital
Nanping
Huihua Ge
- Recruiting
Mindong Hospital of Ningde City
Ningde, Fujian, 352000
Fang Xie, M.D, +8613860388999
- Recruiting
Ningde Hospital affiliated to Ningde Normal University
Ningde
Wenfang Jin
- Recruiting
Quanzhou First Hospital
Quanzhou
Yuchun Lv
About this study
Official title: Multi-center Application of an Artificial Intelligence System for Automatic Real-time Diagnosis of Cervical Lesions Based on Colposcopy Images
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.
Read the detailed 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.
- Conditions
- Artificial Intelligence, Colposcopy, Cervical Lesions, Image
- Being tested
- Artificial intelligence diagnosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence diagnosisDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| Artificial intelligence diagnostic group | Active comparator | 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. Receives: Diagnostic Test: Artificial intelligence diagnosis |
| Gynecologist diagnostic Group | No intervention | 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 being measured
The primary outcome is the main question the study is designed to answer.
HPV testing
Cervical exfoliated cells were collected for HPV testing
Measured: o month
Cervical cytology testing
Cervical exfoliated cells were collected for cytological and pathological examination.
Measured: 0 month
Cervical histopathological examination
Cervical tissue was collected for histopathological examination
Measured: 0 month
Accuracy of CIN2+ diagnosis
Accuracy in the diagnosis of cervical intraepithelial neoplasia grade 2 or worse.
Measured: 0 month
Accuracy of CIN3+ diagnosis
Accuracy in the diagnosis of cervical intraepithelial neoplasia grade 3 or worse.
Measured: 0 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Mar 2022
- Study start1 Aug 2021
- Primary completion1 Aug 2024 (estimated)
- Study completion1 Sep 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Participants were randomly assigned to an Artificial intelligence (AI) group or a control group.
- Masking
- Triple blind
- Who is masked
- Participant, Investigator, Outcomes assessor
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fujian Maternity and Child Health Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Binhua Dong, Fujian Maternity and Child Health Hospital
- Investigators
- Pengming Sun, PhD, Study chair, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05281939
- Sponsor's protocol ID
- AICC2203
- Status last verified
- Nov 2023
- First submitted
- 7 Mar 2022
- Last updated
- 18 Nov 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multi-center Application of an Artificial Intelligence System for Automatic Real-time Diagnosis of Cervical Lesions Based on Colposcopy Images
- ClinicalTrials.gov ID
- NCT05281939
- Acronym / sponsor ID
- AICC2203
- Conditions
- Artificial Intelligence, Colposcopy, Cervical Lesions, Image
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Fujian Maternity and Child Health HospitalUniversity, hospital or other
- Study start
- 1 Aug 2021 (actual)
- Primary completion
- 1 Aug 2024 (estimated)
- Study completion
- 1 Sep 2024 (estimated)
- First posted
- 16 Mar 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, investigator, outcomes assessor)
- Number of arms
- 2
- Model description
- Participants were randomly assigned to an Artificial intelligence (AI) group or a control group.
| Arm | Type | Receives |
|---|---|---|
| Artificial intelligence diagnostic group | Active comparator | Diagnostic Test: Artificial intelligence diagnosisWomen who show abnormalities in cervical cancer screening and require referral for colposcopy. Colposcopy was performed with the aid of an Artificial intelligence (AI) system. |
| Gynecologist diagnostic Group | No intervention | –Women who show abnormalities in cervical cancer screening and require referral for colposcopy. Colposcopy is performed independently by a gynecologist without any external assistance. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence diagnosisParticipants 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. | Diagnostic test | Artificial intelligence diagnostic group |
Primary outcome
HPV testing
Time frame: o month
Cervical exfoliated cells were collected for HPV testing
Cervical cytology testing
Time frame: 0 month
Cervical exfoliated cells were collected for cytological and pathological examination.
Cervical histopathological examination
Time frame: 0 month
Cervical tissue was collected for histopathological examination
Accuracy of CIN2+ diagnosis
Time frame: 0 month
Accuracy in the diagnosis of cervical intraepithelial neoplasia grade 2 or worse.
Accuracy of CIN3+ diagnosis
Time frame: 0 month
Accuracy in the diagnosis of cervical intraepithelial neoplasia grade 3 or worse.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Nov 2023.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Pengming Sun, PhD study chair
Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial intelligence diagnosis in people with Artificial Intelligence and Colposcopy, run by Fujian Maternity and Child Health Hospital at 5 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 18 Nov 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Binhua Dong (+8613599071900, dbh18-jy@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
HPV testing (time frame: o month). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Nov 2023.