Ignite Creation Date:
2025-12-26 @ 5:23 PM
Ignite Modification Date:
2025-12-28 @ 7:51 PM
Study NCT ID:
NCT06963606
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-08-15
First Post:
2025-04-28
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Effectiveness of Computer-Aided Detection Chest X-Ray Screening for Improving Tuberculosis Diagnostic Yield in Chinese Primary Health Care Settings: Study Protocol for a Prospective Cluster Randomized Controlled Trial
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D014376', 'term': 'Tuberculosis'}], 'ancestors': [{'id': 'D009164', 'term': 'Mycobacterium Infections'}, {'id': 'D000193', 'term': 'Actinomycetales Infections'}, {'id': 'D016908', 'term': 'Gram-Positive Bacterial Infections'}, {'id': 'D001424', 'term': 'Bacterial Infections'}, {'id': 'D001423', 'term': 'Bacterial Infections and Mycoses'}, {'id': 'D007239', 'term': 'Infections'}]}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2024-10-28', 'size': 2481863, 'label': 'Study Protocol and Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'Prot_SAP_000.pdf', 'typeAbbrev': 'Prot_SAP', 'uploadDate': '2025-05-06T22:07', 'hasProtocol': True}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['PARTICIPANT']}, 'primaryPurpose': 'SCREENING', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 12000}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-09-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-08', 'completionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-08-11', 'studyFirstSubmitDate': '2025-04-28', 'studyFirstSubmitQcDate': '2025-05-06', 'lastUpdatePostDateStruct': {'date': '2025-08-15', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-05-09', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'The difference in the diagnostic yield of pulmonary tuberculosis screening in township medical and health institutions between the intervention group and the control group.', 'timeFrame': 'Baseline and 6 months', 'description': 'Diagnostic yield = The number of people who visited the hospital for chest DR Examination and were ultimately determined to require further relevant examinations and were diagnosed in the tuberculosis specific disease reporting system/the number of people who underwent chest DR Examination'}], 'secondaryOutcomes': [{'measure': 'The difference in the average number of days from visiting township medical and health institutions to the diagnosis of pulmonary tuberculosis between patients in the intervention group and the control group', 'timeFrame': 'Baseline and 6 months', 'description': "The number of days for confirmed diagnosis = the time of diagnosis reported by the tuberculosis specific disease system - the time of the patient's visit"}, {'measure': 'The differences in the accuracy of different tuberculosis screening strategies between the intervention group and the control group', 'timeFrame': 'Baseline and 6 months', 'description': 'The differences in the accuracy of different tuberculosis screening strategies between the intervention group and the control group, including sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, Youden index, and AUC'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['tuberculosis', 'computer-assisted detection'], 'conditions': ['Tuberculosis (TB)']}, 'descriptionModule': {'briefSummary': 'The global incidence rate and mortality of tuberculosis (TB) pose a challenge to achieving the goals set out in the tuberculosis eradication strategy and the SDGs by 2030. At present, timely and accessible early detection methods for tuberculosis are still a major obstacle. In this context, the emergence of artificial intelligence (AI), especially the AI-assisted chest X-ray (CXR) in the field of diagnostic imaging, has proved the potential to significantly improve the speed and accuracy of tuberculosis diagnosis. However, the extent to which these technologies can affect the broader tuberculosis care cascade, especially by reducing the diagnostic time in the population level, has not yet been explored. The proposed project plans to use the certified AI-assisted CXR system (JF CXR-1) for tuberculosis screening, which aims not only to integrate AI into the diagnosis process, but also to critically assess its impact on the overall tuberculosis care cascade. The selected location for this project is Yichang City in western Hubei Province, China, which is facing a high TB burden. The city has established a strong city-wide health big data platform ten years ago, providing the basis for this project. The project will first optimize the AI-assisted CXR system through retrospective imaging to validate the accuracy of case screening (Stage Ⅰ). Secondly, the project will shift its focus to the real world, where cluster randomized controlled trials will be conducted in primary-care settings (Stage Ⅱ). In this stage, the effectiveness of the AI-assisted CXR system in reducing the diagnostic time of TB cases will be evaluated by comparing with those settings without using the tool. In stage Ⅲ, the qualitative and quantitative methods will be used to evaluate the generalization, practicality, and feasibility of extending the screening strategy in various community environments. If the AI-assisted screening strategy is proven accurate, effective, and sustainable, it may pave the way for its widespread adoption in primary healthcare institutions and other grassroots areas in China. This can not only improve the timeliness of tuberculosis diagnosis, but also help to allocate medical resources more effectively and significantly reduce tuberculosis-related incidence and mortality, bringing positive changes to global public health. In addition, the results of the project can also provide information for policy decisions and guide the formulation of strategies to prioritize the integration of AI into health care, which can not only fight against tuberculosis but also a series of other diseases.', 'detailedDescription': "Research Plan\n\n1. Questionnaire survey - Collect general baseline characteristics The general data characteristics involved in this study include age, gender, marital status, occupation, income, place of residence, lifestyle (smoking, drinking), past medical history, symptom screening, nutritional status, sleep status, mental health status, cognition and acceptance of the application of artificial intelligence in imaging.\n2. Pre-test Before the formal cluster randomized controlled trial, a pre-trial involving 100 residents who met the inclusion and exclusion criteria was conducted in two township medical and health institutions to preliminarily verify its effectiveness through the randomized controlled trial and provide suggestions for the performance and potential adjustments of the artificial intelligence-assisted CXR system before the comprehensive trial.\n3. Cluster randomized controlled trial 3.1 Intervention Group The subjects who visited the township medical and health institutions in the intervention group, underwent chest DR Examinations, and signed the informed consent form were included in the intervention group, and the visiting time was recorded.\n\nAfter the completion of the chest DR Examination, the chest X-ray was analyzed by the artificial intelligence-assisted system (JF CXR-1) to identify the potential signs of tuberculosis. Meanwhile, the doctor analyzed the results of the chest X-ray. After the analysis results were confirmed, the initial judgment results of the doctor and the analysis results of the artificial intelligence-assisted system were recorded, and the reading results of the artificial intelligence-assisted system were fed back to the doctor. Review the doctor's comprehensive analysis results of the artificial intelligence-assisted system, make a final judgment on the chest X-ray results, and determine whether further relevant examinations (such as etiological examination, CT examination, etc.) are needed. Record the doctor's judgment results.\n\nFollow up and record the time of diagnosis reported by the tuberculosis specific disease system.\n\n3.2 Control Group The subjects who visited the township medical and health institutions in the control group, underwent chest DR Examinations, and signed the informed consent form were included in the control group, and the visiting time was recorded.\n\nAfter the chest DR Examination is completed, a regular doctor reviews the films without using an artificial intelligence-assisted system. Once the results of the regular doctor's review are confirmed, a final judgment is made on whether further related examinations (such as etiological tests, CT scans, etc.) are needed, and the doctor's judgment is recorded.\n\nFollow up and record the time of diagnosis reported by the tuberculosis specific disease system."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'minimumAge': '15 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Eligibility criteria Inclusion criteria\n\nParticipants must receive medical treatment at the primary healthcare hospitals in Yichang City, Hubei Province, and underwent chest X-ray examinations. The participants have to meet the following criteria:\n\n1. ≥15 years old.\n2. Appearance of tuberculosis-related respiratory symptoms or signs.\n3. Individuals not previous diagnosed with active pulmonary tuberculosis.\n4. Capable of completing pathogen examinations and subsequent related inspections.\n\nExclusion criteria\n\nThose who meet any of the below criteria will be excluded:\n\n1. Previous diagnosed with extrapulmonary tuberculosis or latent tuberculosis infection.\n2. The quality of Chest X-ray images did not meet the standard requirements.\n3. Unrecognized identity information participants.\n\nWithdrawal Criteria In the event of participant withdrawal, study personnel should use methods such as phone communication, as well as scheduling follow-up appointments, to re-establish contact with the participants. The aim is to understand the reasons for withdrawal, diligently record the reasons for withdrawal, and endeavor to complete the study-related research content.\n\n1. Participants who are lost to follow-up or who do not complete the follow-up period.\n2. Participants who experience a sudden and serious illness or choose not to continue participating in the study.'}, 'identificationModule': {'nctId': 'NCT06963606', 'briefTitle': 'Effectiveness of Computer-Aided Detection Chest X-Ray Screening for Improving Tuberculosis Diagnostic Yield in Chinese Primary Health Care Settings: Study Protocol for a Prospective Cluster Randomized Controlled Trial', 'organization': {'class': 'OTHER', 'fullName': 'Peking Union Medical College'}, 'officialTitle': 'Artificial Intelligence-assisted Chest X-ray in TB Screening: Effectiveness of Enhancing the Care Cascades in Chinese Primary-care Settings (ACCESS-CARE)', 'orgStudyIdInfo': {'id': 'CAMS&PUMC-IEC-2025-044'}, 'secondaryIdInfos': [{'id': '#24-564', 'type': 'OTHER_GRANT', 'domain': 'China Medical Board (CMB)'}]}, 'armsInterventionsModule': {'armGroups': [{'type': 'ACTIVE_COMPARATOR', 'label': 'computer-assisted detection', 'interventionNames': ['Diagnostic Test: Artificial intelligence-assisted chest X-ray in TB screening']}, {'type': 'PLACEBO_COMPARATOR', 'label': 'Do not use computer-assisted detection', 'interventionNames': ['Other: Routine doctors analyze the process of chest X-rays']}], 'interventions': [{'name': 'Artificial intelligence-assisted chest X-ray in TB screening', 'type': 'DIAGNOSTIC_TEST', 'description': "After the completion of the chest DR Examination, the chest X-ray was analyzed by the artificial intelligence-assisted system (JF CXR-1) to identify the potential signs of tuberculosis. Meanwhile, the doctor analyzed the results of the chest X-ray. After the analysis results were confirmed, the initial judgment results of the doctor and the analysis results of the artificial intelligence-assisted system were recorded, and the reading results of the artificial intelligence-assisted system were fed back to the doctor. Review the doctor's comprehensive analysis results of the artificial intelligence-assisted system, make a final judgment on the chest X-ray results, and determine whether further relevant examinations (such as etiological examination, CT examination, etc.) are needed. Record the doctor's judgment results. Follow up and record the time of diagnosis reported by the tuberculosis specific disease system.", 'armGroupLabels': ['computer-assisted detection']}, {'name': 'Routine doctors analyze the process of chest X-rays', 'type': 'OTHER', 'description': "After the chest DR Examination is completed, a regular doctor reviews the films without using an artificial intelligence-assisted system. Once the results of the regular doctor's review are confirmed, a final judgment is made on whether further related examinations (such as etiological tests, CT scans, etc.) are needed, and the doctor's judgment is recorded. Follow up and record the time of diagnosis reported by the tuberculosis specific disease system", 'armGroupLabels': ['Do not use computer-assisted detection']}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Yang Xuelin', 'role': 'CONTACT', 'email': 'yangxuelin321@163.com', 'phone': '0536-13964768397'}, {'name': 'Su Xiaoyou Prof', 'role': 'CONTACT'}], 'overallOfficials': [{'name': 'Wang Ye Prof', 'role': 'STUDY_CHAIR', 'affiliation': 'chool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP'], 'timeFrame': 'January 2025 - December 2027', 'ipdSharing': 'YES', 'description': 'Gender, age, tuberculosis diagnosis situation, diagnosis delay situation, detection rate situation, AI accuracy', 'accessCriteria': 'Personnel who have been approved by the research leader can access the declassified data'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Xuelin Yang', 'class': 'OTHER'}, 'collaborators': [{'name': 'China Medical Board (CMB)', 'class': 'UNKNOWN'}, {'name': 'Yichang Center for Disease Control and Prevention, China', 'class': 'UNKNOWN'}, {'name': 'JF Intelligent Healthcare Medical Technology Co', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Project researcher', 'investigatorFullName': 'Xuelin Yang', 'investigatorAffiliation': 'Peking Union Medical College'}}}}