Tuberculosis (TB)NCT06963606
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
A study testing Artificial intelligence-assisted chest X-ray in TB screening and Routine doctors analyze the process of chest X-rays in people with Tuberculosis (TB), run by Xuelin Yang at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 15 years and olderHealthy volunteers can take part
- Conditions the study is for
- Tuberculosis (TB)
- What is being tested?
- Artificial intelligence-assisted chest X-ray in TB screening and Routine doctors analyze the process of chest X-rays
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 22,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2025to 31 Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 15 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
None listed.
Who cannot join
- Those who meet any of the below criteria will be excluded:
- Diagnosed with extrapulmonary tuberculosis or latent tuberculosis infection during the current visit.
- The quality of Chest X-ray images did not meet the standard requirements.
- Unrecognized identity information participants.
- Withdrawal Criteria
- Participants who are lost to follow-up or who do not complete the follow-up period.
- Participants who experience a sudden and serious illness or choose not to continue participating in the study.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Su Xiaoyou Prof contact
China1 site, 1 recruiting
- Recruiting
Township Health-care settings in Yichang City
Yichang, Hubei, 443000
Jianhua Liu, 86-13964768397, 645454845@qq.com
About this study
Official title: Artificial Intelligence-assisted Chest X-ray in TB Screening: Effectiveness of Enhancing the Care Cascades in Chinese Primary-care Settings (ACCESS-CARE)
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.
Read the detailed description
Research Plan
- 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.
- 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.
- 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.
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.
3.2 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.
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.
- Conditions
- Tuberculosis (TB)
- Being tested
- Artificial intelligence-assisted chest X-ray in TB screening, Routine doctors analyze the process of chest X-rays
- Keywords
- tuberculosis, computer-assisted detection
- Medical subject headings
- Tuberculosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence-assisted chest X-ray in TB screeningDiagnostic test
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.
Routine doctors analyze the process of chest X-raysOther
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
| Study group | Role | What they receive |
|---|---|---|
| computer-assisted detection | Active comparator | Not described Receives: Diagnostic Test: Artificial intelligence-assisted chest X-ray in TB screening |
| Do not use computer-assisted detection | Placebo comparator | Not described Receives: Other: Routine doctors analyze the process of chest X-rays |
What is being measured
The primary outcome is the main question the study is designed to answer.
The difference in the diagnostic yield of pulmonary tuberculosis screening in township medical and health institutions between the intervention group and the control group.
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
Measured: pre-intervention (via retrospective analysis of historical data), post-intervention (six and twelve months after the intervention starts)
Secondary outcomes (2)
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
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
Measured: pre-intervention (via retrospective analysis of historical data), post-intervention (six and twelve months after the intervention starts)
The differences in the accuracy of different tuberculosis screening strategies between the intervention group and the control group
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
Measured: pre-intervention (via retrospective analysis of historical data), post-intervention (six and twelve months after the intervention starts)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 May 2025
- Study start1 Sep 2025
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Tuberculosis (TB)Studies 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
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 22,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xuelin Yang
- Sponsor type
- University, hospital or other
- Collaborators
- China Medical Board (CMB), Yichang Center for Disease Control and Prevention, China, JF Intelligent Healthcare Medical Technology Co
- Responsible party
- Sponsor-investigator, Xuelin Yang, Peking Union Medical College
- Investigators
- Wang Ye Prof, Study chair, chool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06963606
- Sponsor's protocol ID
- CAMS&PUMC-IEC-2025-044
- Other IDs
- #24-564
- Status last verified
- Dec 2025
- First submitted
- 28 Apr 2025
- Last updated
- 31 Dec 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Gender, age, tuberculosis diagnosis situation, diagnosis delay situation, detection rate situation, AI accuracy
Publications
Yang X, Zhang H, Jiang W, Xin Y, Dai Z, Li Z, Xiong J, Sun R, Shao J, Yu J, Wang Y, Su X, Liu J, Li Z. Effectiveness of computer-aided detection chest X-ray screening for improving tuberculosis diagnostic yield in Chinese primary healthcare settings: study protocol for a prospective cluster randomised controlled trial. BMJ Open. 2026 Feb 4;16(2):e112124. doi: 10.1136/bmjopen-2025-112124.
PubMed 41638733
Study at a glance
From the registry record, last updated by the sponsor on 31 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-assisted Chest X-ray in TB Screening: Effectiveness of Enhancing the Care Cascades in Chinese Primary-care Settings (ACCESS-CARE)
- ClinicalTrials.gov ID
- NCT06963606
- Acronym / sponsor ID
- CAMS&PUMC-IEC-2025-044
- Conditions
- Tuberculosis (TB)
- Keywords
- tuberculosis, computer-assisted detection
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 22,000 participants (estimated)
- Lead sponsor
- Xuelin YangUniversity, hospital or other
- Collaborators
- China Medical Board (CMB), Yichang Center for Disease Control and Prevention, China, JF Intelligent Healthcare Medical Technology Co
- Study start
- 1 Sep 2025 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 9 May 2025 (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 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.
Detailed description
Research Plan
- 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.
- 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.
- 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.
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.
3.2 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.
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.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| computer-assisted detection | Active comparator | Diagnostic Test: Artificial intelligence-assisted chest X-ray in TB screening |
| Do not use computer-assisted detection | Placebo comparator | Other: Routine doctors analyze the process of chest X-rays |
Who may qualify
- Ages
- 15 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (0 listed)
Key exclusion criteria (7 listed)
- Diagnosed with extrapulmonary tuberculosis or latent tuberculosis infection during the current visit.
- The quality of Chest X-ray images did not meet the standard requirements.
- Unrecognized identity information participants.
- Withdrawal Criteria
- Participants who are lost to follow-up or who do not complete the follow-up period.
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-assisted chest X-ray in TB screeningAfter 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. | Diagnostic test | computer-assisted detection |
| Routine doctors analyze the process of chest X-raysAfter 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 | Other | Do not use computer-assisted detection |
Primary outcome
The difference in the diagnostic yield of pulmonary tuberculosis screening in township medical and health institutions between the intervention group and the control group.
Time frame: pre-intervention (via retrospective analysis of historical data), post-intervention (six and twelve months after the intervention starts)
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
2 secondary outcomes
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
Time frame: pre-intervention (via retrospective analysis of historical data), post-intervention (six and twelve months after the intervention starts)
The differences in the accuracy of different tuberculosis screening strategies between the intervention group and the control group
Time frame: pre-intervention (via retrospective analysis of historical data), post-intervention (six and twelve months after the intervention starts)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Dec 2025.
- Derived from this study: PubMed 41638733Yang X, Zhang H, Jiang W, Xin Y, Dai Z, Li Z, Xiong J, Sun R, Shao J, Yu J, Wang Y, Su X, Liu J, Li Z. Effectiveness of computer-aided detection chest X-ray screening for improving tuberculosis diagnostic yield in Chinese primary healthcare settings: study protocol for a prospective cluster randomised controlled trial. BMJ Open. 2026 Feb 4;16(2):e112124. doi: 10.1136/bmjopen-2025-112124.
- Individual participant dataSharing planned: Yes. Gender, age, tuberculosis diagnosis situation, diagnosis delay situation, detection rate situation, AI accuracy
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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.
Su Xiaoyou Prof contact
Wang Ye Prof study chair
chool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial intelligence-assisted chest X-ray in TB screening and Routine doctors analyze the process of chest X-rays in people with Tuberculosis (TB), run by Xuelin Yang at 1 site 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 0 inclusion and 7 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: looking for participants now, as of the sponsor's last update on 31 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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. Some participants in this study may receive a placebo. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yang Xuelin (0536-13964768397, yangxuelin321@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome 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. (time frame: pre-intervention (via retrospective analysis of historical data), post-intervention (six and twelve months after the intervention starts)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06963606, which also lists 1 linked publication.
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 31 Dec 2025.