[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100657764":3},{"organization":4,"outcomesModule":7,"designInfo":42,"detailedDescription":41,"studyPopulation":41,"armGroups":51,"interventions":64,"overallOfficials":41,"centralContacts":41,"locations":41,"responsibleParty":71,"collaborators":41,"id":75,"slug":76,"hasResults":77,"nctId":78,"briefTitle":79,"officialTitle":80,"acronym":81,"eligibilityCriteria":82,"healthyVolunteers":77,"sex":83,"minAge":84,"maxAge":41,"enrollmentInfo":85,"targetDuration":41,"studyType":88,"phases":89,"briefSummary":91,"conditions":92,"keywords":95,"overallStatus":101,"whyStopped":41,"lastUpdateSubmitDate":102,"lastUpdatePostDateStruct":103,"startDateStruct":106,"completionDateStruct":108,"leadSponsor":110,"locationsCount":41},{"fullName":5,"class":6},"The First Affiliated Hospital of Guangzhou Medical University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":41},[9],{"measure":10,"description":11,"timeFrame":12},"Proportion of Participants With an Appropriate Final Management Recommendation","Percentage of evaluable participants whose final physician recommendation is within the acceptable management range determined by the blinded independent expert endpoint committee. Recommendations are classified as routine or annual follow-up, short-interval imaging follow-up, or specialist evaluation\u002Freferral. The expert committee will complete adjudication using index data within approximately 30 days, but the participant-level outcome is the recommendation made at Day 0.","At the index LDCT report (Day 0)",[14,17,20,23,26,30,33,37],{"measure":15,"description":16,"timeFrame":12},"Proportion of Participants With Under-Management","Percentage of evaluable participants whose final physician recommendation is less intensive than the minimum acceptable management level determined by the blinded independent expert endpoint committee.",{"measure":18,"description":19,"timeFrame":12},"Proportion of Expert-Defined Referral Cases Missed by the Final Report","Among participants whom the blinded independent expert endpoint committee determines require specialist evaluation\u002Freferral, the percentage whose final physician report does not recommend specialist evaluation\u002Freferral. The denominator includes only expert-defined referral cases.",{"measure":21,"description":22,"timeFrame":12},"Proportion of Participants With Inappropriate Management Escalation","Percentage of evaluable participants whose final physician recommendation is more intensive than the maximum acceptable management level determined by the blinded independent expert endpoint committee.",{"measure":24,"description":25,"timeFrame":12},"Change in Decision Appropriateness After AI Review in the AI-Assisted Arm","In the AI-assisted arm, the physician's locked pre-AI decision will be compared with the final post-AI decision against the same expert-accepted management range. Results will include: (1) correction rate, the number changing from inappropriate to appropriate divided by the number with an inappropriate pre-AI decision; (2) AI-induced error rate, the number changing from appropriate to inappropriate divided by the number with an appropriate pre-AI decision; and (3) net correction rate, calculated as (number corrected minus number made incorrect) divided by all evaluable participants in the AI-assisted arm. These denominators must be locked in the statistical analysis plan before database lock.",{"measure":27,"description":28,"timeFrame":29},"Physician Response to the AI Recommendation","Time in minutes from the start of physician image review to final report completion, as recorded by the reporting platform or study system.","During the index LDCT reporting session (Day 0)",{"measure":31,"description":32,"timeFrame":29},"Proportion of AI-Assisted Cases With System Failure","Percentage of eligible cases in the AI-assisted arm for which the AI system does not return a usable output because of processing failure, timeout, technical incompatibility, or an output meeting the prespecified abnormal-output fallback criteria.",{"measure":34,"description":35,"timeFrame":36},"Proportion of Participants With a Pulmonary Nodule-Related Management Action Within 90 Days","Percentage of participants with at least one documented pulmonary nodule-related management action during follow-up, including specialist evaluation, repeat chest CT, contrast-enhanced CT, PET-CT, biopsy, or surgery. Each component will also be summarized separately. Information will be obtained from available routine records, participant-provided records, or ethics-approved telephone follow-up.","90 days after the index LDCT examination (allowable window, ±14 days)",{"measure":38,"description":39,"timeFrame":40},"Proportion of Participants With a Reporting-Workflow-Related Management or Data Security Adverse Event","Percentage of participants with a prespecified event potentially related to the assigned reporting workflow, including missed or substantially delayed evaluation of a high-risk nodule, an unnecessary invasive procedure, hospitalization or major psychological\u002Feconomic burden caused by an erroneous recommendation, or a serious data security event. Serious events will also be reported according to applicable ethics and institutional requirements.","From the index LDCT report through 90 days after the index examination (allowable window, ±14 days)",null,{"allocation":43,"interventionModel":44,"interventionModelDescription":41,"primaryPurpose":45,"observationalModel":41,"timePerspective":41,"maskingInfo":46},"RANDOMIZED","PARALLEL","SCREENING",{"masking":47,"maskingDescription":41,"whoMasked":48},"DOUBLE",[49,50],"PARTICIPANT","OUTCOMES_ASSESSOR",[52,58],{"label":53,"type":54,"description":55,"interventionNames":56},"AI-Assisted Pulmonary Nodule Reporting Workflow","EXPERIMENTAL","Physicians first record and lock an initial pulmonary nodule management decision without viewing AI results. They then review locked-version AI outputs, including nodule characteristics, estimated malignancy risk, and a management recommendation, and issue the final physician-signed report. Physicians may accept, modify, or reject the AI recommendation. The AI cannot automatically sign reports or directly instruct participants.",[57],"Other: AI-Assisted Pulmonary Nodule Reporting Workflow",{"label":59,"type":60,"description":61,"interventionNames":62},"Conventional Pulmonary Nodule Reporting Workflow","ACTIVE_COMPARATOR","Physicians interpret LDCT examinations and issue pulmonary nodule management recommendations using the participating branch's conventional clinical reporting workflow. Study AI output is not displayed.",[63],"Other: Conventional Pulmonary Nodule Reporting Workflow",[65,68],{"type":6,"name":53,"description":66,"armGroupLabels":67,"otherNames":41},"A locked-version artificial intelligence decision-support workflow applied after the physician records an initial assessment. The system displays pulmonary nodule location, size, density, morphologic features, estimated malignancy risk, and a suggested management category. The physician retains final responsibility. If the system fails, times out, or produces an abnormal output, the case returns to manual reporting. No online learning or automatic model updating is allowed during the trial.",[53],{"type":6,"name":59,"description":69,"armGroupLabels":70,"otherNames":41},"Standard manual LDCT interpretation and pulmonary nodule management recommendation by qualified physicians using the participating branch's routine reporting process, without access to study AI output.",[59],{"type":72,"investigatorFullName":73,"investigatorTitle":74,"investigatorAffiliation":5,"oldNameTitle":41,"oldOrganization":41},"PRINCIPAL_INVESTIGATOR","Jianxing He","PhD","100657764","ai-assisted-management-of-pulmonary-nodules-found-on-low-dose-ct-in-health-screening-100657764",false,"NCT07841600","AI-Assisted Management of Pulmonary Nodules Found on Low-Dose CT in Health Screening","Clinical Utility of Artificial Intelligence-Assisted Management Decisions for Pulmonary Nodules Detected by Low-Dose Computed Tomography in Health Screening: A Multicenter, Prospective, Cluster-Randomized Controlled Trial","AI-NoduleCare","Inclusion Criteria:\n\n* Age 18 years or older.\n* Undergoing chest low-dose computed tomography at a participating health examination branch during the study recruitment period.\n* At least one pulmonary nodule is identified on the index LDCT and requires risk stratification and a management decision regarding follow-up, repeat imaging, or specialist evaluation.\n* Thin-section reconstructed images are of sufficient quality for clinical interpretation and, where applicable, AI analysis.\n* Required clinical risk information is available, including age, sex, smoking history, history of malignancy, family history of lung cancer, and available prior chest imaging.\n* Included under the ethics committee-approved consent, simplified notification, waiver, and\u002For opt-out process, with no documented refusal of research data use.\n\nExclusion Criteria:\n\n* Previously diagnosed lung cancer or currently receiving lung cancer-related treatment.\n* Known pulmonary metastasis from another malignancy.\n* Imaging findings that clearly require immediate entry into a lung cancer specialty diagnostic or treatment pathway and are not appropriate for routine pulmonary nodule risk-stratified management.\n* An urgent thoracic condition requiring immediate management, such as pneumothorax, large pleural effusion, or acute pulmonary embolism.\n* Severe imaging artifact, incompatible slice thickness or reconstruction, or a lesion type, imaging parameter, or disease extent outside the prespecified locked scope of the AI system.\n* Previous enrollment in this study.\n* Explicit refusal of research data use, or another reason judged by the investigator to make inclusion inappropriate.","ALL","18 Years",{"count":86,"type":87},2000,"ESTIMATED","INTERVENTIONAL",[90],"NA","Pulmonary nodules are frequently found during low-dose computed tomography (LDCT) health screening. The main challenge is not only detecting nodules, but also recommending the appropriate next step, such as routine follow-up, short-interval imaging follow-up, or specialist evaluation. This multicenter cluster-randomized trial will evaluate whether an artificial intelligence (AI)-assisted reporting workflow improves the appropriateness of pulmonary nodule management decisions in health examination settings without increasing under-management or missed referrals. Participating health examination branches, rather than individual participants, will be randomly assigned in a 1:1 ratio to a conventional reporting workflow or an AI-assisted reporting workflow. All final reports will be reviewed and signed by qualified physicians. An independent expert endpoint committee, blinded to study assignment and AI output, will determine the acceptable management range for each case. Approximately 2,000 adults with pulmonary nodules detected on LDCT will be included across at branches.",[93,94],"Pulmonary Nodule","Clinical Decision Support",[96,97,98,99,100],"Artificial Intelligence","Low-Dose Computed Tomography","Pulmonary Nodule Management","Lung Cancer Screening","Human-AI Collaboration","NOT_YET_RECRUITING","2026-09-20",{"date":104,"type":105},"2026-09-25","ACTUAL",{"date":107,"type":87},"2026-10-01",{"date":109,"type":87},"2028-12-31",{"name":5,"class":6}]