Trial · NCT07895147
Clinical Application of Artificial Intelligence Methods in Diagnostic Imaging of Pulmonary Nodules
NCT07895147, Active not recruiting, Shanghai East Hospital
3 sourcestrials-2026-11-12-0.5.0
Cite this record
ConductScience Library. Clinical Application of Artificial Intelligence Methods in Diagnostic Imaging of Pulmonary Nodules. Trials, record NCT07895147. Release trials-2026-11-12-0.5.0, 12 Nov 2026. https://conductscience.org/library/trials/NCT07895147
On this page
Trial
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
- Title
- Clinical Application of Artificial Intelligence Methods in Diagnostic Imaging of Pulmonary Nodules
- NCT number
- NCT07895147
- Status
- Active not recruiting
- Enrollment
- 10,000 participants, estimated
- Lead sponsor
- Shanghai East Hospital
- Conditions
- Pulmonary Nodules, Lung Neoplasms, Lung Diseases
- Interventions
- No intervention
- First posted
- 2026-10-26
- Last updated
- 2026-10-26
Source ClinicalTrials.gov
Registered outcomes5
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description |
|---|---|---|---|
| Primary | The Area Under the Receiver Operating Characteristic Curve (AUC) for AI Model Diagnosis of Pulmonary Nodules | Baseline to 1 month after obtaining pathological results | Using pathological results as the gold standard, calculate the area under the operating characteristic curve (AUC) of the AI model for diagnosing pulmonary nodules at the nodule level, and report the 95% confidence interval. If there are multiple nodules in the same patient, adjust according to patient clustering. |
| Secondary | Sensitivity of AI model in diagnosing pulmonary nodules | Baseline to 1 month after obtaining pathological results | Using pathological results as the gold standard, calculate the sensitivity of the AI model in diagnosing pulmonary nodules, defined as the true positive number/(true positive number+false negative number), and summarize it in proportion and 95% confidence interval. |
| Secondary | Specificity of artificial intelligence models in diagnosing pulmonary nodules | Baseline to 1 month after obtaining pathological results | Using pathological results as the gold standard, calculate the specificity of the AI model in diagnosing pulmonary nodules, defined as the true negative number/(true negative number+false positive number), and summarize it in proportion and 95% confidence interval. |
| Secondary | Consistency between AI and Radiologists (Kappa) | Assessed during the data analysis phase (September 2026 to December 2026) | Agreement in diagnosing benign and malignant pulmonary nodules between the AI and radiologists, measured by Kappa statistics. |
| Secondary | Diagnostic Time | Assessed during the data analysis phase (September 2026 to December 2026) | The time taken by radiologists to review the CT images and provide a diagnosis. |
Source ClinicalTrials.gov
Outcomes0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
None in this release.
Results by group0
Each measurement the trial posted, group by group, in the words and numbers it posted.
None in this release.
Participant flow0
How many started, completed or left each group, period by period.
None in this release.
Adverse events0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
None in this release.
Adverse event totals0
The totals the trial posted for each group.
None in this release.
Papers0
The papers the trial lists on ClinicalTrials.gov.
None in this release.
Grants0
The grants NIH RePORTER links to the trial, newest funded first: NIH's, and some of AHRQ's, CDC's, FDA's and VA's. Each shows its title, principal investigators, organization and funder in its newest year.
None in this release.
Trial comparisons0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
None in this release.
Tests0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
None in this release.
Data dates
The day each source was copied for this release.
Sources 3
- ClinicalTrials.gov's posted results, through CTTI's AACTFreely available. Acknowledgement requested
- ClinicalTrials.govNo license stated
- NIH RePORTER's clinical studies and project filesPublic domain
Each source's terms
- ClinicalTrials.gov's posted results, through CTTI's AACTClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299
- ClinicalTrials.govnone stated
- NIH RePORTER's clinical studies and project filesPublic domain (NIH RePORTER)
ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
For software
Read this trial from software.
The record and each of its lists are JSON, twenty rows a page. No key.
GET https://conductscience.org/api/trials/v1/NCT07895147
GET https://conductscience.org/api/trials/v1/NCT07895147/registered