Taichung Veterans General Hospital City: Taichung
Taichung, 407219, Taiwan
Location status: Recruiting
NCT Number: NCT07761377
This retrospective observational study evaluates the diagnostic performance of AccuPulmo CT Portal, an artificial intelligence-assisted medical imaging software, for detecting pulmonary fibrosis on pre-existing chest computed tomography images.
A total of 900 chest computed tomography examinations obtained at Taichung Veterans General Hospital between January 1, 2020, and December 31, 2024, will be retrospectively selected. The planned sample includes 300 examinations with pulmonary fibrosis and 600 examinations without pulmonary fibrosis.
All study images will be de-identified and coded before evaluation. Three qualified specialists in pulmonology or radiology will independently review each image without access to the original radiology report or the artificial intelligence output. The reference standard will be established by majority agreement of at least two of the three specialists.
AccuPulmo CT Portal will retrospectively analyze the coded images. An artificial intelligence-derived pulmonary fibrosis area greater than 10 percent will be classified as positive, and an area of 10 percent or less will be classified as negative. The primary performance measures are sensitivity and specificity. Secondary measures include accuracy, positive predictive value, negative predictive value, and performance across clinically relevant subgroups.
The software results will not be returned to treating physicians and will not affect participant diagnosis, treatment, or clinical management.
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Request Info20 year and older
All sexes
Observational
Taichung, 407219, Taiwan
Location status: Recruiting
This is a single-center, retrospective, non-interventional diagnostic performance study using pre-existing chest computed tomography images and associated clinical information from Taichung Veterans General Hospital.
Eligible participants are adults aged 20 years or older who underwent chest computed tomography for pulmonary disease between January 1, 2020, and December 31, 2024. The study plans to include 900 chest computed tomography examinations, comprising approximately 300 pulmonary fibrosis-positive examinations and 600 pulmonary fibrosis-negative examinations.
Potentially eligible examinations will initially be identified from existing institutional radiology records. Images with missing data or image characteristics that substantially interfere with lung texture assessment, including cardiac implants, extensive pneumonia, or pleural effusion, will be excluded according to the prespecified eligibility criteria.
Before specialist review, positive and negative samples will be combined, de-identified, assigned blinded study codes, and randomly allocated for review. Three qualified specialists holding board certification in pulmonology or radiology will independently evaluate all study images. The specialists will not have access to participant identifiers, the original radiology reports, or the AccuPulmo CT Portal results. Each specialist will estimate the proportion of pulmonary fibrosis and classify each examination as pulmonary fibrosis positive or negative. The reference standard will be determined by majority agreement, defined as concordant classification by at least two of the three specialists.
All coded images will subsequently be analyzed by AccuPulmo CT Portal. According to the prespecified classification rule, a pulmonary fibrosis area greater than 10 percent will be classified as positive, designated as Critical Risk, whereas a pulmonary fibrosis area of 10 percent or less will be classified as negative, designated as Low Risk.
After completion of the blinded assessments, the database will be unblinded for statistical analysis. The AccuPulmo CT Portal classifications will be compared with the specialist-derived reference standard. The primary endpoints are sensitivity and specificity. Secondary endpoints are accuracy, positive predictive value, negative predictive value, and performance consistency across relevant clinical subgroups.
The study uses only pre-existing images and records. AccuPulmo CT Portal will be operated in an offline research environment, and its outputs will not be returned to treating physicians or used for clinical decision-making. No additional imaging examination, clinical procedure, treatment assignment, or participant contact will occur as part of this study.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
AccuPulmo CT Portal is an artificial intelligence-assisted medical imaging software intended to analyze chest computed tomography images and identify imaging findings associated with pulmonary fibrosis. The software estimates the proportion of pulmonary fibrosis within the lung. In this study, a pulmonary fibrosis area greater than 10 percent is classified as positive, and a pulmonary fibrosis area of 10 percent or less is classified as negative.
The software will be applied retrospectively to de-identified pre-existing chest computed tomography images in an offline research environment. Its output will not be returned to treating physicians and will not affect participant diagnosis, treatment, or clinical management.
Time frame: Baseline
Sensitivity is defined as the proportion of pulmonary fibrosis-positive examinations according to the specialist-derived reference standard that are correctly classified as positive by AccuPulmo CT Portal. The reference standard is determined by majority agreement of at least two of three blinded specialists. A pulmonary fibrosis area greater than 10 percent generated by AccuPulmo CT Portal is classified as positive. Sensitivity will be reported with a 95 percent confidence interval and evaluated against the prespecified performance threshold of 0.80 using a one-sided binomial test.
Time frame: Baseline
Specificity is defined as the proportion of pulmonary fibrosis-negative examinations according to the specialist-derived reference standard that are correctly classified as negative by AccuPulmo CT Portal. The reference standard is determined by majority agreement of at least two of three blinded specialists. A pulmonary fibrosis area of 10 percent or less generated by AccuPulmo CT Portal is classified as negative. Specificity will be reported with a 95 percent confidence interval and evaluated against the prespecified performance threshold of 0.80 using a one-sided binomial test.
Time frame: Baseline
Diagnostic accuracy is defined as the proportion of all evaluated examinations that are correctly classified by AccuPulmo CT Portal as pulmonary fibrosis positive or pulmonary fibrosis negative compared with the specialist-derived reference standard. Accuracy will be reported with a 95 percent confidence interval and evaluated relative to the prespecified threshold of 0.80.
Time frame: Baseline
Positive predictive value is defined as the proportion of examinations classified as positive by AccuPulmo CT Portal that are pulmonary fibrosis positive according to the specialist-derived reference standard. Positive predictive value will be reported with a 95 percent confidence interval and evaluated relative to the prespecified threshold of 0.80.
Time frame: Baseline
Negative predictive value is defined as the proportion of examinations classified as negative by AccuPulmo CT Portal that are pulmonary fibrosis negative according to the specialist-derived reference standard. Negative predictive value will be reported with a 95 percent confidence interval and evaluated relative to the prespecified threshold of 0.80.
Time frame: Baseline
The sensitivity and specificity of AccuPulmo CT Portal will be descriptively evaluated across prespecified clinically relevant subgroups, subject to the availability of corresponding retrospective data. Subgroups may include age, sex, computed tomography acquisition characteristics, and other clinical categories defined in the statistical analysis plan. Estimates will be reported with 95 percent confidence intervals.
Contact information is provided by the study sponsor or research team.
Taichung Veterans General Hospital
Other
Evaluation of the Accuracy and Effectiveness of the AccuPulmo CT Portal AI-Assisted Interpretation System for the Diagnosis of Pulmonary Fibrosis
Acronym: ACCUPULMO-FIBR
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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