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Evaluation for the performance of CAD software assisting pulmonary nodule detection on Chest CT images

Evaluation for the performance of CAD software (VUNO Med-LungCT AI) assisting pulmonary nodule detection on Chest CT images

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
JPRN
Registry ID
JPRN-jRCT1032240277
Enrollment
300
Registered
2024-08-19
Start date
2024-08-29
Completion date
Unknown
Last updated
2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Patients undergoing lung cancer screening and those needing further tests after initial screening

Interventions

None listed

Sponsors

Kenichi Sugihara
Lead Sponsor
VUNO Inc.
Collaborator

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1) CT images captured during the period from Jan 2020 to Sep 2024 at each Image Collection Medical Institution 2) CT images captured by the following vendor's CT devices GE, Philips, Siemens and Canon (Toshiba) 3) CT images of patients aged 18 years or older at the time of CT imaging 4) CT images with 512 x 512 or 1024 x 1024 pixel sizes 5) CT images with 0.6 to 5 mm slice thickness 6) CT images taken at low or standard doses 7) CT images in which the image reconstruction function (kernel) is a "Pulmonary Field Condition" 8) Non-contrast CT images

Exclusion criteria

Exclusion criteria: 1) CT images that is missing on imaging in part of both lungs 2) CT images judged by the Image Collection Physicians to have significant artifacts There are three main types of artifacts-motion artifact, metal artifact, and streak artifact etc.- and those that meet the following criteria are considered "significant artifacts". - Pulmonary nodules are not clearly depicted. - There is a shadow that interferes with interpretation. 3) CT images that the case is considered difficult to create GS due to diffuse shadows over a wide area of the lung field, infiltrative shadows, lesions over a wide area of the pleura, hemorrhage within the lung field, widespread pleural effusion, pneumothorax, innumerable pulmonary nodules, presence of surgical scars, etc. 4) CT images with inadequate DICOM header information 5) In case that IC from patients is required to obtain, CT images of patients who were unable to obtain informed consent or denied the use of images in this study when obtaining written or oral informed consent 6) Other cases that the Image Collection Physicians deems inappropriate

Design outcomes

Primary

MeasureTime frame
Figure of Merit (FOM) of JAFROC improvement in performance of the readers when interpreting images "With CAD (w/ CAD)" compared to performance of the readers when interpreting images "Without CAD (w/o CAD)" in detecting pulmonary nodules, in reader study

Contacts

Public ContactHirano Akina

M3, Inc.

akina-hirano@m3.com+81-70-1489-6687

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Jul 3, 2026