Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
168 participants
OBSERVATIONAL
2026-01-31
2026-09-01
Brief Summary
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The study has two objectives:
1. To evaluate the image quality and diagnostic confidence of Precise Image compared to the standard-of-care reference (iDose4) for:
* Head and body CT images of pediatric patients (from 18 months up to and including 18 years of age).
2. To demonstrate non-inferiority of Precise Image to iDose4 for the relevant anatomy and resolution groups.
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Detailed Description
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Precise Image is a reconstruction mode using a trained deep learning neural network to generate images with reduced noise and improved low contrast detectability at lower doses compared with standard FBP (filter back projection) reconstruction.
Pediatric subjects (from 18 months up to and including 18 years of age) scanned by the FDA-cleared Philips Incisive CT (K212441) or CT 5300 (K232491) systems.
The study will include reconstructed images of 168 CT scans in total. For each of the anatomies and resolutions, 42 scans will be used.
Conditions
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Study Design
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OTHER
RETROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
* Subject age: From 18 months up to and including 18 years of age.
Exclusion Criteria
* Scans that were acquired using generic 'Head' and 'Body' scan types (as Precise Image does not support generic 'Head' and 'Body' scan types).
* Scans that were acquired using a kilovoltage peak of 70 kVp (as Precise Image does not support a kilovoltage peak lower than 80 kVp).
* Scans that the site radiologists have deemed as non-diagnostic image quality in standard practice (e.g. patient movement).
* Scans that are not completed due to technical difficulties.
18 Months
18 Years
ALL
No
Sponsors
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Philips Healthcare (Suzhou) Co., Ltd.
INDUSTRY
Philips Clinical & Medical Affairs Global
INDUSTRY
Responsible Party
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Other Identifiers
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Precise Image for Pediatrics
Identifier Type: -
Identifier Source: org_study_id
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