PixelShine

AlgoMedica
PixelShine is an algorithm for de-noising CT datasets supporting new CT protocols to drive patient dose down, while image quality is maintained. PixelShine may aid detectability of subtle pathologies which are borderline visible. The applications apply to CT without limit whether Filtered Back projection (FBP) and Iterative Reconstruction (IR).
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Information source: Vendor
Last updated: June 9, 2024

General Information

General
Product name PixelShine
Company AlgoMedica
Subspeciality Neuro, Cardiac, MSK, Chest, Abdomen, Vascular Analysis
Modality CT
Disease targeted
Key-features Noise reduction of low dose CT
Suggested use Before: stratifying reading process (non, single, double read)
During: perception aid (prompting all abnormalities/results/heatmaps)

Technical Specifications

Data characteristics
Population Pediatric CT, Neuro, Thoracic, MSK, Cardiac (Dx and Structural)
Input Low-dose CT
Input format DICOM
Output Enhanced CT
Output format DICOM
Technology
Integration Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone third party application
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Hybrid solution
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time Varies according to study size

Regulatory

Certification
CE
Certified, Class IIa , MDD
FDA 510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE)

Market

Market presence
On market since 05-2019
Distribution channels
Countries present (clinical, non-research use) 12
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Pay-per-use, Subscription, One-off payment
Based on Number of installations, Number of analyses, Number of licensed CT scanners

Evidence

Evidence
Peer reviewed papers on performance

  • Assessment of image quality and impact of deep learning‑based software in non‑contrast head CT scans (read)

  • Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen (read)
Non-peer reviewed papers on performance
Other relevant papers