ClariCT.AI

ClariPi Inc.

ClariCT.AI is an AI-powered CT image denoising solution to provide increased image clarity in CT examinations with excessive image noise due to either low dose or large patient. It is trained to work in a vendor-agnostic way, to reduce noise and enhance image quality for low-dose and ultra-low-dose DICOM CT images. ClariCT.AI separates image noise selectively while enhancing underlying structures; thus, providing clarity restored images.

Information source: Vendor
Last updated: Oct. 3, 2023

General Information

General
Product name	ClariCT.AI
Company	ClariPi Inc.
Subspeciality	Neuro, Cardiac, MSK, Chest, Abdomen
Modality	CT
Disease targeted	N/A
Key-features	CT Image denoising for any body part
Suggested use	During

Technical Specifications

Data characteristics
Population	No restrictions
Input	CT, contrast or non-contrast
Input format	DICOM
Output	Enhanced series with ClariCT.AI tag
Output format	DICOM
Technology
Integration	Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application
Deployment	Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
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	10 - 60 seconds

Regulatory

Certification
CE	Certified, Class IIa , MDD
FDA	510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE)	ClariCT.AI software is intended for denoise processing and enhancement of DICOM images from a CT scanner when higher image quality and/or lower dose acquisitions are desired.

Market

Market presence
On market since	06-2019
Distribution channels	Siemens Syngo.Via, Nuance PIN, Blackford, Eureka Clinical AI, deepcOS, CARPL.ai
Countries present (clinical, non-research use)	44
Paying clinical customers (institutes)	45
Research/test users (institutes)	12
Pricing
Pricing model	Pay-per-use, Subscription, One-time license fee
Based on	Number of installations, Number of analyses

Evidence

Evidence
Peer reviewed papers on performance	The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging (read) Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality (read) Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction (read) Effect of a novel denoising technique on image quality and diagnostic accuracy in low-dose CT in patients with suspected appendicitis (read) Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques (read) Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions (read) Application of Vendor-Neutral Iterative Reconstruction Technique to Pediatric Abdominal Computed Tomography (read) Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction (read) Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging (read)
Non-peer reviewed papers on performance
Other relevant papers	SPIE conference proceedings 2020: Combined low-dose simulation and deep learning for CT denoising: application of ultra-low-dose cardiac CTA (read)