ClariCT.AI
General Information
Product name
Company
Subspeciality
Modality
Disease targeted
Not specified
Main task
Not specified
Technical Specifications
Population
Patient population age
Not specified
Input
Input format
Output
Output format
Integration
Deployment
Trigger for analysis
Processing time
Regulatory Information
Pathway:
MDD
Class:
Class IIa
Pathway:
510(k) cleared
Class:
Class II
Other certifications
Not specified
Market Presence
On market since
AI Platforms
Resellers
Not specified
Countries present
Paying clinical customers
Research/test users
Pricing Information
Pricing model
Based on
Evidence & Research
Peer-Reviewed Papers
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The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging
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Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality
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Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction
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Effect of a novel denoising technique on image quality and diagnostic accuracy in low-dose CT in patients with suspected appendicitis
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Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques
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Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions
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Application of Vendor-Neutral Iterative Reconstruction Technique to Pediatric Abdominal Computed Tomography
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Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction
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Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging
Other Articles
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SPIE conference proceedings 2020: Combined low-dose simulation and deep learning for CT denoising: application of ultra-low-dose cardiac CTA
Source: vendor | First published: May 2, 2024 | Last updated: Jul 9, 2025