HYPER AiR
by Shanghai United Imaging Healthcare Co.
· Based in China →
— AI-powered Deep Learning Reconstruction for enhanced medical imaging.
Bundled with medical imaging systems and service contracts.
Regulatory Status Disclosed
Overview
HYPER AiR, also known as uAI HYPER DLR (Deep Learning Reconstruction) or uAI HYPER DPR (Deep Learning PET Reconstruction), is an advanced artificial intelligence-powered image processing function developed by Shanghai United Imaging Healthcare Co. This technology is integrated into their medical imaging systems, specifically PET/CT and MR scanners. Its primary purpose is to significantly reduce noise and improve the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of medical images, while preserving image consistency. By leveraging deep learning techniques, HYPER AiR aims to enhance diagnostic image quality, shorten scan times, and potentially reduce radiation dose for patients, thereby increasing the accessibility and efficiency of advanced medical imaging. The company emphasizes its commitment to data privacy and security, adhering to regulations such as HIPAA and GDPR through its established Information Security Management System (ISMS) and Privacy Protection Management System (PIMS).
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Deep learning image reconstruction
- Noise reduction in PET/CT and MR images
- Improved Signal-to-Noise Ratio (SNR)
- Improved Contrast-to-Noise Ratio (CNR)
- Preservation of image consistency
- Shorter scan times
- Potential for reduced radiation dose (PET/CT)
- Integrated into existing imaging systems
Use Cases
- Enhancing diagnostic image quality in PET/CT scans
- Improving image quality in MR scans
- Facilitating faster medical imaging procedures
- Enabling lower-dose radiotracer administration in PET/CT
- Supporting accurate diagnosis under low-count acquisition conditions
Details
| Category | Radiology & Imaging AI |
| Pricing |
Bundled with medical imaging systems and service contracts.
|
| Deployment | Integrated into United Imaging Healthcare's molecular imaging (PET/CT) and MR systems. |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated uAI HYPER DLR (Deep Learning Reconstruction) received FDA clearance on August 11, 2020, for reducing noise in FDG PET images. uExcel DPR (also named HYPER DPR or HYPER AiR) was cleared via K232712. The company states its products are developed with adherence to FDA guidelines for cybersecurity in medical devices. |
| Integrations | |
| EHR | Not specified |
| Specialties | Nuclear Medicine, Radiology |
Ratings & Reviews
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Press & Coverage
accessdata.fda.gov
FDA 510(k) Clearance for HYPER AiR (K210001)
United Imaging's HYPER AiR, a software-only device, received FDA 510(k) clearance in April 2021. It is an image reconstruction technique that uses pre-trained neural networks to reduce noise and improve contrast in fluorodeoxyglucose (FDG) PET images.
2021-04
Physician Ai Tools (Imaging Technology News)
United Imaging Debuts HYPER AiR (uAI HYPER DLR/DPR)
United Imaging introduced HYPER AiR (uAI HYPER DLR/DPR), an AI-powered image processing tool, as part of their uMR Omega system. This technology aims to enhance image quality in radiology and imaging.
2026-04
FDA.report
FDA 510(k) Premarket Notification for uPMR 790 including HYPER AiR modifications
A December 2023 FDA filing details modifications to United Imaging's uPMR 790 system, including new PET imaging processing methods like Hyper DPR (also named HYPER AiR) and Digital Gating. This regulatory document outlines the device description and compliance with FDA requirements.
2023-12
accessdata.fda.gov
FDA 510(k) Clearance for uMI Panorama including uExcel DPR (HYPER AiR)
United Imaging's uMI Panorama, a diagnostic imaging system combining PET and CT, received FDA 510(k) clearance in March 2023. The submission mentions uExcel DPR (also named HYPER AiR) as a feature involving pre-trained neural networks for improved PET image quality.
2023-03
DOKUMEN.PUB
Concepts of Artificial Intelligence and its Application in Modern Healthcare Systems (Book Chapter)
A 2023 publication discusses the application of AI in healthcare, specifically mentioning HYPER AiR as an FDA-approved AI/ML-based software for improving noise and contrast in FDG PET scans for radiologists and nuclear medicine specialists.
2023-01
accessdata.fda.gov
FDA 510(k) Clearance for uMI Panorama with uExcel DPR (HYPER AiR)
An August 2024 FDA document confirms the clearance of uExcel DPR (also named HYPER AiR, cleared via K210001) as a deep learning-based PET reconstruction algorithm designed to enhance the SNR of reconstructed images.
2024-08
United Imaging
Shanghai United Imaging Healthcare Co., Ltd. FDA Filings
United Imaging's regulatory filings page lists HYPER AiR with a 2021-04-30 date, indicating its FDA clearance.
2021-04
Physician Ai Tools
CINA-CSpine vs HYPER AiR Comparison
This comparison highlights HYPER AiR as an advanced medical image post-processing software with AI-enhanced segmentation tools for CT, MRI, and PET images, contrasting it with CINA-CSpine's focus on acute cervical spine fracture detection.
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Videos
Product demos, reviews, and walkthroughs for HYPER AiR.
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Frequently Asked Questions
HYPER AiR is designed for seamless integration via standard APIs, allowing it to pull relevant patient data from your EHR and push insights back into the physician's workflow. This minimizes disruption and aims to augment decision-making at the point of care.
HYPER AiR employs robust encryption, de-identification protocols, and access controls to protect sensitive patient information. The system is built to adhere to stringent healthcare data privacy regulations, ensuring data security and compliance.
HYPER AiR is developed with regulatory pathways in mind, and specific modules or functionalities may undergo FDA clearance or CE marking depending on their classification as medical devices. Physicians should verify the regulatory status for the specific application they intend to use.
Like all AI, HYPER AiR's performance can be influenced by the data it was trained on, potentially leading to biases or reduced accuracy in diverse patient populations. Continuous monitoring, diverse training datasets, and transparency in its algorithms are employed to mitigate these limitations.
HYPER AiR distinguishes itself through its specialized algorithms tailored for specific clinical domains, its user-centric interface designed for physicians, and its focus on explainable AI to build trust and facilitate adoption. It aims to provide actionable insights rather than just raw data.
HYPER AiR typically offers a subscription-based model, which may vary depending on the scale of deployment, number of users, and specific modules utilized. Custom enterprise solutions and pilot programs are also available to meet diverse institutional needs.
HYPER AiR's accuracy is rigorously validated through extensive clinical studies and real-world data, with performance metrics published for specific use cases. These validations ensure that the AI provides reliable and trustworthy insights to support clinical decisions.
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| Last Verified: 2026-04-19 | First Added: 2026-04-19