HYPER AiR is an image processing function intended to be used by radiologists and nuclear medicine physicians to reduce noise and improve contrast of fluorodeoxyglucose (FDG) PET images.

Device Description

HYPER AiR is a software-only device. HYPER AiR is an image reconstruction technique which incorporates pre-trained neural networks in the iteration reconstruction process to control image noise and contrast. It is intended to be implemented on previously cleared PET/CT devices uMI 550 (K193241) and uMI 780 (K172143). HYPER AiR serves as an alternative to the existing image reconstruction algorithm that are available on the predicate devices.

AI/ML Overview

The provided text describes the 510(k) summary for the HYPER AiR device, a software-only image processing function for FDG PET images. Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the performance tests and clinical image evaluation described. The device's performance is reported in terms of improvement over the conventional OSEM (Ordered Subset Expectation Maximization) algorithm.

Acceptance Criteria (Implicit)	Reported Device Performance
Non-Clinical (Bench Testing):
Performance on noise reduction improvement	HYPER AiR can improve image contrast while suppressing background noise.
Performance on image contrast improvement	HYPER AiR can improve image contrast while suppressing background noise.
Performance on contrast to noise ratio improvement	Performed, indicating improvement.
Clinical Image Evaluation:
Better image contrast compared to OSEM	HYPER AiR produces images with better image contrast than OSEM.
Lower image noise compared to OSEM	HYPER AiR produces images with lower image noise than OSEM.
Image quality sufficient for clinical diagnosis	The image quality was sufficient for clinical diagnosis.
Overall similar performance to predicate devices (for SE)	Based on comparison and analysis, the proposed device has similar performance, equivalent safety and effectiveness as the predicate devices. Differences do not affect indications for use, safety, and effectiveness.

2. Sample Size Used for the Test Set and Data Provenance

Test Set Sample Size: The exact number of cases or images in the test set for the clinical image evaluation is not specified. It only states "The clinical image evaluation was performed by comparing HYPER AiR with OSEM."
Data Provenance: The raw datasets used for evaluation were "obtained on UH's uMI 780 and uMI 550," which are devices from United Imaging Healthcare. The country of origin of this data is not explicitly stated, but given the sponsor's location (Shanghai, China), it can be inferred that the data likely originated from China. The data was retrospective as it involved applying two different reconstruction algorithms (HYPER AiR and OSEM) to the identical raw datasets already obtained.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

Number of Experts: "Each image was read by three board-certified nuclear medicine physicians."
Qualifications of Experts: "board-certified nuclear medicine physicians." No specific years of experience are mentioned.

4. Adjudication Method for the Test Set

The adjudication method is not explicitly stated. It says "Each image was read by three board-certified nuclear medicine physicians who provided an assessment of image contrast, image noise and image quality." It does not describe how discrepancies among the three readers were resolved or if a consensus mechanism was used.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size

MRMC Study: A comparative evaluation was done with human readers comparing HYPER AiR reconstructed images to OSEM reconstructed images. This is akin to an MRMC study if the multiple readers evaluated the same cases under both conditions.
Effect Size: The document states that "HYPER AiR produces images with better image contrast and lower image noise than OSEM while the image quality was sufficient for clinical diagnosis." However, a quantitative effect size (e.g., statistical significance of improvement, specific metrics like AUC difference, or reader confidence scores) is not provided in this summary. It's a qualitative statement of improvement. The study focuses on the standalone performance of the image processing rather than human readers improving with AI assistance vs without, although improved image quality implies potential for human improvement.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

Yes, standalone performance was evaluated through "Engineering bench testing" where the "evaluation and analysis used the identical raw datasets obtained on UH's uMI 780 and uMI 550, and then applies both HYPER AiR and OSEM to do image reconstruction. The resultant images were then compared for: Performance on noise reduction, Performance on image contrast, Performance on contrast to noise ratio." The aim was to show HYPER AiR's intrinsic ability to improve image characteristics compared to OSEM.

7. The Type of Ground Truth Used

The ground truth used for the evaluation was expert consensus/reader assessment by three board-certified nuclear medicine physicians for the clinical image evaluation. For the non-clinical bench testing, the "ground truth" was essentially the quantitative improvement in objective image metrics (noise reduction, contrast, CNR) based on the algorithm's output compared to OSEM. This is not a "true" clinical ground truth like pathology, but rather a technical performance measure.

8. The Sample Size for the Training Set

The document does not specify the sample size used for the training set of the neural networks integrated into HYPER AiR. It only mentions "pre-trained neural networks."

9. How the Ground Truth for the Training Set was Established

The document does not provide information on how the ground truth for the training set was established. It merely states that HYPER AiR "incorporates pre-trained neural networks in the iteration reconstruction process to control image noise and contrast."

Summary

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Shanghai United Imaging Healthcare Co., Ltd. % Xin GAO Regulatory Affairs Manager No. 2258 Chengbei Road Shanghai, Shanghai 201807 CHINA

Re: K210001

Trade/Device Name: HYPER AiR Regulation Number: 21 CFR 892.1200 Regulation Name: Emission computed tomography system Regulatory Class: Class II Product Code: KPS Dated: March 27, 2021 Received: March 30, 2021

Dear Xin GAO:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for

April 30, 2021

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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known) K210001

Device Name HYPER AiR

Indications for Use (Describe)

HYPER AiR is an image processing function intended to be used by radiologists and nuclear medicine physicians to reduce noise and improve contrast of fluorodeoxyglucose (FDG) PET images.

Type of Use (Select one or both, as applicable)

Prescription Use (Part 21 CFR 801 Subpart D)

| Over-The-Counter Use (21 CFR 801 Subpart C)

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510 (k) SUMMARY

1. Date of Preparation

March 26, 2021

2. Sponsor Identification

Shanghai United Imaging Healthcare Co.,Ltd. No.2258 Chengbei Rd. Jiading District, 201807, Shanghai, China

Contact Person: Xin GAO Position: Regulatory Affairs Manager Tel: +86-021-67076888-5386 Fax: +86-021-67076889 Email: xin.gao@united-imaging.com

3. Identification of Proposed Device

Trade Name: HYPER AiR Common Name: Emission Computed Tomography System Model(s): HYPER AiR

Regulatory Information Regulation Number: 21 CFR 892.1200 Regulation Name: Emission Computed Tomography System Regulatory Class: II Product Code: KPS Review Panel: Radiology

4. Identification of Predicate Device(s)

Predicate Device 510(k) Number: K193241 Device Name: Emission Computed Tomography System Model(s): uMI 550

Regulatory Information Regulation Number: 21 CFR 892.1200 Regulation Name: Emission Computed Tomography System Regulatory Class: II Product Code: KPS, JAK Review Panel: Radiology

5. Device Description:

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implemented on previously cleared PET/CT devices uMI 550 (K193241) and uMI 780 (K172143). HYPER AiR serves as an alternative to the existing image reconstruction algorithm that are available on the predicate devices.

6. Indications for Use

HYPER AiR is an image processing function intended to be used by radiologists and nuclear medicine physicians to reduce noise and improve contrast of fluorodeoxyglucose (FDG) PET images.

7. Comparison of Technological Characteristics with the Predicate Devices

A comparison between the technological characteristics of proposed and predicate devices is provided as below.

ITEM	Predicate DeviceuMI 550 ( K193241)	Proposed DeviceHYPER AIR	Remark
ImageProcessingLocation	Onsite on the facilityPET/CT reconstructioncomputer.	Onsite on the facilityPET/CTreconstructioncomputer.	Same
Operatingsystem	Windows	Windows	Same
Workflow	Support online & offline	Support online & offline	Same
Protocols	Standard scannerprotocols	Standard scannerprotocols	Same
Algorithm	OSEM included in uMI550 uses maximum-likelihood estimationtechniques bymaximizing theprobability to the givencounts represented by theimage corresponding tothe true activitydistribution in the source,under a Poissonprobability model for thepositron emission. Toaccelerate convergencespeed, it divides theprojection data into alimited number of subsetsand accesses them inorder for iterativecalculation.	HYPER AIR is amodification ofconventional OSEMby incorporating thepre-trainedconvolutional neuralnetworks into theiteration process.	OSEM included in uMI550 uses maximum-likelihood estimationtechniques. But HYPERAIR incorporatesconvolutional neuralnetworks into OSEM tohelp to reconstruct PETimages. Convolutionalneural network is able todistinguish the noisecomponent and the imagedetails, and removes thenoise component from theimage or enhances theimage details. So HYPERAIR can produce lowernoise and higher contrastPET images than OSEM.Performance EvaluationReport and Clinical Image

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		Evaluation show that it's safe and effective.
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HYPER AiR utilizes the same hardware with the predicate devices and does not introduce any new restrictions on use. The differences do not affect the safety and the effectiveness.

8. Performance Data

Non-Clinical Testing

Non-clinical testing including image performance tests and clinical image evaluation were concluded for the HYPER AiR during the product development. UNITED IMAGING HEALTHCARE claims conformance to the following standards and guidance:

Software

A NEMA PS 3.1-3.20(2011): Digital Imaging and Communications in Medicine (DICOM)
IEC 62304: Medical Device Software - software life cycle process
A Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices
A Content of Premarket Submissions for Management of Cybersecurity in Medical Devices

Other Standards and Guidance

ISO 14971: Medical Devices - Application of risk management to medical devices
Code of Federal Regulations, Title 21, Part 820 Quality System Regulation A

Software Verification and Validation

Software documentation for a Moderate Level of Concern software per FDA Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" is included as a part of this submission. The risk analysis was completed and risk control was implemented to mitigate identified hazards. The testing results show that all the software specifications have met the acceptance criteria. Verification and validation testing of the proposed device was found acceptable to support the claim of substantial equivalence. UNITED IMAGING HEALTHCARE conforms to the Cybersecurity requirements by implementing a process of preventing unauthorized access, modification, misuse or denial of use, or unauthorized use of information that is stored, accessed, or transferred from a medical device to an external recipient. Cybersecurity information in accordance with guidance document "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices" is included in this submission.

Performance Verification

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Engineering bench testing was performed to support substantial equivalence and the product performance claims. The evaluation and analysis used the identical raw datasets obtained on UH's uMI 780 and uMI 550, and then applies both HYPER AiR and OSEM to do image reconstruction. The resultant images were then compared for:

Performance on noise reduction
A Performance on image contrast
Performance on contrast to noise ratio

Performance tests have been conducted to show that HYPER AiR can improve image contrast while suppressing background noise.

Clinical Image Evaluation

The clinical image evaluation was performed by comparing HYPER AiR with OSEM. Each image was read by three board-certified nuclear medicine physicians who provided an assessment of image contrast, image noise and image quality. The results of the evaluation indicated that HYPER AiR produces images with better image contrast and lower image noise than OSEM while the image quality was sufficient for clinical diagnosis.

9. Conclusions

Based on the comparison and analysis above, the proposed device has similar performance, equivalent safety and effectiveness as the predicate devices. The differences between the proposed device and predicate devices do not affect the indications for use, safety and effectiveness. And no issues are raised regarding to safety and effectiveness. The proposed device is determined to be Substantially Equivalent (SE) to the predicate devices.

Regulation Number and Section

§ 892.1200 Emission computed tomography system.

(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.