Deep Recon is a data driven image reconstruction method based on deep learning technology. It is intended to produce cross-sectional images by computer reconstruction of X-ray transmission data taken at different angles planes, including Axial, Helical, and Cardiac acquisition.

Deep Recon is designed to generate CT images with lower image noise, and improved low contrast detectability, and it can reduce the dose required for diagnostic CT imaging.

Deep Recon can be used for head, chest, abdomen, cardiac and vascular CT applications for adults. Deep Recon is intended to be used with uCT 760 and uCT 780 only.

Device Description

The Deep Recon is a data driven image reconstruction method based on deep learning technology. Dedicated deep neural networks are designed and trained for different body parts. As a part of reconstruction chain, the Deep Recon generates CT images with an appearance similar to traditional FBP, but with a decreased image noise, and an improved low contrast detectability. The Deep Recon was specifically trained on uCT 760 and uCT 780 (K172135). The function is integrated on the mentioned CT systems as a part of reconstruction chain.

AI/ML Overview

The initial document provides a 510(k) summary for the Deep Recon device, a data-driven image reconstruction method based on deep learning technology for CT systems. The device is intended to produce cross-sectional images with lower image noise, improved low contrast detectability, and the ability to reduce the required dose for diagnostic CT imaging.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria in a dedicated table. Instead, it describes performance goals as "equivalent or better performance" compared to Filtered Back Projection (FBP) for various image quality metrics, and "equivalent or better" diagnostic quality in clinical evaluations.

Feature/Metric	Acceptance Criteria (Implied)	Reported Device Performance (Deep Recon vs. FBP)
Low Contrast Detectability (LCD)	Equivalent or better than FBP	Improved compared to FBP
Image Noise	Equivalent or better than FBP	Decreased compared to FBP
Mean CT Number	Equivalent to FBP	Equivalent to FBP
Uniformity	Equivalent to FBP	Equivalent to FBP
Spatial Resolution	Equivalent to FBP	Equivalent to FBP
Reconstructed Section Thickness	Equivalent to FBP	Equivalent to FBP
Diagnostic Quality (Reader Study 1)	Equivalent or better than FBP	Equivalent or better than FBP in diagnostic quality
Diagnostic Quality (Reader Study 2)	Low-dose Deep Recon equivalent to standard-dose FBP	Low-dose images with Deep Recon are equivalent or better than standard-dose images with FBP in diagnostic quality

2. Sample Size and Data Provenance for Test Set

Clinical Image Evaluation (Reader Study 1):
- Sample Size: 80 retrospectively collected clinical cases.
- Data Provenance: Retrospective, country of origin not specified, but the device manufacturer is based in Shanghai, China.
Clinical Image Evaluation (Reader Study 2):
- Sample Size: 40 retrospectively collected clinical cases (20 low dose, 20 standard dose).
- Data Provenance: Retrospective, country of origin not specified, but the device manufacturer is based in Shanghai, China.

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)

Clinical Image Evaluation (Reader Study 1):
- Number of Experts: 2 board-certified radiologists.
- Qualifications: "board-certified radiologists." No further details on years of experience are provided.
Clinical Image Evaluation (Reader Study 2):
- Number of Experts: "a board-certified radiologist." This implies only one radiologist was used.
- Qualifications: "board-certified radiologist." No further details on years of experience are provided.

4. Adjudication Method for the Test Set

The document describes that for Reader Study 1, "Each image was read by 2 board-certified radiologists." It does not specify an adjudication method like 2+1 or 3+1 for discrepancies.
For Reader Study 2, "Each image was read by a board-certified radiologist," indicating no inter-reader adjudication was performed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No explicit MRMC comparative effectiveness study is mentioned that quantifies the "effect size of how much human readers improve with AI vs without AI assistance." The studies performed are comparative evaluations of the image quality and diagnostic usefulness of Deep Recon images versus FBP images, as assessed by human readers. They do not describe a scenario where AI assists human readers and measures the improvement.

6. Standalone Performance

No explicit standalone performance study (algorithm only without human-in-the-loop performance) is described in terms of diagnostic accuracy. The document focuses on the output of the algorithm (the reconstructed images) and how those images are perceived by human readers. The phantom studies (bench testing) could be considered standalone in the sense that they evaluate the algorithm's direct image quality metrics, but not diagnostic performance.

7. Type of Ground Truth Used

Clinical Image Evaluation Studies: The "ground truth" for the clinical evaluations (Reader Studies 1 & 2) was the expert consensus of the radiologists regarding image noise, structure fidelity, image quality, and clinical features based on a 4-point or 5-point scale. It does not refer to pathology, patient outcomes data, or an independent gold standard for diagnosis.
Bench Testing: The ground truth for bench testing (LCD, image noise, CT number, uniformity, spatial resolution, section thickness) involved standard phantom measurements and model observers, which represent established physical metrics rather than clinical ground truth derived from patients.

8. Sample Size for the Training Set

The document states that the Deep Recon's "Dedicated deep neural networks are designed and trained for different body parts." It also mentions that the "Deep Recon was specifically trained on uCT 760 and uCT 780 (K172135)."
However, the specific sample size (number of images or cases) used for the training set is not provided in this document.

9. How Ground Truth for the Training Set Was Established

The document mentions that the DNN is "trained on low dose FBP images to get normal dose (high quality) FBP images." This implies that the training likely used pairs of low-dose FBP images (as input to the network) and corresponding "normal dose (high quality) FBP images" (as the target or ground truth for the network to learn from).
The methodology for establishing the "normal dose (high quality) FBP images" as ground truth for training is not explicitly detailed. It's implicitly assumed that these are standard-of-care FBP reconstructions from standard-dose acquisitions.

Summary

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Shanghai United Imaging Healthcare Co., Ltd. % Shumei Wang QM & RA VP No. 2258 Chengbei Road Shanghai, Shanghai 201807 CHINA

Re: K193073

Trade/Device Name: Deep Recon Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK Dated: May 25, 2020 Received: May 27, 2020

Dear Shumei Wang:

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

July 6, 2020

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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

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Indications for Use

510(k) Number (if known) K193073

Device Name Deep Recon

Indications for Use (Describe)

Deep Recon is designed to generate CT images with lower image noise, and improved low contrast detectability, and it can reduce the dose required for diagnostic CT imaging.

Deep Recon can be used for head, chest, abdomen, cardiac and vascular CT applications for adults. Deep Recon is intended to be used with uCT 760 and uCT 780 only.

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

X Prescription Use (Part 21 CFR 801 Subpart D)

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

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510 (k) SUMMARY

K193073

1. Date of Preparation May 25, 2020

2. Sponsor Identification

Shanghai United Imaging Healthcare Co.,Ltd.

No.2258 Chengbei Rd. Jiading District, 201807, Shanghai, China

Contact Person: Shumei Wang Position: QM&RA VP Tel: +86-021-67076888-6776 Fax: +86-021-67076889 Email: shumei.wang(@united-imaging.com

3. Identification of Proposed Device

Trade Name: Deep Recon Common Name: Computed Tomography X-ray System Model(s): Deep Recon

Regulatory Information Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-ray System Regulatory Class: II Product Code: JAK Review Panel: Radiology

4. Identification of Predicate Device(s)

Primary Predicate Device:

510(k) Number: K172135 Device Name: uCT Computed Tomography X-Ray System Model(s): uCT 760, uCT 780

Regulatory Information Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-ray System Regulatory Class: II Product Code: JAK Review Panel: Radiology

Secondary Predicate Device:

510(k) Number: K183202 Device Name: Deep Learning Image Reconstruction

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Regulatory Information Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-Ray System Regulatory Class: II Product Code: JAK Review Panel: Radiology

5. Device Description:

6. Indications for Use

Deep Recon is designed to generate CT images with lower image noise, and improved low contrast detectability, and it can reduce the dose required for diagnostic CT imaging.

Deep Recon can be used for head, chest, abdomen, cardiac and vascular CT applications for adults.

Deep Recon is intended to be used with uCT 760 and uCT 780 only.

Specification/Attribute	Primary Predicate Device	Secondary Predicate Device	Proposed Device
	Filtered Back Projection(FBP) on uCT 760/780(K172135)	Deep Learning ImageReconstruction (K183202)	Deep Recon
Technology	Basic analyticreconstruction method	Utilizes a dedicated DeepNeural Network (DNN)which is trained on the CTScanner and designedspecifically to generatehigh quality CT images	Dedicated deep neuralnetwork (DNN) which istrained on low dose FBPimages to get normaldose (high quality) FBPimages
ClinicalWorkflow	Select recon type andconvolution kernel	Select recon type andstrength	Select recon type,convolution kernel andstrength (noise indexlevel)

7. Comparison of Technological Characteristics with the Predicate Devices

Deep Recon utilizes the same hardware with the primary predicate device and does not introduce any new restrictions on use.

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Image /page/5/Picture/1 description: The image contains the logo for United Imaging. The words "UNITED" and "IMAGING" are stacked on top of each other in a bold, sans-serif font. To the right of the text is a stylized "U" shape, which is divided vertically by a white line. The logo is simple and modern, with a focus on the company name.

The technological characteristics of Deep Recon is substantially equivalent to the secondary predicate device Deep Learning Image Reconstruction, the differences do not affect the safety and effectiveness.

8. Performance Data

Non-Clinical Testing

Non-clinical testing including image performance tests and clinical image evaluation were conducted for the Deep Recon 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)
A IEC 62304: Medical Device Software - software life cycle process
Guidance for the Content of Premarket Submissions for Software Contained in A 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 A devices
Code of Federal Regulations, Title 21, Part 820 Quality System Regulation A
Code of Federal Regulations, Title 21, Subchapter J Radiological Health 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

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Submissions for Management of Cybersecurity in Medical Devices" is included in this submission.

Performance Verification

Engineering bench testing was performed to support substantial equivalence and the product performance claims. The evaluation and analysis used the same raw datasets obtained on UIH's uCT 760/780 and then applies both Deep Recon and Filtered Back Projection reconstruction. The resultant images were then compared for:

Low contrast detectability (LCD) using the CCT189 MITA CT IQ low contrast phantom (The Phantom Laboratory, Salem, NY) and a model observer
Image noise using the CCT189 MITA CT IQ low contrast phantom
Mean CT number and uniformity using uniform water phantoms
Spatial resolution using the Catphan 700 phantom (The Phantom Laboratory, Salem, NY) with a small diameter tungsten wire inside to generate the point spread function
Reconstructed section thickness using the Catphan 700 phantom with a pair of tungsten ramps

Bench testing shows that the Deep Recon provides equivalent or better performance (improved LCD, decreased image noise, equivalent uniformity/spatial resolution/ reconstructed section thickness) compared to Filtered Back Projection.

Clinical Image Evaluation

The reader study used a total of 80 retrospectively collected clinical cases. The raw data from each of these cases was reconstructed with both Filtered Back Projection and Deep Recon. Each image was read by 2 board-certified radiologists who provided an assessment of both image noise and structure fidelity according to a 4point scale (1=unacceptable for diagnostic interpretation, 2=suboptimal, acceptable for limited diagnostic information only, 3=average, acceptable for diagnostic interpretation, 4=better than usual, acceptable for diagnostic interpretation). The results of the study indicate that Deep Recon is equivalent or better than Filtered Back Projection in diagnostic quality.

An additional study used a total of 40 retrospectively collected clinical cases (20 low dose cases and 20 standard dose cases). Each of the low dose cases was reconstructed with Deep Recon and compared with standard dose case reconstructed with Filtered Back Projection. Each image was read by a board-

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certified radiologist who provided an assessment of both image quality and clinical features according to a 5-point scale (1 = Unacceptable for diagnostic interpretation, 2 = Suboptimal, acceptable for limited diagnostic information only, 3 = Average, acceptable for diagnostic interpretation, 4 = Better than usual acceptable for diagnostic interpretation, 5 = Excellent for diagnostic interpretation). A comment about image quality and clinical features also left. The result of the study indicate that low dose images with Deep Recon are equivalent or better than standard dose images with Filtered Back Projection in diagnostic quality.

Clinical Testing

No Clinical Study is included in this submission.

9. Conclusions

The changes associated with Deep Recon do not change the indications for use from the primary predicate device, with no impact on control mechanism, operating principle, and energy type. Deep Recon also represents equivalent technological characteristic to the secondary predicate device.

Deep Recon was developed under UIH's quality management system. Design verification, along with bench testing and the clinical reader study demonstrate that Deep Recon is substantially equivalent and as safe and as effective as the legally marketed predicate device.

Based on the comparison and analysis above, the proposed device has similar performance, equivalent safety and effeteness as the predicate device. The differences above between the proposed device and predicate device do not affect the intended 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 device.

Regulation Number and Section

§ 892.1750 Computed tomography x-ray system.

(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.