uCT 780 is a computed tomography x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes and indicated for the whole body (including head, neck, cardiac and vascular).

uCT 780 is intended to be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.

uWS-CT-Dual Energy Analysis is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. These differences provide information on the chemical composition of the scanned body materials and enable images to be generated at multiple energies within the available spectrum. uWS-CT-Dual Energy Analysis software combines images acquired with low and high energy spectra to visualize this information.

Device Description

The Computed Tomography X-ray System, uCT 780, is intended to produce cross-sectional images of the patient by computer reconstruction of X-ray transmission data taken at different angles and planes. These images may be obtained either with or without contrast.

The uWS-CT-Dual Energy Analysis is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. These differences provide information on the chemical composition of the scanned body materials. CT dual energy analysis application combines images acquired with low and high energy spectra to visualize this information. In this submission, the DETLA (Deep Recon) feature of the proposed device is modified and there is no other significant change compared with K230162.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Device: DELTA (Deep Recon) algorithm for uCT 780

Acceptance Criteria	Reported Device Performance
Bench Test (Phantom Testing)
DELTA passes basic IQ test per IEC 61223-3-5.	DELTA passed the basic general IQ test, satisfying IEC 61223-3-5.
Noise power spectrum of DELTA similar to FBP.	Noise power spectrum of DELTA is similar to FBP.
Mean CT value of DELTA under various tissues similar to FBP (error < 4HU head, < 6HU body).	Mean CT value error for different tissues of DELTA is less than 4HU in head and 6HU in body compared to FBP.
Compared with FBP, DELTA has better LCD and noise under same scanning dose.	DELTA showed better LCD and noise compared with FBP at the same scanning dose.
DELTA can reduce scanning dose, keeping similar LCD compared with FBP.	DELTA can reduce scanning dose compared with FBP at the same LCD.
Compared with FBP, DELTA has better high contrast spatial resolution.	DELTA showed better spatial resolution compared with FBP at the same scanning dose.
Clinical Assessment
Mean score of simulated low dose DELTA image is equal to or higher than the mean score of normal dose FBP.	Validation dataset evaluation showed that the DELTA method yielded equivalent or better image quality at lower dose compared with FBP on them with standard dose.
Mean score of DELTA is better than the mean score of FBP under the same dose.	Test dataset evaluation showed that DELTA reconstruction method yielded better image quality compared with FBP reconstruction.
DELTA can have satisfied diagnosis requirement in all evaluated images.	All the study results show that the acceptance criteria were met. (Implies that images were sufficient for diagnosis, though a specific result wasn't explicitly stated in the findings section)

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

Sample Size for Test Set:
- Validation Dataset: 20 cases (5 for each body part: abdomen, chest, head, cardiac).
- Test Dataset: 40 cases (10 for each body part: abdomen, chest, head, cardiac).
Data Provenance: Not explicitly stated (e.g., country of origin). The data is from "sample clinical data" and "different head, chest, abdomen and cardiac protocol, common diseases, different scanning dose and a wide range of population." It's described as "clinical datasets," implying real patient data. It is not explicitly stated whether it is retrospective or prospective, but the phrasing "sample clinical data" suggests it could be retrospective.

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

Number of Experts: 2
Qualifications: Board-certified U.S. radiologists.

4. Adjudication Method for the Test Set

The text states that the image quality was scored by the 2 radiologists, implying individual assessments rather than a formal adjudication method like 2+1 or 3+1. There is no mention of a process to resolve disagreements between the two radiologists' scores.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its effect size

While a reader study was performed with multiple readers (2 radiologists) and multiple cases, it did not explicitly state it as an MRMC comparative effectiveness study in the typical sense of measuring improvement in human performance with AI vs. without AI assistance.

Instead, the study evaluated the image quality of images reconstructed by DELTA (AI-based) compared to FBP (standard) directly. The radiologists scored the image quality of the images themselves, not their diagnostic performance with or without the DELTA reconstructions.

Therefore, no effect size of how much human readers improve with AI vs. without AI assistance is provided.

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

Yes, a significant part of the evaluation was a standalone algorithm performance assessment through Bench Testing - Phantom Test. This involved objective measurements of image quality metrics (IQ, noise power spectrum, mean CT value, LCD, spatial resolution) using various phantoms, without human interpretation involved.

The "Clinical Assessment" also indirectly assesses the algorithm's standalone performance by having radiologists score the output images of the algorithm where the algorithm is the primary variable (DELTA vs. FBP).

7. The Type of Ground Truth Used

Bench Test (Phantom Testing): The ground truth for the bench tests would be the known physical properties and measurements from the phantoms themselves (e.g., precise HU values in known materials, known low-contrast objects, quantifiable spatial resolution targets).
Clinical Assessment (Reader Study): The ground truth for the clinical assessment was based on expert consensus (or at least expert scoring) from the two board-certified U.S. radiologists. They performed a five-point scale evaluation of image quality aspects (noise, structure fidelity, and overall image quality), which serves as the "ground truth" for the perceived image quality. There is no mention of pathology or outcomes data being used as ground truth for the clinical evaluation of image quality.

8. The Sample Size for the Training Set

The sample size for the training set is not explicitly given. The document mentions an increased dataset size for the DELTA algorithm: "The new dataset was increased to 127 cases compared with the original dataset." This "new dataset" would likely include training data, but it's not specified how many of these 127 cases were specifically used for training. It also states, "The performance testing for DELTA (Deep Recon) algorithm was performed on 60 subjects during the product development." The phrase "during the product development" might imply the training phase, but it's ambiguous.

However, it explicitly states: "Training dataset do not contain any validation dataset or test dataset of DELTA algorithm."

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

The document does not explicitly describe how the ground truth for the training set (e.g., the "new dataset increased to 127 cases") was established. For deep learning reconstruction algorithms like DELTA, the "ground truth" for training often involves:

Reference standard images: High-quality, higher-dose CT scans (or simulated ideal images) that the low-dose or noisy input images are trained to match or approximate.
Image quality metrics: Objective computational metrics might be used to label or guide the training process to optimize for reduced noise, improved spatial resolution, etc.

Without further information, the exact method for establishing ground truth for the training data remains unspecified.

Summary

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January 7, 2025

Shanghai United Imaging Healthcare Co., Ltd. % Xin Gao Regulatory Affairs Manager No. 2258 Chengbei Rd. Jiading District Shanghai, 201807 CHINA

Re: K241079

Trade/Device Name: uCT 780 Regulation Number: 21 CFR 892.1750 Regulation Name: Computed Tomography X-Ray System Regulatory Class: Class II Product Code: JAK Dated: April 30, 2024 Received: December 6, 2024

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 (the 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 available 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.

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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30. Design controls; 21 CFR 820.90. Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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-regulatory

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assistance/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,

Lu Jiang

Lu Jiang, PhD Assistant Director Diagnsotic X-ray Systems Team DHT8B: Division of Radiologic Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

K241079

Device Name

uCT 780

Indications for Use (Describe)

Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

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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Image /page/4/Picture/1 description: The image contains the logo for United Imaging. The logo consists of the words "UNITED IMAGING" in a bold, sans-serif font, stacked on top of each other. To the right of the text is a stylized letter "U" that is split into two halves with a horizontal line in the middle. The logo is a dark gray color and is set against a white background.

510 (K) SUMMARY

K241079

Date of Preparation 1.

January 7, 2025

2. Sponsor Identification

Shanghai United Imaging Healthcare Co.,Ltd.

No.2258 Chengbei Rd. Jiading District, 201807, Shanghai, China Contact Person: Xin GAO Position: Regulatory Affair Manager Tel: +86-021-67076888-5386 Fax: +86-021-67076889 Email: xin.gao@united-imaging.com

3. Identification of Proposed Device

Trade Name: uCT 780 Model(s): uCT 780, uWS-CT-Dual Energy Analysis Regulation Name: Computed Tomography X-ray System Regulation Number: 21 CFR 892.1750 Regulatory Class: II Product Code: JAK Review Panel: Radiology

Identification of Predicate Device(s) 4.

Predicate Device 510(k) Number: K230162 Trade Name: uCT 760 with uWS-CT-Dual Energy Analysis, uCT 780 with uWS-CT-Dual Energy Analysis Model(s): uCT 760, uCT 780 Regulation Name: Computed Tomography X-ray System Regulation Number: 21 CFR 892.1750 Regulatory Class: II Product Code: JAK Review Panel: Radiology

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Device Description: 5.

6. Indications for Use

Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

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7. Substantially Equivalence Discussion

The DELTA (Deep Recon) is an AI-based reconstruction method that deployed within uCT 780 acquisition and processing software. The hardware environment and software platform for the DELTA in the proposed device and the predicate device are same. The indications for use and the scientific technology remain unchanged. The table below summarizes the Technological Characteristics between the proposed and predicate devices.

	Proposed deviceuCT 780 with uWS-CT-DualEnergy Analysis	Predict deviceuCT 760/780 with uWS-CT-Dual Energy Analysis(K230162)	Discussion
DELTA(DeepRecon)	AI-based deep learningreconstruction algorithm.	AI-based deep learningreconstruction algorithm.	Same
ApplicableBody Parts	head, chest, abdomen, cardiac andvascular CT applications for adults	head, chest, abdomen,cardiac and vascular CTapplications for adults	Same
UserInterface	5 noise reduction levels.	4 noise reduction levels.	The user interfaceincreased the choice for thenoise reduction level. Thiswill not introduce safety oreffectiveness issues.
Dataset Size	The new dataset was increased to127 cases compared with theoriginal dataset.	Original Dataset	Dataset was increased .toimprove robustness of thealgorithm. The differencewill not introduce safety oreffectiveness issues.
Clinicalworkflow	Select recon type and strength(noise index level).	Select recon type andstrength (noise index level).	Same

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Image /page/7/Picture/1 description: The image shows the logo for United Imaging. The words "UNITED IMAGING" are stacked on top of each other and are in a bold, sans-serif font. To the right of the words is a stylized "U" shape that is also in a bold font. The logo is simple and modern, and the colors are muted.

Phantom test	Phantom test for dosereduction, LCD, noise andspatial resolution improvingtest.Basic IQ performance testincluding HU value on waterphantom, thickness sectiontest.Additional HU value test ontissue-mimicking phantom,noise spectrum power test.	Phantom test for dosereduction, LCD, noiseand spatial resolutionimproving test.Basic IQ performancetest including HU valueon water phantom,thickness section test.	Adding more phantomtests, including HU valuetest on tissue-mimickingphantom and noisespectrum power test to givea more comprehensive testsresults. The difference willnot introduce safety andeffectiveness issues.
Conclusion	DELTA is able to generate CTimages at lower dose in the lowdose simulation validation andgenerate equivalent or betterimage quality compared with FBPin the clinical scenarios of realscan under a variety of dose levels.	DELTA is able to generateCT images with lower imagenoise and improve lowcontrast detectability andalso to reduce the doserequired for diagnostic CTimaging.	Substantially Equivalent.The function of DELTA isoptimized in the proposeddevice, which improved thealgorithm framework. Thedifference will notintroduce safety andeffectiveness issues.

8. Performance Testing Information

The performance testing for DELTA (Deep Recon) algorithm was performed on the Bench Testing and the Clinical Evaluation.

8.1 Bench Test - Phantom Test

The bench testing is conducted on a variety of phantoms to quantify the improvement of images with DELTA when compared to the Standard Reconstruction Protocol using FBP. Some basic image quality tests were also performed on DELTA to confirm that it meets the diagnostic requirements. The image quality (IQ) of the proposed device was tested through the below evaluation methods:

8.1.1 Phantom Testing

. General IQ Performance testing of metrics identified in IEC 61223-3-5 to study overall performance in a standardized and referenceable manner. General IQ tests include CT HU number test and thickness section test based on water phantom and Catphan 700 phantom.
. Noise power spectrum test was applied on water phantom for more comprehensive test on DELTA.
. Mean CT value test on CT Whole Body Phantom PBU-60 phantom was applied to comparing mean HU value of different clinical relevant tissue between FBP and DELTA.
. A low contrast detectability (LCD) test was performed to evaluate the LCD enhancement, dose reduction and noise reduction on MITA CCT189 and MITA CCT191 phantom.
. A high contrast spatial resolution test was performed under the guidance of IEC 61223-3-5 to evaluate the high contrast spatial resolution enhanced comparing with FBP.

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8.1.2 Study pass criteria

DELTA should pass the basic IQ test according to IEC 61223-3-5. .
Noise power spectrum of DELTA should be similar to FBP image. ●
Mean CT value of DELTA under various tissues should be similar to that of FBP ● and the error should be less than 4HU in head and 6HU in body.
. Compared with FBP, DELTA should have better LCD and noise under same scanning dose, and DELTA can reduce scanning dose keep similar LCD comparing with FBP.
. Compared with FBP, DELTA should have better high contrast spatial resolution.

8.1.3 Bench Testing Results and conclusion

DELTA passed the basic general IQ test which satisfied the requirement of IEC ● 61223-3-5.
Noise power spectrum of DELTA is similar to FBP. ●
Mean CT value for different tissues of DELTA is less than 4HU in head and 6HU in body compared with that of FBP
. DELTA showed better LCD and noise compared with FBP at same scanning dose and can reduce scanning dose compared with FBP at same LCD.
DELTA showed better spatial resolution compared with FBP at same scanning dose.

In summary, the bench testing result shows that DELTA had better image noise, LCD and spatial resolution at same scanning dose compared with FBP, and DELTA can keep the image quality (LCD) under lower scanning dose compared with FBP.

8.2 Clinical Assessment

8.2.1 Clinical Evaluation Method

The proposed device performed a reader study of sample clinical data as clinical evaluation. The image quality was scored by 2 board-certified U.S. radiologists to perform a five-point scale evaluation of both image sets on three image quality aspects: noise, structure fidelity and overall image quality.

8.2.2 Testing Data Information

The clinical datasets in the evaluation covered different head, chest, abdomen and cardiac protocol, common diseases, different scanning dose and a wide range of population. The datasets were reconstructed with DELTA and FBP.

The performance testing for DELTA algorithm was performed on 60 subjects during the product development.

The validation dataset includes 20 cases, with 5 cases for each body part-abdomen, chest, head, and cardiac-scanned at a normal dose and reconstructed using normaldose FBP, as well as simulated low-dose DELTA images.

The test dataset includes 40 cases, with 10 cases for each body part-abdomen, chest, head, and cardiac-scanned at various doses and reconstructed using FBP and DELTA.

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Validation data & Testing Data Independence

Training dataset do not contain any validation dataset or test dataset of DELTA algorithm.

Body Part	Number	Age	Sex	Dose Distribution
Head	15	27-82	6 male9 female	≥70%: 1030%-70%: 3<30%: 2
Chest	15	46-81	9 male6 female	≥70%: 330%-70%: 6<30%: 6
Abdomen	15	30-87	10 male5 female	≥70%: 530%-70%: 5<30%: 5
Cardiac	15	29-75	7 male8 female	≥70%: 730%-70%: 8<30%: 0

	The distribution of validation and test data was shown below:

8.2.3 Clinical Acceptance Criteria

The mean score of simulated low dose DELTA image is equal or higher than the mean score of normal dose FBP.
The mean score of DELTA is better than the mean score of FBP under same dose.
DELTA can have satisfy diagnosis requirement in all the evaluated image.

8.2.4 Clinical Evaluation Results and Conclusion

Validation dataset evaluation showed that the DELTA method yielded equivalent or better image quality at lower dose compared with the FBP on them with standard dose.
. Test dataset evaluation showed that DELTA reconstruction method yielded better image quality compared with FBP reconstruction.
All the study results show that the acceptance criteria were met.

This clinical study alone can not be used to support the promotional claim such as uCT 780 DELTA achieves equivalent or better image quality than FBP, additional tests are needed.

In summary, due to the testing limited, a consultation with a radiologist and a physicist should be made to determine the appropriate dose to obtain diagnostic image quality for the particular clinical task before using uCT 780 DELTA (Deep Recon).

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uCT 780 DELTA has not been thoroughly tested in pediatric population. A consultation with a radiologist and a physicist should be made to determine the appropriate dose to obtain diagnostic image quality for the particular clinical task; Clinicians or dosimetrists should only be allowed to reduce patient dose in clinical practice.

In clinical practice, the use of uCT 780 DELTA may reduce CT patient dose depending on the clinical task, patient size, anatomical location, and clinical practice. A consultation with a radiologist and a physicist should be made to determine the appropriate dose to obtain diagnostic image quality for the particular clinical task. Low Contrast Detectability (LCD), Image Noise were assessed using normal dose comparing uCT 780 DELTA and FBP. The LCD and Image Noise measured in 0.625mm slices. The claims are based on level 5.

9. Conclusion and Summary

The changes associated with DELTA (Deep Recon) in uCT 780 do not create a new Intended Use and represent technological characteristics that the dose is substantially reduced at the same resolution, the low contrast resolution is substantially increased at the same dose and the noise is substantially reduced.

United Imaging's quality system's design verification, and risk management processes did not identify any new questions of safety or effectiveness, hazards, unexpected results, or adverse effects stemming from the changes to the predicate.

Based on development under United Imaging's quality system, the successful system and software verification and validation testing, the additional engineering bench testing, and the clinical testing demonstrates that uCT 780 is substantially equivalent to and as safe and as effective for its Intended Use, as the legally marketed 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.