The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, (Volumetric), and Cardiac acquisitions, for all ages.

Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.

Device Description

Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression.

The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction support a normal throughput for routine CT.

The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity™ CT Images".

The system allows user selection of three strengths of Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preference for the specific clinical need.

The DLR algorithm was modified on the Revolution CT/Apex platform for improved reconstruction speed and image quality and cleared in February 2022 with K213999. The same modified DLIR is now being ported to Revolution EVO (K131576) /Revolution Maxima (K192686), Revolution Ascend (K203169, K213938) and Discovery CT750 HD family CT systems including Discovery CT750 HD, Revolution Frontier and Revolution Discovery CT (K120833).

AI/ML Overview

The provided text describes that the Deep Learning Image Reconstruction software was tested for substantial equivalence to a predicate device (K213999). The study performed was largely an engineering bench testing, comparing various image quality metrics between images reconstructed with Deep Learning Image Reconstruction (DLIR) and ASiR-V from the same raw datasets.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The text indicates that the device aims to maintain the performance of ASiR-V in specific areas while offering an image appearance similar to traditional FBP images. The "acceptance criteria" can be inferred from the list of image quality metrics evaluated, with the performance goal being comparable or improved relative to ASiR-V.

Acceptance Criteria (Implied Goal: Performance comparable to or better than ASiR-V)	Reported Device Performance (Implied: Met acceptance criteria, no adverse findings)
Image noise (pixel standard deviation)	DLIR maintains ASiR-V performance.
Low contrast detectability (LCD)	Evaluation performed. (Implied: Met acceptance criteria)
High-contrast spatial resolution (MTF)	Evaluation performed. (Implied: Met acceptance criteria)
Streak artifact suppression	DLIR maintains ASiR-V performance.
Spatial Resolution, longitudinal (FWHM slice sensitivity profile)	Evaluation performed. (Implied: Met acceptance criteria)
Noise Power Spectrum (NPS) and Standard Deviation of noise	Evaluation performed (NPS plots similar to FBP). (Implied: Met acceptance criteria)
CT Number Uniformity	Evaluation performed. (Implied: Met acceptance criteria)
CT Number Accuracy	Evaluation performed. (Implied: Met acceptance criteria)
Contrast to Noise (CNR) ratio	Evaluation performed. (Implied: Met acceptance criteria)
Artifact analysis (metal objects, unintended motion, truncation)	Evaluation performed. (Implied: Met acceptance criteria)
Pediatric Phantom IQ Performance Evaluation	Evaluation performed. (Implied: Met acceptance criteria)
Low Dose Lung Cancer Screening Protocol IQ Performance Evaluation	Evaluation performed. (Implied: Met acceptance criteria)
Image appearance (NPS plots similar to traditional FBP)	Designed to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images.
No additional risks/hazards, warnings, or limitations	No additional hazards were identified, and no unexpected test results were observed.
Maintains normal throughput for routine CT	Reconstruction times with Deep Learning Image Reconstruction support a normal throughput for routine CT.

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

Test Set Sample Size: The text states "the identical raw datasets obtained on GEHC's Revolution Ascend, Revolution Frontier and Discovery CT750 HD systems". However, the number of cases or specific sample size for these datasets is not explicitly stated.
Data Provenance: The raw datasets were "obtained on GEHC's Revolution Ascend, Revolution Frontier and Discovery CT750 HD systems". The country of origin is not specified, and it is stated that the study used retrospective raw datasets (i.e., existing data, not newly acquired data for the study).

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

The provided text focuses on engineering bench testing and image quality metrics. It does not mention the use of experts to establish ground truth for the test set or their qualifications. The evaluation primarily relies on quantitative image quality metrics.

4. Adjudication Method for the Test Set

Since experts were not explicitly used to establish ground truth, there is no mention of an adjudication method for the test set in the provided text.

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

An MRMC comparative effectiveness study was not performed according to the provided text. The study focused on technical image quality comparisons at the algorithm level, not human reader performance with or without AI assistance.

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

Yes, a standalone performance evaluation was done. The study described is primarily a standalone evaluation of the algorithm's image quality output (e.g., noise, resolution, artifacts, detectability) when compared to images reconstructed with ASiR-V from the same raw data.

7. The Type of Ground Truth Used

The "ground truth" for the test set was essentially:

Quantitative Image Quality Metrics: Performance relative to ASiR-V for metrics like image noise, LCD, spatial resolution, streak artifact suppression, CT uniformity, CT number accuracy, CNR, spatial resolution (longitudinal), NPS, and artifact analysis.
Reference Image Appearance: The stated goal was an image appearance similar to traditional FBP images shown on axial NPS plots.

There is no mention of pathology, expert consensus on clinical findings, or outcomes data being used as ground truth for this particular substantial equivalence study.

8. The Sample Size for the Training Set

The text states that the Deep Neural Network (DNN) is "trained on the CT scanner" and models the scanned object using "information obtained from extensive phantom and clinical data." However, the specific sample size for the training set is not provided.

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

The ground truth for the training set is implicitly established through the "extensive phantom and clinical data" mentioned as being used to train the DNN. The text indicates the DNN is trained to model noise propagation and identify noise characteristics to remove it, and to generate images with an appearance similar to traditional FBP while maintaining ASiR-V performance. This suggests the training involves learning from "ground truth" as defined by:

Reference Image Quality: Likely images reconstructed with proven methods (e.g., FBP, ASiR-V) or images from phantoms with known properties.
Noise Characteristics: The DNN is trained to understand and model noise.

However, the specific methodology for establishing this ground truth for the training data (e.g., expert annotation, simulated data, pathology confirmed disease) is not detailed in the provided text.

Summary

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April 20, 2023

Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

GE Healthcare Japan Corporation % He Haibo Regulatory Affairs Leader 7-127, 4-chome, Asahigaoka Hino, Tokyo 191-8503 JAPAN

Re: K230807

Trade/Device Name: Deep Learning Image Reconstruction Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK Dated: March 23, 2023 Received: March 23, 2023

Dear He Haibo:

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

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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.

Lu Jiang

Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Radiological 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

510(k) Number (if known)

K230807

Device Name Deep Learning Image Reconstruction

Indications for Use (Describe)

Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.

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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Image /page/3/Picture/1 description: The image shows the GE Healthcare logo. The logo consists of a purple circular emblem with a stylized "GE" monogram inside. To the right of the emblem is the text "GE HealthCare" in a sans-serif font, also in purple. The logo is clean and modern, representing the company's brand identity.

K230807 510(k) SUMMARY OF SAFETY AND EFFECTIVNESS

This 510(k) summary of Safety and Effectiveness information is submitted in accordance with the requirement of 21 CFR Part 807.92:

Date:	April 20, 2023
Submitter:	GE Healthcare Japan Corporation7-127, Asahigaoka, 4-chomeHino-shi, Tokyo, 191-8503, Japan
Primary Contact:	He HaiboRegulatory Affairs LeaderPhone: 86-010-5708-3413Email: haibo.he1@ge.com
Secondary Contacts:	Helen PengSenior Regulatory Affairs DirectorPhone: 1-262-424-8222Email: hong.peng@med.ge.comLaura TurnerRegulatory Affairs ManagerPhone: 1-262-200-1044Email: laura.turner@ge.com
Subject Device Name:	Deep Learning Image Reconstruction
Device Classification	Class II
Regulation Number/Product Code:	21 CFR 892.1750 Computed tomography x-ray system / JAK

Predicate Device Information

Device Name:	Deep Learning Image Reconstruction
Manufacturer:	GE Medical Systems, LLC
510(k) Number:	K213999 cleared on February 18, 2022
Regulation Number/Product Code:	21 CFR 892.1750 Computed tomography x-ray system / JAI

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Image /page/4/Picture/1 description: The image shows the GE HealthCare logo. The logo consists of a purple circular emblem with the letters "GE" intertwined inside. To the right of the emblem, the words "GE HealthCare" are written in purple, with "GE" slightly larger than "HealthCare".

Reference Devices Information

Device Name:	ASiR-V
Manufacturer:	GE Medical Systems, LLC
510(k) Number:	K133640 cleared on March 25, 2014
Regulation Number/Product Code:	21 CFR 892.1750 Computed tomography x-ray system / JAK
Device Name:	Deep Learning Image Reconstruction
Manufacturer:	GE Healthcare Japan Corporation
510(k) Number:	K212067 cleared on September 17, 2021
Regulation Number/Product Code:	21 CFR 892.1750 Computed tomography x-ray system / JAK

Device Description

The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction support a normal throughput for routine CT.

The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity™ CT Images".

Compared to the predicate device, the intended use and indications for use of Deep Learning Image Reconstruction are identical.

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Image /page/5/Picture/1 description: The image shows the GE HealthCare logo. The logo consists of a purple circular emblem with a stylized "GE" monogram inside. To the right of the emblem, the text "GE HealthCare" is written in a simple, sans-serif font, also in purple. The logo is clean and modern, representing the company's brand identity.

Intended Use

The Deep Learning Image Reconstruction software is intended for head, whole body, cardiac, and vascular CT scans.

Indications for Use

The Deep Learning Image Reconstruction software is a deep-learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages.

Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.

Comparisons

The GEHC Deep Learning Image Reconstruction (DLIR) option for Revolution Maxima, Revolution Ascend and Discovery CT750 HD family CT systems is substantially equivalent to the predicate device K213999 for Revolution CT/Apex platform. The fundamental technology, i.e., the DLR algorithm, remains unchanged from the predicate. The table below summarizes the substantive feature/ technological similarities and differences between the predicate device and the proposed device.

Specification/Attribute	Predicate DeviceDeep Learning Image Reconstruction(K213999)	Proposed DeviceDeep Learning ImageReconstruction
Technology	DLIR uses a dedicated Deep NeuralNetwork (DNN) which is trained on theCT scanner and therefore models thepropagation of noise through thesystem to identify and remove thenoise	Same
System statistics -Noise modeling ofthe data collectionimaging chain(photon noise andelectronic noise)	Characterization of the photonstatistics as it propagates through thepreprocessing and calibration imagingchain	Same
System statistics -Noise characteristicsof the reconstructedimages	DLIR uses a trained DNN which modelsthe scanned object using informationobtained from extensive phantom andclinical data to identify the noisecharacteristics and remove it	Same

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Image /page/6/Picture/1 description: The image shows the GE HealthCare logo. The logo consists of a purple circular emblem on the left and the text "GE HealthCare" in purple on the right. The emblem appears to be a stylized version of the letters "GE" intertwined.

Specification/Attribute	Predicate DeviceDeep Learning Image Reconstruction(K213999)	Proposed DeviceDeep Learning ImageReconstruction
Clinical Workflow	Select recon type and strength (Low,Medium, High).	Same
Referenceprotocols/dose	Using the same reference protocolsprovided on the Revolution CT/Apexplatform systems for ASIR-V	Using the same reference protocolsprovided on the Revolution EVO,Revolution Maxima, RevolutionAscend and Discovery CT750 HDfamily systems for ASIR-V
DeploymentEnvironment	On CT Console	On CT ConsoleOn GE's Edison Platform.

The subject device Deep Learning Image Reconstruction does not introduce any additional risks/hazards, warnings, or limitations.

Determination of Substantial Equivalence

Summary of Non-Clinical Testing

Deep Learning Image Reconstruction has successfully completed the design control testing per our quality system. No additional hazards were identified, and no unexpected test results were observed. Deep Learning Image Reconstruction was designed under the Quality System Regulations of 21CFR 820 and ISO 13485. GEHC believes that the extensive bench testing is sufficient for FDA's substantial equivalence determination.

The following quality assurance measures have been applied to the development of the system:

. Requirement Definition
Risk Analysis and Control
Technical Design Reviews
Formal Design Reviews
Software Development Lifecycle
- O Code Review
- Software Unit Implementation O
- Software Integrations and Integration Testing O
System Testing
- Safety Testing (Verification) o
- O Image Performance Testing (Verification)
- Simulating Use Testing (Validation) O
Software Release

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Image /page/7/Picture/1 description: The image shows the GE Healthcare logo. The logo consists of the GE monogram in a purple circle on the left, followed by the words "GE HealthCare" in purple text on the right. The text is in a sans-serif font and is slightly blurred.

The testing and results did not raise different questions of safety and effectiveness than associated with predicate device. We consider the proposed device is substantially equivalent to the predicate device, DLIR for Revolution CT/Apex platform systems.

The substantial equivalence is also based on the software documentation for a "Moderate" level of concern.

Engineering bench testing was also performed to support substantial equivalence and the product performance claims. The evaluation and analysis used the same test methodologies and acceptance criteria with the identical raw datasets obtained on GEHC's Revolution Ascend, Revolution Frontier and Discovery CT750 HD systems and then applying the Deep Learning Image Reconstruction or ASiR-V reconstruction (hence the dose (CTDIvol) is identical for both). The resultant images were then compared for:

Low Contrast Detectability (LCD)
Image Noise (pixel standard deviation)
High-Contrast Spatial Resolution (MTF)
Streak Artifact Suppression
Spatial Resolution, longitudinal (FWHM slice sensitivity profile)
Noise Power Spectrum (NPS) and Standard Deviation of noise
CT Number Uniformity
CT Number Accuracy
Contrast to Noise (CNR) ratio
Artifact analysis metal objects, unintended motion, truncation ●
Pediatric Phantom IQ Performance Evaluation
Low Dose Lung Cancer Screening Protocol IQ Performance Evaluation

Substantial Equivalence

Deep Learning Image Reconstruction for Revolution Maxima, Revolution Ascend and Discovery CT750 HD family was developed under GEHC Healthcare's quality system. The changes associated with Deep Learning Image Reconstruction (DLIR) do not change the Intended Use from the predicate, and represent equivalent technology characteristics, with no impact on control mechanism, operating principle, and energy type. Design verification, along with bench testing provided in this submission demonstrates that the Deep Learning Image Reconstruction (DLIR) met all of its design requirement and performance criteria. GEHC's quality system's design, verification, and risk management processes did not identify any additional hazards, unexpected results, or adverse effects stemming from the changes to the predicate.

GE HealthCare believes that Deep Learning Image Reconstruction software is substantially equivalent to the legally marketed predicate device and hence is safe and effective for its intended use.

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.