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.

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: dose, 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 software 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' preferences and experience for the specific clinical need.

AI/ML Overview

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

Acceptance Criteria and Device Performance

The core of the acceptance criteria revolves around demonstrating that the Deep Learning Image Reconstruction (DLIR) on the Revolution Ascend system is substantially equivalent to its predicate device (DLIR on Revolution EVO) and performs at least as well as, or better than, ASiR-V reconstruction in key image quality metrics.

Acceptance Criteria Category	Specific Criterion	Reported Device Performance (Deep Learning Image Reconstruction)
Image Quality Metrics (vs. ASiR-V)	Image noise (pixel standard deviation)	As good or better than ASIR-V on Revolution Ascend.
	Low contrast detectability (LCD)	As good or better than ASIR-V on Revolution Ascend.
	High-contrast spatial resolution (MTF)	As good or better than ASIR-V on Revolution Ascend.
	Streak artifact suppression	As good or better than ASIR-V on Revolution Ascend.
	Spatial Resolution	Tested, explicit comparison to ASIR-V not detailed but implied to be equivalent or better based on overall claim.
	Noise Power Spectrum (NPS) and Standard Deviation of noise	NPS plots similar to traditional FBP images while maintaining ASiR-V performance.
	CT Number Accuracy and Uniformity	Tested, explicit comparison to ASIR-V not detailed but implied to be equivalent or better based on overall claim.
	Contrast to Noise (CNR) ratio	Tested, explicit comparison to ASIR-V not detailed but implied to be equivalent or better based on overall claim.
Safety and Effectiveness	No new risks/hazards, warnings, or limitations compared to predicate.	No new risks/hazards, warnings, or limitations were identified. Substantially equivalent and as safe and effective as the predicate.
Clinical Equivalence	Intended use and indications for use remain identical to the predicate device.	Intended use and indications for use are identical to the predicate.
Fundamental Technology	Fundamental control mechanism, operating principle, and energy type unchanged from the predicate.	Fundamental control mechanism, operating principle, and energy type unchanged. The DLIR algorithm remains unchanged from the predicate.
Clinical Workflow	Maintain existing clinical workflow (select recon type and strength).	Same as predicate.
Reference Protocols/Dose	Use same reference protocols provided on Revolution Ascend for ASiR-V (implies similar dose performance).	Using the same Reference Protocols provided on the Revolution Ascend system for ASiR-V. (This implies similar dose performance as inherent in the reference protocols which likely target optimized dose).
Deployment Environment	Deployment on GE's Edison Platform.	Same as predicate.
Diagnostic Use	Image quality related to diagnostic use is assessed favorably by experts.	Demonstrated through favorable assessment by board-certified radiologists who independently assessed image quality for diagnostic use.
Image Noise Texture/Sharpness	Favorable comparison to ASiR-V in terms of image noise texture and image sharpness.	Readers directly compared ASiR-V and DLIR images and assessed these key metrics. (Implied positive outcome based on substantial equivalence claim).
Pediatric Image Quality	Performance for pediatric images.	Evaluation performed. (Implied acceptable performance).
Low Dose Lung Cancer Screening	Performance for Low Dose Lung Cancer Screening.	Evaluation performed. (Implied acceptable performance).

Study Details

Sample size used for the test set and the data provenance:
- Sample Size: A total of 60 retrospectively collected clinical cases were used.
- Data Provenance: The data was retrospectively collected. The country of origin is not explicitly stated in the provided text.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of Experts: 9 board-certified radiologists.
- Qualifications: These radiologists had "expertise in the specialty areas that align with the anatomical region of each case."
Adjudication method for the test set:
- Each image was read by 3 different radiologists.
- The readers completed their evaluations independently and were blinded to the results of the other readers' assessments.
- The text doesn't explicitly state an adjudication method like 2+1 or 3+1 for discrepancies. It implies a consensus or agreement was sought, or that individual assessments contributed to the overall conclusion of substantial equivalence. Given they provided an assessment on a Likert scale and then compared images, it seems individual reader assessments were aggregated, rather than a discrepancy resolution process.
If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
- Yes, an MRMC study was implicitly done, as 9 radiologists evaluated 60 cases, with each case being read by 3 different radiologists. The study involved a comparison between ASiR-V reconstructions and Deep Learning Image Reconstruction (DLIR) images.
- Effect Size: The document does not provide a specific effect size (e.g., percentage improvement in accuracy or AUC) of how much human readers improved with DLIR assistance compared to ASiR-V. It states that the study results "support substantial equivalence and performance claims" and that readers assessed image quality and compared noise texture and sharpness, implying favorable or equivalent performance.
If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, extensive standalone (algorithm only) non-clinical engineering bench testing was performed. This included evaluations of:
  - Low contrast detectability (LCD)
  - Image Noise (pixel standard deviation)
  - High contrast spatial resolution (MTF)
  - Streak Artifact Suppression
  - Spatial Resolution
  - Noise Power Spectrum (NPS) and Standard Deviation of noise
  - CT Number Accuracy and Uniformity
  - Contrast to Noise (CNR) ratio
  - Artifact analysis - metal objects, unintended motion, truncation
  - Pediatric Image Quality Performance
  - Low Dose Lung Cancer Screening
The type of ground truth used:
- For the clinical reader study, the ground truth was based on expert assessment/consensus (implying the "gold standard" for diagnostic image quality, noise texture, and sharpness was the radiologists' expert opinion). The cases were "retrospectively collected clinical cases," suggesting the presence of a known clinical diagnosis or outcome, but the specific ground truth for disease presence/absence is not explicitly stated as the primary output of the DLIR evaluation. The evaluation focused more on image quality attributes and comparison between reconstruction methods rather than diagnostic accuracy against a separate definitive truth.
The sample size for the training set:
- The document states the Deep Neural Network (DNN) was "trained on the Revolution family CT Scanners" but does not provide the specific sample size (number of images or cases) used for training.
How the ground truth for the training set was established:
- The text does not explicitly detail how the ground truth for the training set was established. It mentions the DNN was "designed and trained specifically to generate CT Images to give an image appearance... similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: dose, image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression." This suggests the training likely involved pairing raw CT data with expertly reconstructed ASiR-V or FBP images as a reference for image quality characteristics. The ground truth in this context would be the desired output image characteristics (e.g., low noise, high resolution) that the DLIR algorithm was optimized to reproduce.

Summary

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September 17, 2021

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GE Healthcare Japan Corporation % Katelyn Rowley Regulatory Affairs Leader GE Medical Systems, LLC 3000 North Grandview Blvd. WAUKESHA WI 53188

Re: K212067

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: August 27, 2021 Received: August 30, 2021

Dear Katelyn Rowley:

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 medical devices and radiation-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) K212067

Device Name Deep Learning Image Reconstruction

Indications for Use (Describe)

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.

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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K212067

510(k) SUMMARY

This 510(k) summary of Safety and Effectiveness information is submitted in accordance with the requirement of 21 CFR Part 807.87(h):

Date:	July 01, 2021
Submitter:	GE Healthcare Japan Corporation7-127, Asahigaoka, 4-chomeHino-shi, Tokyo, 191-8503, Japan
Primary Contact:	Katelyn RowleyRegulatory Affairs LeaderPhone 262-309-5888Email: Katelyn.rowely@ge.com
Secondary Contacts:	Helen PengSenior Regulatory Affairs DirectorGE HealthcareTel: 262-424-8222Email: hong.peng@med.ge.comJohn JaeckleChief Regulatory Affairs StrategistGE HealthcareTel: 262-424-9547Email: john.jaeckle@med.ge.com
Device Trade Name:	Deep Learning Image Reconstruction
Device Classification	Class II
Regulation Number/Product Code:	21 CFR 892.1750 Computed tomography x-ray system / JAE
Predicate Device Information
Device Name:	Deep Learning Image Reconstruction
Manufacturer:	GE Medical Systems, LLC
510(k) Number:	K193170 cleared on December 13, 2019
Regulation Number/Product Code:	21 CFR 892.1750 Computed tomography x-ray system / JAE

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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:	Revolution Ascend
Manufacturer:	GE Healthcare Japan Corporation
510(k) Number:	K203169 cleared on March 20, 2020
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 software 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".

Deep Learning Image Reconstruction software was initially introduced on the Revolution CT systems (K133705, K163213). Subsequently, it was introduced on the Revolution EVO system (K131576) and cleared in December 2019 with K193170. The DLIR algorithm is now being ported, without retraining, to Revolution Ascend (K203169), thus triggering this premarket notification.

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

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Intended Use

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

Indications for Use

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

Comparisons

The GE Deep Learning Image Reconstruction (DLIR) software for the Revolution Ascend is substantially equivalent to the unmodified predicate device DLIR reconstruction option for Revolution EVO CT systems. The fundamental technology, i.e the DLIR algorithm, remains unchanged from the predicate. The table below summarizes the substantive feature/technological differences between the predicate device and the proposed device:

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GE Healthcare 510(k) Premarket Notification Submission - DLIR

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Specification/Attribute	Deep Learning ImageReconstruction(Predicate Device, K193170)	Deep Learning ImageReconstruction(Proposed Device)
Technology	Utilizes a dedicated Deep NeuralNetwork (DNN) which wastrained on the Revolution familyCT Scanners and designedspecifically to generate highquality CT images	Same
Clinical Workflow	Select recon type and strength(Low, Medium, High).	Same
Clinical Use	Routine Clinical Use	Same
Referenceprotocols/dose	Using the same Referenceprotocols provided on theRevolution EVO system forASIR-V	Using the same Referenceprotocols provided on theRevolution Ascend system forASIR-V
IQ performance vs dose	Image noise, low contrastdetectability, spatial resolution,and low signal artifactsuppression as good or betterthan ASIR-V on Revolution EVO	Image noise, low contrastdetectability, spatial resolution,and low signal artifactsuppression as good or betterthan ASIR-V on RevolutionAscend
DeploymentEnvironment	On GE's Edison Platform.	Same

Deep Learning Image Reconstruction as deployed on the Revolution Ascend CT System does not introduce any new risks/hazards, warnings, or limitations.

The changes described above do not change the fundamental control mechanism, operating principle, and do not change the intended use from the predicate device.

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

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Regulations of 21CFR 820 and ISO 13485. GE believes that the extensive bench testing and the physician evaluation are 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
- Code Review o
- Software Unit Implementation O
- O Software Integrations and Integration Testing
. System Testing
- Safety Testing (Verification) O
- Image Performance Testing (Verification) O
- Simulating Use Testing (Validation) O
Software Release

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.

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

Additional Non-Clinical Testing

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 GE's Revolution Ascend CT systems and then applies the Deep Learning Image Reconstruction or ASiR-V reconstruction. The resultant images were then compared for:

. Low contrast detectability (LCD)
트 Image Noise (pixel standard deviation)
. High contrast spatial resolution (MTF)
. Streak Artifact Suppression
. Spatial Resolution
. Noise Power Spectrum (NPS) and Standard Deviation of noise
트 CT Number Accuracy and Uniformity
. Contrast to Noise (CNR) ratio
. Artifact analysis - metal objects, unintended motion, truncation
. Pediatric Image Quality Performance
트 Low Dose Lung Cancer Screening

Clinical Testing

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The reader study used a total of 60 retrospectively collected clinal cases. The raw data from each of these cases was reconstructed with both ASiR-V and Deep Learning Image Reconstruction and presented to each reader independently. The results of the study support substantial equivalence and performance claims.

These images were read by 9 board-certified radiologists with expertise in the specialty areas that align with the anatomical region of each case. Each image was read by 3 different radiologists who provided an assessment of image quality related to diagnostic use according to a 5-point Likert scale. Additionally, the readers were asked to compare directly the ASiR-V and Deep Learning Image Reconstruction images according to the key metric of image noise texture and image sharpness. The readers completed their evaluations independently and were blinded to the results of the other readers' assessments.

Substantial Equivalence

The changes associated with Deep Learning Image Reconstruction do not change the Intended Use from the predicate, and represent equivalent technology, with no impact on control mechanism, operating principle, or energy type.

Deep Learning Image Reconstruction (DLIR) software for Revolution Ascend was developed under GE Healthcare's quality system. Design verification, validation along with bench testing and the clinical reader study demonstrate that the Deep Learning Image Reconstruction (DLIR) software is substantially equivalent and hence as safe and as effective as the legally marketed predicate device. GE's quality system's design, verification, and risk management processes did not identify any new hazards, unexpected results, or adverse effects stemming from the changes to the predicate.

Based on development under GE Healthcare's quality system, the successful verification and validation testing, including the additional engineering bench testing, and the clinical reader study, GE Healthcare believes that Deep Learning Image Reconstruction Software is substantially equivalent to the 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.