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: 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.

Deep Learning Image Reconstruction software was initially introduced on the Revolution CT systems (K133705, K163213). The DLR algorithm is now ported to Revolution EVO (K131576), which offers 64 detector row and up to 40mm collimation, and ASIR-V reconstruction option.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state quantitative "acceptance criteria" in a pass/fail format with numerical thresholds. Instead, it describes performance goals relative to the predicate device (ASiR-V) or traditional FBP images. The reported device performance generally indicates "as good as or better than" the reference.

Acceptance Criteria (Stated Goal)	Reported Device Performance
Image Appearance (Axial NPS plots)	Similar to traditional FBP images
Image Noise (pixel standard deviation)	As good as or better than ASiR-V
Low Contrast Detectability (LCD)	As good as or better than ASiR-V
High-Contrast Spatial Resolution (MTF)	As good as or better than ASiR-V
Streak Artifact Suppression	As good as or better than ASiR-V
Image Quality Preference (Reader Study)	DLIR images preferred over ASiR-V for image noise texture, image sharpness, and image noise texture homogeneity (Implied acceptance criteria: DLIR is preferred)

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

Sample Size: 60 retrospectively collected clinical cases.
Data Provenance: Retrospective. The origin country is not explicitly stated, but the submitter is GE Healthcare Japan Corporation, so it's possible some or all cases originated from Japan or a region where GE Healthcare Japan Corporation operates.

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

Number of Experts: 7 board-certified radiologists.
Qualifications: Board-certified radiologists with expertise in the specialty areas that align with the anatomical region of each case. The document does not specify years of experience.

4. Adjudication Method for the Test Set

Adjudication Method: Each image was read by 3 different radiologists who provided independent assessments of image quality. The readers were blinded to the results of other readers' assessments. There is no explicit mention of an adjudication process (e.g., 2+1 or 3+1 decision) for discrepant reader opinions; it appears the individual assessments were analyzed.

5. 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

MRMC Study: Yes, a clinical reader study was performed where 7 radiologists read images reconstructed with both ASiR-V (without DLIR) and DLIR.
Effect Size of Human Reader Improvement: The document states that readers were asked to "compare directly the ASIR-V and Deep Learning Image Reconstruction (DLIR) images according to three key metrics of image quality preference – image noise texture, image sharpness, and image noise texture homogeneity." It reports that the results support substantial equivalence and performance claims and implies a preference for DLIR images, but does not quantify the effect size of how much human readers "improve" with AI assistance in terms of diagnostic accuracy or efficiency. The study primarily focused on radiologists' preference for image quality characteristics.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

Standalone Performance: Yes, extensive non-clinical engineering bench testing was performed where DLIR and ASiR-V reconstructions were compared using identical raw datasets. This included objective metrics such as Low Contrast Detectability (LCD), Image Noise (pixel standard deviation), High-Contrast Spatial Resolution (MTF), Streak Artifact Suppression, Noise Power Spectrum (NPS), CT Number Accuracy and Uniformity, and Contrast to Noise (CNR) ratio. This constitutes a standalone (algorithm-only) performance evaluation.

7. The Type of Ground Truth Used

For the Reader Study (Clinical Performance): The ground truth for evaluating diagnostic use was based on the assessment of image quality related to diagnostic use according to a 5-point Likert Scale by board-certified radiologists. This is a form of expert consensus on image quality suitable for diagnosis, rather than a definitive "truth" established by pathology or patient outcomes.
For the Bench Testing (Technical Performance): The "ground truth" was the objective measurement of various image quality metrics (e.g., pixel standard deviation for noise, MTF for spatial resolution) in phantoms, which have known properties.

8. The Sample Size for the Training Set

The document states that the Deep Neural Network (DNN) used in Deep Learning Image Reconstruction was "trained specifically" but does not disclose the sample size of the training set.

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

The document implies that the DNN was trained to generate CT Images to give an image appearance similar to traditional FBP images while maintaining ASiR-V performance in certain areas. This suggests that existing "traditional FBP images" or images reconstructed with "ASiR-V" served as a reference or a form of "ground truth" for the training process. However, the exact methodology for establishing ground truth during the training phase (e.g., using paired low-dose/high-dose images, or simulated noise reduction) is not detailed in the provided text.

Summary

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December 13, 2019

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GE Healthcare Japan Corporation % Ms. Helen Peng Sr. Regulatory Affairs Director GE Medical Systems, LLC 3000 North Grandview Blvd. WAUKESHA WI 53188

Re: K193170

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: November 14, 2019 Received: November 15, 2019

Dear Ms. Peng:

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.

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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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration

Indications for Use

Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2020 See PRA Statement below.

510(k) Number (if known)

K193170

Device Name Deep Learning Image Reconstruction

Indications for Use (Describe)

Type of Use (Select one or both, as applicable)
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[X] Prescription Use (Part 21 CFR 801 Subpart D)	Over-The-Counter Use (21 CFR 801 Subpart C)
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K193170

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 807.87(h):

Date:	November 14, 2019
Submitter:	GE Healthcare Japan Corporation7-127, Asahigaoka, 4-chomeHino-shi, Tokyo, 191-8503, Japan
Primary Contact:	Tomohiro ItoSr. Regulatory Affairs LeaderPhone +81-42-585-5383 or +81-90-8346-6807Email: tomohiro.ito@ge.com
Secondary Contacts:	Helen PengSr. Regulatory Affairs DirectorGE HealthcareTel: 262-424-8222Email: hong.peng@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 / JAK
Predicate Device Information
Device Name:	Deep Learning Image Reconstruction
Manufacturer:	GE Medical System SCS (d.b.a GE Healthcare)
510(k) Number:	K183202 cleared on April 12, 2019
Regulation Number/Product Code:	21 CFR 892.1750 Computed tomography x-ray system / JAK
Reference Device Information
Device Name:	ASiR-V
Manufacturer:	GE Medical System SCS (d.b.a GE Healthcare)
510(k) Number:	K133640 cleared on March 25, 2014

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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".

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

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 software for the Revolution EVO is substantially equivalent to the unmodified predicate device DLIR reconstruction for Revolution fomily of CT systems. The fundamental technology, i.e the DLIR algorithm, remains unchanged from the

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predicate. The table below summarizes the substantive feature/technological differences between the predicate device and the proposed device:

Specification/Attribute	Deep Learning ImageReconstruction	Deep Learning ImageReconstruction
	Predicate Device, (K183202)	(Proposed Device)
Technology	Utilizes a dedicated Deep NeuralNetwork (DNN) which is trainedfor the Revolution family CTScanners and designedspecifically to generate highquality CT images	Same
Clinical Workflow	Select recon type and strength(High, Medium, Low)	Same
Clinical Use	Routine Clinical Use	Same
Referenceprotocols/dose	Using the same Referenceprotocols provided on theRevolution CT system for ASiR-V	Using the same Referenceprotocols provided on theRevolution EVO system forASiR-V
IQ performance vsdose	Image noise, low contrastdetectability, spatial resolution,and low signal artifactsuppression as good or betterthan ASIR-V on Revolution CT	Image noise, low contrastdetectability, spatial resolution,and low signal artifactsuppression as good or betterthan ASIR-V on Revolution EVO.
DeploymentEnvironment	On CT console	On GE's Edison Platform.

Deep Learning Image Reconstruction algorithm itself 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 software has successfully completed the design control testing per our quality system. No new hazards were identified, and no unexpected test results were obtained. Deep Learning Image Reconstruction was designed under the Quality System Regulations of 21CFR 820 and ISO 13485. GE believes that the extensive software testing, IQ

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bench testing, and the clinical reader study evaluation are sufficient for FDA's substantial equivalence determination.

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

. Risk Analysis and Control
. Requirement Reviews
. Design Reviews
. Software Development Lifecycle
. Verification Testing at unit, integration and system levels
. Design Validation Resting

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 family of products.

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

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 EVO CT System and then applies the Deep Learning Image Reconstruction (DLIR) or ASiR-V reconstruction (hence the dose (CTDIvol) is identical for both). The resultant images were then compared for:

Low Contrast Detectability (LCD) using the head and body MITA/FDA low contrast phantoms and a model observer
Low Contrast Detectability (LCD) with statistical method
Image Noise (pixel standard deviation) using both head and body uniform phantoms

■ High-Contrast Spatial Resolution (MTF) using a quality assurance phantom with a small diameter tungsten wire surrounded by water inside the phantom to generate the point spread function

▪ Streak Artifact Suppression using an oval uniform polyethylene phantom with embedded high attenuation objects to produce the artifacts

Spatial Resolution, longitudinal (FWHM slice sensitivity profile)
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

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Clinical Testing

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 (DLIR) and presented to each reader independently. The results of the study support substantial equivalence and performance claims.

These images were read by 7 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. The readers completed their evaluations independently and were blinded to the results of the other readers' assessments

Additionally, the readers were asked to compare directly the ASIR-V and Deep Learning Image Reconstruction (DLIR) images according to three key metrics of image quality preference – image noise texture, image sharpness, and image noise texture homogeneity.

Substantial Equivalence

The changes associated with Deep Learning Image Reconstruction (DLIR) software do not change the Indications for Use from the predicate, and represent equivalent technological characteristics, with no impact on control mechanism, operating principle, and energy type.

Deep Learning Image Reconstruction (DLIR) software for Revolution EVO 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, validation 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, the additional engineering bench testing, and the clinical reader study, GE Healthcare believes that the Deep Learning Image Reconstruction (DLIR) Software is substantially equivalent to the unmodified 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.