(77 days)
Yes
The device description explicitly states it uses a "dedicated Deep Neural Network (DNN)" and "deep learning technology" for image reconstruction.
No
Explanation: This device is for image reconstruction and processing, which aids in diagnosis by improving image quality, but it does not directly treat or prevent a disease or condition.
No
The device is image reconstruction software that produces CT images. It does not provide a diagnosis.
No
The device is described as being integrated into the scanner's existing raw data-based image reconstruction chain, implying it is part of a larger hardware system (the CT scanner) and not a standalone software product.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In vitro diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- Device Function: The described device is a software that processes X-ray transmission data to reconstruct CT images. It is part of the imaging process itself, not a test performed on a biological sample.
- Intended Use: The intended use is to produce cross-sectional images for diagnostic purposes, which is a function of medical imaging equipment, not an IVD.
The software is a component of a medical imaging system (CT scanner) that aids in the visualization of internal structures. While the resulting images are used for diagnosis, the software itself is not performing an in vitro diagnostic test.
No
The input document does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / 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, (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.
Product codes
JAK
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.
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
X-ray transmission data / CT
Anatomical Site
head and whole body
Indicated Patient Age Range
all ages
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
Utilizes a dedicated Deep Neural Network (DNN) which was trained on the Revolution family CT Scanners and designed specifically to generate high quality CT images.
Description of the test set, sample size, data source, and annotation protocol
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.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
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)
- 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 reader study used a total of 60 retrospectively collected clinal cases. The results of the study support substantial equivalence and performance claims.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
image noise texture and image sharpness.
Predicate Device(s)
Reference Device(s)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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.
0
September 17, 2021
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo features the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
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
1
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
2
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) |
CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
3
Image /page/3/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters 'GE' written in a stylized script, enclosed within a blue circle. There are three white water droplet shapes surrounding the circle, positioned at the top, left, and right sides.
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 Corporation |
| 7-127, Asahigaoka, 4-chome | |
| Hino-shi, Tokyo, 191-8503, Japan | |
| Primary Contact: | Katelyn Rowley |
| Regulatory Affairs Leader | |
| Phone 262-309-5888 | |
| Email: Katelyn.rowely@ge.com | |
| Secondary Contacts: | Helen Peng |
| Senior Regulatory Affairs Director | |
| GE Healthcare | |
| Tel: 262-424-8222 | |
| Email: hong.peng@med.ge.com | |
| John Jaeckle | |
| Chief Regulatory Affairs Strategist | |
| GE Healthcare | |
| Tel: 262-424-9547 | |
| Email: 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 |
4
Image /page/4/Picture/1 description: The image shows the logo for General Electric (GE). The logo is a blue circle with the letters "GE" in a stylized script inside. There are small white teardrop shapes around the inside of the circle. The blue color is a medium shade, and the white provides contrast.
| 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
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.
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.
5
Image /page/5/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters 'GE' intertwined in a stylized script, enclosed within a blue circle. There are also some white curved lines around the circle.
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 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:
6
GE Healthcare 510(k) Premarket Notification Submission - DLIR
Image /page/6/Picture/1 description: The image shows the logo for General Electric (GE). The logo is a blue circle with the letters "GE" in a stylized font in the center. There are three water droplet shapes surrounding the letters. The logo is simple and recognizable.
| Specification/
Attribute | Deep Learning Image
Reconstruction
(Predicate Device, K193170) | Deep Learning Image
Reconstruction
(Proposed Device) |
|-----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Technology | Utilizes a dedicated Deep Neural
Network (DNN) which was
trained on the Revolution family
CT Scanners and designed
specifically to generate high
quality CT images | Same |
| Clinical Workflow | Select recon type and strength
(Low, Medium, High). | Same |
| Clinical Use | Routine Clinical Use | Same |
| Reference
protocols/dose | Using the same Reference
protocols provided on the
Revolution EVO system for
ASIR-V | Using the same Reference
protocols provided on the
Revolution Ascend system for
ASIR-V |
| IQ performance vs dose | Image noise, low contrast
detectability, spatial resolution,
and low signal artifact
suppression as good or better
than ASIR-V on Revolution EVO | Image noise, low contrast
detectability, spatial resolution,
and low signal artifact
suppression as good or better
than ASIR-V on Revolution
Ascend |
| Deployment
Environment | 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
7
Image /page/7/Picture/1 description: The image shows the logo for General Electric (GE). The logo consists of the letters "GE" intertwined in a stylized script, enclosed within a blue circle. There are also three water droplets surrounding the circle, one at the top and one on each side.
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
8
Image /page/8/Picture/1 description: The image shows the logo for General Electric (GE). The logo is a blue circle with the letters "GE" in a stylized font in the center. There are decorative swirls around the letters. The logo is simple and recognizable.
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.