(119 days)
Yes
The device description explicitly states that it uses a "deep learning based reconstruction method" and a "dedicated Deep Neural Network (DNN)". The name of the software itself is "Deep Learning Image Reconstruction".
No
The device is described as an "image reconstruction method" intended to "produce cross-sectional images" from X-ray transmission data. Its function is to process imaging data, not to directly treat or diagnose a disease or condition.
No
The device is an image reconstruction software that produces CT images, which are then used by a clinician for diagnosis. The software itself does not provide a diagnosis.
No
The device is described as software integrated into a scanner's existing raw data-based image reconstruction chain. While the core technology is software (Deep Learning Image Reconstruction), it is presented as a component of a larger CT scanner system, not a standalone software-only medical device. The description implies it relies on and interacts directly with the hardware of the CT scanner to function.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The intended use is to produce cross-sectional images of the head and whole body from X-ray transmission data. This is a function of a medical imaging device (CT scanner), not a test performed on biological samples in vitro (outside the body).
- Device Description: The device is described as an image reconstruction method integrated into a CT scanner. It processes raw X-ray data to create images. This is a core function of a CT system.
- Input: The input is X-ray transmission data, not biological samples like blood, urine, or tissue.
- Output: The output is cross-sectional images (DICOM compatible), not diagnostic results derived from analyzing biological samples.
IVD devices are specifically designed to examine specimens derived from the human body to provide information for diagnostic, monitoring, or compatibility purposes. This device operates on physical data acquired from the patient's body using X-rays, not on biological samples.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a 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 (comma separated list FDA assigned to the subject device)
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' preference for the specific clinical need.
DLIR has been cleared on GE's multiple CT systems of varying image chains, including Revolution CT systems (K133705, K163213, K19177), Revolution EVO (K131576) /Revolution Maxima (K192686) and Revolution Ascend (K201369). Now the DLR algorithm is being ported to Discovery CT750 HD family CT systems (K120833) which include Discovery CT750 HD, Revolution Frontier and Revolution Discovery CT, 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 from Computed Tomography (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
Not Found
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
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. GE believes that the extensive software testing are sufficient for FDA's substantial equivalence determination.
The following quality assurance measures have been applied to the development of the software:
- Requirement Definition
- Risk Analysis and Control
- . Technical Design Reviews
- Formal Design Reviews
- Software Development Lifecycle
- o Code Review
- O Software Unit Implementation
- O Software Integrations and Integration Testing
- System Testing
- Safety Testing (Verification) O
- Image Performance Testing (Verification) O
- O Simulating Use Testing (Validation)
- 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 for Revolution CT products.
Additionally, the same set of tests and test methods employed on the predicate DLIR for Revolution CT were reproduced to support substantial equivalence and the product performance claims of the subject device.
Design verification and validation, including IQ bench testing, demonstrate that the Deep Learning Image Reconstruction (DLIR) software met all of its design requirement and performance criteria. Design control and risk management processes did not identify any new hazards, unexpected results, or adverse effects stemming from the changes to the predicate.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
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.
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July 29, 2022
GE Healthcare Japan Corporation % Wang Xing Regulatory Affairs Manager GE Hangwei Medical Systems Co., Ltd. West Area of Building No.3. No.1 Yongchang North Road Beijing Economic & Technological Development Area, Beijing 100176 CHINA
Re: K220961
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: July 13, 2022 Received: July 14, 2022
Dear Wang Xing:
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
1
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 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 (OS) 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,
Laurel Burk, Ph.D. Assistant Director DHT8B: Division of 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)
K220961
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) | |
|---|---|
| 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 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, and it is associated with a well-known and established company.
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: | March 31, 2022 |
|---|---|
| Submitter: | GE Healthcare Japan Corporation |
| 7-127, Asahigaoka, 4-chome | |
| Hino-shi, Tokyo, 191-8503, Japan | |
| Primary Contact: | Wang Xing |
| Regulatory Affairs Manager | |
| Phone +86 (10) 57388271 | |
| Email: Xing1.wang@ge.com | |
| Secondary Contacts: | Helen Peng |
| Senior Regulatory Affairs Director | |
| GE Healthcare | |
| Tel: +1 (262) 4248222 | |
| Email: hong.peng@med.ge.com | |
| John Jaeckle | |
| Chief Regulatory Affairs Strategist | |
| GE Healthcare | |
| Tel: +1 (262) 4249547 | |
| 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 /JAK |
| Predicate Device Information | |
| Device Name: | Deep Learning Image Reconstruction |
| Manufacturer: | GE Medical Systems, LLC |
| 510(k) Number: | K183202 cleared on April 12, 2019 |
| Regulation Number/ Product Code: | 21 CFR 892.1750 Computed tomography x-ray system /JAK |
| 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: | Discovery CT750 HD |
| Manufacturer: | GE Medical Systems, LLC |
| 510(k) Number: | K120833 cleared on June 13, 2012 |
| Regulation Number/ Product Code: | 21 CFR 892.1750 Computed tomography x-ray system /JAK |
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Image /page/4/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters "GE" in a stylized script, enclosed within a blue circle. There are decorative swirls around the letters, adding a touch of elegance to the design. The logo is simple, recognizable, and represents the General Electric brand.
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' preference for the specific clinical need.
DLIR has been cleared on GE's multiple CT systems of varying image chains, including Revolution CT systems (K133705, K163213, K19177), Revolution EVO (K131576) /Revolution Maxima (K192686) and Revolution Ascend (K201369). Now the DLR algorithm is being ported to Discovery CT750 HD family CT systems (K120833) which include Discovery CT750 HD, Revolution Frontier and Revolution Discovery CT, thus triggering this premarket notification.
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.
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Image /page/5/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 four water droplet shapes surrounding the circle. The logo is simple and recognizable, and it is often used to represent the company's brand.
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 Discovery CT750 HD family CT systems, is substantially equivalent to the unmodified predicate device DLR reconstruction option (K183202) for Revolution CT family. 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:
| Specification/ Attribute | Deep Learning Image
Reconstruction | Deep Learning Image
Reconstruction |
|-----------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | (Predicate Device, K183202) | (Proposed Device) |
| Technology | Utilizes a dedicated Deep Neural
Network (DNN) 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 CT
family systems for ASiR-V | Using the same Reference protocols
provided on the Discovery CT750
HD family CT systems 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 CT family systems | Image noise, low contrast
detectability, spatial resolution, and
low signal artifact suppression as
good or better than ASiR-V on
Discovery CT750 HD family CT
systems |
| Deployment
Environment | On CT Console | On GE's Edison Platform. |
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Image /page/6/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 circular frame. The logo is blue and the background is white.
Deep Learning Image Reconstruction on the Discovery CT750 HD family CT systems 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 Performance 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. GE believes that the extensive software testing are sufficient for FDA's substantial equivalence determination.
The following quality assurance measures have been applied to the development of the software:
- Requirement Definition
- Risk Analysis and Control
- . Technical Design Reviews
- Formal Design Reviews
- Software Development Lifecycle
- o Code Review
- O Software Unit Implementation
- O Software Integrations and Integration Testing
- System Testing
- Safety Testing (Verification) O
- Image Performance Testing (Verification) O
- O Simulating Use Testing (Validation)
- 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 for Revolution CT products.
The substantial equivalence is also based on the software documentation for a "Moderate" level of concern device.
Additionally, the same set of tests and test methods employed on the predicate DLIR for Revolution CT were reproduced to support substantial equivalence and the product performance claims of the subject device.
Substantial Equivalence
Deep Learning Image Reconstruction (DLIR) software for Discovery CT750 HD family CT systems was developed under GE 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.
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Image /page/7/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 circle. Three water droplet-like shapes surround the circle, adding a decorative element to the design. The logo is presented in a blue color scheme.
Design verification and validation, including IQ bench testing, demonstrate that the Deep Learning Image Reconstruction (DLIR) software met all of its design requirement and performance criteria. Design control and risk management processes did not identify any new 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.