(28 days)
Yes
The device description explicitly states it uses a "dedicated Deep Neural Network (DNN)" and "deep learning technology" for image reconstruction.
No.
This device is software for image reconstruction in CT scans, designed to improve the quality of diagnostic images. It does not directly provide therapy or treatment to a patient.
No
Explanation: The device is a software for image reconstruction, enhancing the quality of CT images. It does not directly perform diagnosis but aids in producing images for diagnostic interpretation by a healthcare professional.
No
The device is described as being integrated into the scanner's existing raw data-based image reconstruction chain, implying it is a component of a larger hardware system (the CT scanner). While the core technology is software (DNN), it is not a standalone software device.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens taken from the human body, such as blood, urine, or tissue, to provide information about a person's health.
- Device Function: The Deep Learning Image Reconstruction software processes X-ray transmission data to create cross-sectional images of the human body. It does not analyze biological specimens.
- Intended Use: The intended use is to produce images for diagnostic purposes, but this is done by processing imaging data, not by analyzing biological samples.
The device is a medical imaging software that aids in the interpretation of medical images generated by a CT scanner. While it is used in the diagnostic process, it does not fit the definition of an In Vitro Diagnostic device.
No
The provided text 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, 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.
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: 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.
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
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.
Key Metrics
- Low Contrast Detectability (LCD)
- Image Noise (pixel standard deviation)
- High-Contrast Spatial Resolution (MTF)
- Streak Artifact Suppression
- Spatial Resolution, longitudinal (FWHM slice sensitivity profile)
- Noise Power Spectrum (NPS) and Standard Deviation of noise
- CT Number Accuracy and Uniformity
- Contrast to Noise (CNR) ratio
- Artifact analysis metal objects, unintended motion, truncation
- Pediatric Image Quality Performance
- Low Dose Lung Cancer Screening
- Image noise texture
- Image sharpness
- Image noise texture homogeneity
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.
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December 13, 2019
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
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
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 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)
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.
| 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.
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DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
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Image /page/3/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters 'GE' in a stylized, cursive font, enclosed within a blue circle. Three water droplets surround the circle, positioned at the top, left, and right. The logo is simple and recognizable, representing the company's brand identity.
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 Corporation |
| 7-127, Asahigaoka, 4-chome | |
| Hino-shi, Tokyo, 191-8503, Japan | |
| Primary Contact: | Tomohiro Ito |
| Sr. Regulatory Affairs Leader | |
| Phone +81-42-585-5383 or +81-90-8346-6807 | |
| Email: tomohiro.ito@ge.com | |
| Secondary Contacts: | Helen Peng |
| Sr. Regulatory Affairs Director | |
| GE Healthcare | |
| Tel: 262-424-8222 | |
| Email: 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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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 font in the center. There are three water droplets surrounding the letters. The logo is simple and recognizable.
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.
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
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 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 Image
Reconstruction | Deep Learning Image
Reconstruction |
|-----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | Predicate Device, (K183202) | (Proposed Device) |
| Technology | Utilizes a dedicated Deep Neural
Network (DNN) which is trained
for the Revolution family CT
Scanners and designed
specifically to generate high
quality CT images | Same |
| Clinical Workflow | Select recon type and strength
(High, Medium, Low) | Same |
| Clinical Use | Routine Clinical Use | Same |
| Reference
protocols/dose | Using the same Reference
protocols provided on the
Revolution CT system for ASiR-V | Using the same Reference
protocols provided on the
Revolution EVO 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 CT | Image noise, low contrast
detectability, spatial resolution,
and low signal artifact
suppression as good or better
than ASIR-V on Revolution EVO. |
| Deployment
Environment | 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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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 circle. The logo is simple and recognizable.
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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Image /page/7/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 circle. There are water droplet-like shapes surrounding the circle. The logo is blue.
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.