(28 days)
Yes
The device description explicitly states it uses a "dedicated Deep Neural Network (DNN)" and refers to the technology as "deep learning".
No
The device is described as an "image reconstruction method" and its intended use is to "produce cross-sectional images." It processes X-ray transmission data to generate images, which is diagnostic, not therapeutic.
No
This device is an image reconstruction software that processes X-ray transmission data to produce CT images. It does not perform diagnosis itself; it generates images that can be used by a clinician for diagnostic purposes.
No
The device is described as an image reconstruction method integrated into existing CT scanners, which are hardware devices. While the core technology is software (Deep Learning Image Reconstruction), it functions as a component of a larger hardware system that acquires the raw data. The description explicitly mentions it being "integrated into the scanner's existing raw data-based image reconstruction chain."
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are devices intended for use in vitro for the examination of specimens, including blood, tissue, and urine, derived from the human body, solely or principally for the purpose of providing information concerning a physiological or pathological state, or in congenital abnormalities.
- Device Function: The described device is a software that processes X-ray transmission data to reconstruct cross-sectional images of the human body. It is an image processing tool used in conjunction with a CT scanner.
- Lack of Specimen Analysis: The device does not analyze biological specimens (blood, tissue, etc.) derived from the human body. Its input is X-ray data, not biological samples.
Therefore, while it is a medical device used for diagnostic purposes, it falls under the category of medical imaging software rather than an In Vitro Diagnostic device.
No
The document states "Control Plan Authorized (PCCP) and relevant text Not Found", indicating no explicit mention or approval of a PCCP for this 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: 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 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.
The DLR algorithm was modified on the Revolution CT/Apex platform for improved reconstruction speed and image quality and cleared in February 2022 with K213999. The same modified DLIR is now being ported to Revolution EVO (K131576) /Revolution Maxima (K192686), Revolution Ascend (K203169, K213938) and Discovery CT750 HD family CT systems including Discovery CT750 HD, Revolution Frontier and Revolution Discovery CT (K120833).
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
On CT Console
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)
Engineering bench testing was performed. The evaluation and analysis used the same test methodologies and acceptance criteria with the identical raw datasets obtained on GEHC's Revolution Ascend, Revolution Frontier and Discovery CT750 HD systems and then applying the Deep Learning Image Reconstruction or ASiR-V reconstruction (hence the dose (CTDIvol) is identical for both).
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
- 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 Uniformity
- CT Number Accuracy
- Contrast to Noise (CNR) ratio
- Artifact analysis metal objects, unintended motion, truncation
- Pediatric Phantom IQ Performance Evaluation
- Low Dose Lung Cancer Screening Protocol IQ Performance Evaluation
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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April 20, 2023
Image /page/0/Picture/1 description: The image shows 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 blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
GE Healthcare Japan Corporation % He Haibo Regulatory Affairs Leader 7-127, 4-chome, Asahigaoka Hino, Tokyo 191-8503 JAPAN
Re: K230807
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: March 23, 2023 Received: March 23, 2023
Dear He Haibo:
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.
Lu Jiang
Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Radiological 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)
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 GE Healthcare logo. The logo consists of a purple circular emblem with a stylized "GE" monogram inside. To the right of the emblem is the text "GE HealthCare" in a sans-serif font, also in purple. The logo is clean and modern, representing the company's brand identity.
K230807 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 807.92:
| Date: | April 20, 2023 |
|---|---|
| Submitter: | GE Healthcare Japan Corporation |
| 7-127, Asahigaoka, 4-chome | |
| Hino-shi, Tokyo, 191-8503, Japan | |
| Primary Contact: | He Haibo |
| Regulatory Affairs Leader | |
| Phone: 86-010-5708-3413 | |
| Email: haibo.he1@ge.com | |
| Secondary Contacts: | Helen Peng |
| Senior Regulatory Affairs Director | |
| Phone: 1-262-424-8222 | |
| Email: hong.peng@med.ge.com | |
| Laura Turner | |
| Regulatory Affairs Manager | |
| Phone: 1-262-200-1044 | |
| Email: laura.turner@ge.com | |
| Subject Device 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: | K213999 cleared on February 18, 2022 |
| Regulation Number/ | |
| Product Code: | 21 CFR 892.1750 Computed tomography x-ray system / JAI |
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Image /page/4/Picture/1 description: The image shows the GE HealthCare logo. The logo consists of a purple circular emblem with the letters "GE" intertwined inside. To the right of the emblem, the words "GE HealthCare" are written in purple, with "GE" slightly larger than "HealthCare".
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: | Deep Learning Image Reconstruction |
| Manufacturer: | GE Healthcare Japan Corporation |
| 510(k) Number: | K212067 cleared on September 17, 2021 |
| 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: 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 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.
The DLR algorithm was modified on the Revolution CT/Apex platform for improved reconstruction speed and image quality and cleared in February 2022 with K213999. The same modified DLIR is now being ported to Revolution EVO (K131576) /Revolution Maxima (K192686), Revolution Ascend (K203169, K213938) and Discovery CT750 HD family CT systems including Discovery CT750 HD, Revolution Frontier and Revolution Discovery CT (K120833).
Compared to the predicate device, the intended use and indications for use of Deep Learning Image Reconstruction are identical.
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Image /page/5/Picture/1 description: The image shows the GE HealthCare logo. The logo consists of a purple circular emblem with a stylized "GE" monogram inside. To the right of the emblem, the text "GE HealthCare" is written in a simple, sans-serif font, also in purple. The logo is clean and modern, representing the company's brand identity.
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 GEHC Deep Learning Image Reconstruction (DLIR) option for Revolution Maxima, Revolution Ascend and Discovery CT750 HD family CT systems is substantially equivalent to the predicate device K213999 for Revolution CT/Apex platform. The fundamental technology, i.e., the DLR algorithm, remains unchanged from the predicate. The table below summarizes the substantive feature/ technological similarities and differences between the predicate device and the proposed device.
| Specification/
Attribute | Predicate Device
Deep Learning Image Reconstruction
(K213999) | Proposed Device
Deep Learning Image
Reconstruction |
|----------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------|
| Technology | DLIR uses a dedicated Deep Neural
Network (DNN) which is trained on the
CT scanner and therefore models the
propagation of noise through the
system to identify and remove the
noise | Same |
| System statistics -
Noise modeling of
the data collection
imaging chain
(photon noise and
electronic noise) | Characterization of the photon
statistics as it propagates through the
preprocessing and calibration imaging
chain | Same |
| System statistics -
Noise characteristics
of the reconstructed
images | DLIR uses a trained DNN which models
the scanned object using information
obtained from extensive phantom and
clinical data to identify the noise
characteristics and remove it | Same |
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Image /page/6/Picture/1 description: The image shows the GE HealthCare logo. The logo consists of a purple circular emblem on the left and the text "GE HealthCare" in purple on the right. The emblem appears to be a stylized version of the letters "GE" intertwined.
| Specification/
Attribute | Predicate Device
Deep Learning Image Reconstruction
(K213999) | Proposed Device
Deep Learning Image
Reconstruction |
|-----------------------------|---------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Clinical Workflow | Select recon type and strength (Low,
Medium, High). | Same |
| Reference
protocols/dose | Using the same reference protocols
provided on the Revolution CT/Apex
platform systems for ASIR-V | Using the same reference protocols
provided on the Revolution EVO,
Revolution Maxima, Revolution
Ascend and Discovery CT750 HD
family systems for ASIR-V |
| Deployment
Environment | On CT Console | On CT Console
On GE's Edison Platform. |
The subject device Deep Learning Image Reconstruction does not introduce any additional risks/hazards, warnings, or limitations.
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 Regulations of 21CFR 820 and ISO 13485. GEHC believes that the extensive bench testing is 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
- O Code Review
- Software Unit Implementation O
- Software Integrations and Integration Testing O
- System Testing
- Safety Testing (Verification) o
- O Image Performance Testing (Verification)
- Simulating Use Testing (Validation) O
- Software Release
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Image /page/7/Picture/1 description: The image shows the GE Healthcare logo. The logo consists of the GE monogram in a purple circle on the left, followed by the words "GE HealthCare" in purple text on the right. The text is in a sans-serif font and is slightly blurred.
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/Apex platform systems.
The substantial equivalence is also based on the software documentation for a "Moderate" level of concern.
Engineering bench testing was also performed to support substantial equivalence and the product performance claims. The evaluation and analysis used the same test methodologies and acceptance criteria with the identical raw datasets obtained on GEHC's Revolution Ascend, Revolution Frontier and Discovery CT750 HD systems and then applying the Deep Learning Image Reconstruction or ASiR-V reconstruction (hence the dose (CTDIvol) is identical for both). 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, longitudinal (FWHM slice sensitivity profile)
- Noise Power Spectrum (NPS) and Standard Deviation of noise
- CT Number Uniformity
- CT Number Accuracy
- Contrast to Noise (CNR) ratio
- Artifact analysis metal objects, unintended motion, truncation ●
- Pediatric Phantom IQ Performance Evaluation
- Low Dose Lung Cancer Screening Protocol IQ Performance Evaluation
Substantial Equivalence
Deep Learning Image Reconstruction for Revolution Maxima, Revolution Ascend and Discovery CT750 HD family was developed under GEHC 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. Design verification, along with bench testing provided in this submission demonstrates that the Deep Learning Image Reconstruction (DLIR) met all of its design requirement and performance criteria. GEHC's quality system's design, verification, and risk management processes did not identify any additional 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.