(59 days)
Yes
The device description explicitly states that it uses a "Deep Neural Network (DNN)" and is a "deep learning based reconstruction method," which are forms of AI/ML.
No.
Explanation: The device is an image reconstruction method for CT scans, improving image quality and speed. It does not directly provide therapy or treatment to a patient.
No.
The device is an image reconstruction method that processes X-ray transmission data to produce CT images. It does not directly perform the diagnostic function itself but generates images used by clinicians for diagnosis.
No
The device is described as an image reconstruction method integrated into a CT scanner's existing raw data-based image reconstruction chain. While it is a software algorithm, it is explicitly stated to be part of a larger hardware system (Revolution family CT systems) and its function is directly tied to processing data from that hardware. It is not a standalone software application.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices intended to be used in vitro for the examination of specimens, including blood and tissue samples, derived from the human body, solely or principally for the purpose of providing information concerning a physiological or pathological state, or a congenital abnormality, or to monitor therapeutic measures.
- Device Function: The described device, "Deep Learning Image Reconstruction," is a software option for a CT scanner. Its purpose is to process X-ray transmission data acquired from within the patient's body to create cross-sectional images. It does not analyze biological specimens in vitro.
- Intended Use: The intended use clearly states it's for producing cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data. This is a function of an imaging device, not an IVD.
The device is an image reconstruction method that enhances the quality of images produced by a CT scanner, which is an in vivo imaging modality.
No
The input letter does not contain any explicit statement that the FDA has reviewed, approved, or cleared a PCCP for this specific device. The relevant section states "Not Found", indicating no mention of PCCP authorization.
Intended Use / Indications for Use
The Deep Learning Image Reconstruction option 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 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 support a normal throughput for routine CT.
Deep Learning Image Reconstruction was trained specifically on the Revolution CT/Apex platform (K163213, K133705, K19177). 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.
The DLR algorithm is now being modified on the Revolution family CT systems (K133705, K163213, K19177) for improved reconstruction speed and image quality, thus triggering this premarket notification.
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, cardiac, and vascular
Indicated Patient Age Range
all ages
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
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.
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.
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)
Non-Clinical Testing:
Engineering bench testing was 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 GE's Revolution CT/Apex platform 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
Clinical Testing:
The reader study used a total of 40 retrospectively collected clinical cases. The raw data from each of these cases was reconstructed with both ASiR-V and Deep Learning Image Reconstruction and presented side by side to each reader independently. The results of the study support substantial equivalence and performance claims.
These images were read by 6 board certified radiologists with expecialty 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. Three readers read the cases primarily covering body and extremity anatomy, and three different read the cases primarily covering neuro anatomy.
Additionally, the readers were asked to compare directly the ASIR-V and Deep Learning Image Reconstruction images according to three key metrics of image quality preference – image noise texture, image sharpness, and image noise texture homogeneity.
The result of this reader study confirmed that the DLIR (the subject device) produce diagnostic quality images and have significantly better subjective image quality than the corresponding images generated with the ASiR-V reconstruction algorithm.
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
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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February 18, 2022
GE Medical Systems, LLC. % Amy Yang Regulatory Affairs Manager 3000 N. Grandview Blvd. WAUKESHA WI 53188
Re: K213999
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: December 20, 2021 Received: December 21, 2021
Dear Amy Yang:
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 Assistant Director Diagnostic X-ray Systems Team 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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K213999
Section 4: Indications for Use Statement
Deep Learning Image Reconstruction (DLIR)
3
Indications for Use
510(k) Number (if known)
Device Name Deep Learning Image Reconstruction
Indications for Use (Describe)
The Deep Learning Image Reconstruction option 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 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/4/Picture/1 description: The image shows the logo for General Electric (GE). The logo consists of the letters "GE" in a stylized script, enclosed within a circle. The circle is surrounded by a series of curved lines, resembling water droplets or waves. The logo is colored in blue.
510(k) SUMMARY OF SAFETY AND EFFECTIVNESS K213999
This 510(k) summary of Safety and Effectiveness information is submitted in accordance with the requirement of 21 CFR Part 807.92:
| Date: | December 20, 2021 |
|---|---|
| Submitter: | GE Medical Systems, LLC |
| 3000 North Grandview Blvd | |
| Waukesha, WI 53188 | |
| Primary Contact: | Amy Yang |
| Regulatory Affairs Manager | |
| GE Healthcare | |
| Phone: 1-414-514-3904 | |
| Email: amy.yang@ge.com | |
| Secondary Contacts: | Helen Peng |
| Senior Regulatory Affairs Director | |
| GE Healthcare | |
| Phone: 1-262-424-8222 | |
| Email: hong.peng@med.ge.com |
Nikolina Mavrodieva
Regulatory Affairs Leader
GE Healthcare
Phone: 1-905-302-3913
Email: nikolina.mavrodieva@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: | K183202 cleared on April 12, 2019 |
| Regulation Number/
Product Code: | 21 CFR 892.1750 Computed tomography x-ray system / JAK |
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Image /page/5/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. The circle is surrounded by what appear to be stylized water droplets or splashes, giving the logo a dynamic and fluid appearance. The color of the logo is a bright, solid blue.
| Reference Devices Information | |
|---|---|
| Device Name: | ASiR-V |
| Manufacturer: | GE Medical Systems, LLC |
| 510(k) Number: | K134640 cleared on March 25, 2014 |
| Regulation Number/ | |
| Product Code: | 21 CFR 892.1750 Computed tomography x-ray system / JAK |
. Devices Inform Dofa
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.
Deep Learning Image Reconstruction was trained specifically on the Revolution CT/Apex platform (K163213, K133705, K19177). 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.
The DLR algorithm is now being modified on the Revolution family CT systems (K133705, K163213, K19177) for improved reconstruction speed and image quality, 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 option is intended for head, whole body, cardiac, and vascular CT scans.
Indications for Use
The Deep Learning Image Reconstruction option 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 can be used for head, whole body, cardiac, and vascular CT applications.
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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' in a stylized, cursive font, enclosed within a blue circle. There are decorative swirls or flourishes surrounding the letters within the circle, giving the logo a classic and recognizable appearance. The logo is simple, yet distinctive, and is widely recognized as the symbol of the General Electric company.
Comparisons
The modified Deep Learning Image Reconstruction (DLIR) option is substantially equivalent to the predicate device K183202. The modified DLIR is based on the same fundamental technology as the predicate device and is implemented on the Revolution CT/Apex platform (K133705, K163213, K19177). They utilize the same hardware and software platform technology on which substantial equivalence is demonstrated. The table below summarizes the substantive feature/technological similarities and differences between the predicate device and the proposed device:
| Specification/ Attribute | Predicate Device
DLIR for Revolution CT (K183202) | Proposed Device
Modified DLIR for
Revolution CT/Apex
platform |
|-------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|
| 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 DNN technology with
revised network
architecture with retraining
and inferencing techniques |
| 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 |
| Clinical Workflow | Select recon type and strength (Low,
Medium, High). | Same |
The subject device Deep Learning Image Reconstruction does not introduce any additional risks/hazards, warnings, or limitations.
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Image /page/7/Picture/1 description: The image shows the logo for General Electric (GE). The logo consists of the letters "GE" in a stylized script, enclosed within a blue circle. The circle has a design of swirling lines around the letters, giving the impression of movement or energy.
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. 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
- O Software Unit Implementation
- Software Integrations and Integration Testing O
- System Testing
- o Safety Testing (Verification)
- O Image Performance Testing (Verification)
- 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 same test methodologies and acceptance criteria with the identical raw datasets obtained on GE's Revolution CT/Apex platform 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
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Image /page/8/Picture/1 description: The image shows the logo for General Electric (GE). The logo consists of the letters 'GE' in a stylized script, enclosed within a blue circle. There are also some white swirls around the circle, giving it a dynamic and fluid appearance.
- 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
Clinical Testing
The reader study used a total of 40 retrospectively collected clinical cases. The raw data from each of these cases was reconstructed with both ASiR-V and Deep Learning Image Reconstruction and presented side by side to each reader independently. The results of the study support substantial equivalence and performance claims.
These images were read by 6 board certified radiologists with expecialty 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. Three readers read the cases primarily covering body and extremity anatomy, and three different read the cases primarily covering neuro anatomy.
Additionally, the readers were asked to compare directly the ASIR-V and Deep Learning Image Reconstruction images according to three key metrics of image quality preference – image noise texture, image sharpness, and image noise texture homogeneity.
The result of this reader study confirmed that the DLIR (the subject device) produce diagnostic quality images and have significantly better subjective image quality than the corresponding images generated with the ASiR-V reconstruction algorithm.
Substantial Equivalence
The changes associated with Deep Learning Image Reconstruction do not change the Intended Use or indications for use from the predicate, and represent equivalent technological characteristics including the dedicated neural network, with no impact on control mechanism or operating principle.
Deep Learning Image Reconstruction was developed under GE Healthcare's quality system. Design verification, along with bench testing and the clinical reader study provided in this submission demonstrates that modified Deep Learning Image Reconstruction 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 additional hazards, unexpected results, or adverse effects stemming from the changes to the predicate.
GE Healthcare believes that Deep Learning Image Reconstruction is substantially equivalent to the predicate device and hence is safe and effective for its intended use.