(167 days)
Yes
The device description explicitly states it uses a "dedicated Convolution Neural Network (CNN)" and is marketed as "Deep Learning Image Reconstruction".
No
The device is an image reconstruction method for CT scans, not a device used for direct treatment or diagnosis. Its function is to improve the quality of diagnostic images.
No.
This device is an image reconstruction method for CT scans, improving image quality and appearance, but it does not directly perform diagnosis or provide diagnostic output. Its output (images) would then be interpreted by a medical professional for diagnostic purposes.
No
The device is described as an "image reconstruction method" that is "integrated into the scanner's existing raw data-based image reconstruction chain". While it is software, it is presented as a component or feature of a larger CT scanner system, not a standalone software-only medical device.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In vitro diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- Device Function: The Deep Learning Image Reconstruction for Gemstone Spectral Imaging is a software option for a CT scanner. Its function is to process X-ray transmission data acquired from within the body to create cross-sectional images. It does not analyze samples taken from the body.
- Intended Use: The intended use is to produce cross-sectional images for diagnostic purposes, but this is achieved through image reconstruction of data acquired in vivo, not through the analysis of in vitro samples.
Therefore, while this device is a medical device used for diagnosis, it falls under the category of imaging devices rather than in vitro diagnostics.
No
The provided text does not contain any explicit statement that the FDA has reviewed, approved, or cleared a PCCP for this specific device. The phrase "Control Plan Authorized (PCCP) and relevant text" having "Not Found" confirms the absence of such information.
Intended Use / Indications for Use
The Deep Learning Image Reconstruction for Gemstone Spectral Imaging option is a deep learning based CT reconstruction method intended to produce cross-sectional images by computer reconstruction of dual energy X-ray transmission data acquired with Gemstone Spectral Imaging, for all ages. Deep Learning Image Reconstruction for Gemstone Spectral Imaging can be used for whole body, vascular, and contrast enhanced head CT applications.
Product codes
JAK
Device Description
Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI) is the next step in CT reconstruction advancement. Like its predicate device (DLIR), DLIR-GSI is an image reconstruction method that uses a dedicated Convolution Neural Network (CNN) that has been designed and trained specifically to reconstruct CT GSI Images to give an image appearance similar to traditional FBP images while maintaining or improving the performance of ASiR-V. The DLIR-GSI can generate monochromatic images (MC), material decomposition images (MD), and virtual unenhanced images (VUE). Multiple MD images such as lodine, Water, Calcium, Hydroxyapatite (HAP), Fat, Uric Acid can be prescribed by the user and generated by the subject device. DLIR-GSI demonstrates same or better Imaging performance as compared to ASiR-V in the following areas: low contrast detectability (LCD), image noise, contrast to noise ratio (CNR), high contrast spatial resolution, CT number accuracy, MD quantification accuracy and metal artifact reduction. Reconstruction times with DLIR-GSI support a normal throughput for routine CT.
The device is marketed as Deep Learning Image Reconstruction for Gemstone Spectral Imaging and the images produced are branded as "TrueFidelity™ CT Images".
Deep Learning Image Reconstruction for Gemstone Spectral Imaging is compatible with dual energy scan modes using the standard kernel and was trained specifically on the Revolution CT family of systems (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 DLR-GSI: Low, Medium, or High. The strength selection will vary with individual users' preference for the specific clinical need.
As compared to the predicate device, the intended use of Deep Learning Image Reconstruction for Gemstone Spectral Imaging does not change (head and whole-body CT image reconstruction). Both algorithms are designed to produce cross-sectional images of the head and body by computer reconstruction of X-ray transmission data, for all ages.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Computed tomography x-ray system
Anatomical Site
Whole body, head
Indicated Patient Age Range
all ages
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
Utilizes a dedicated neural network which is trained using dual energy acquired images on the CT Scanner and designed specifically to generate high quality CT images.
Description of the test set, sample size, data source, and annotation protocol
Engineering bench testing and clinical reader study.
Bench testing used identical raw datasets obtained on GE's Revolution CT family of systems.
Clinical Testing:
A total of 40 retrospectively collected cases (typical and challenging clinical cases) were used. The raw data from each case was reconstructed with ASIR-V and DLIR-GSI.
7 additional retrospectively collected clinically challenging cases containing small, low contrast objects were evaluated.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Engineering bench testing:
The evaluation and analysis used the identical raw datasets obtained on GE's Revolution CT family of systems and then applies the Deep Learning Image Reconstruction for Gemstone Spectral Imaging or ASiR-V reconstruction. The resultant images were then compared for: Low contrast detectability (LCD), Image noise (pixel standard deviation), High contrast spatial resolution (MTF), Contrast to noise ratio (CNR), CT Number accuracy, CT Number uniformity, Material Decomposition accuracy, lodine detection, Metal artifact reduction, Pediatric test with anthropomorphic phantom. The test results did not raise different questions of safety and effectiveness than associated with the predicate device.
Clinical Testing:
Reader study used a total of 40 retrospectively collected cases. These images were read by 5 board-certified radiologists. 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. Readers were also asked to compare ASiR-V and DLIR-GSI images according to the key metric of image noise texture.
The result of this reader study confirmed that the DLIR-GSI produce diagnostic quality images and have preferred noise texture than the reference device ASiR-V.
Additionally, 7 retrospectively collected clinically challenging cases containing small, low contrast objects were evaluated. These images were read by a board-certified radiologist. The reader confirmed that all object(s) were adequately visualized for diagnostic use using DLIR-GSI. This additional clinical evaluation further confirmed that DLIR-GSI produces diagnostic quality images, even for extremely clinically challenging cases.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found
Predicate Device(s)
Reference Device(s)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.
0
December 10, 2020
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GE Medical Systems, LLC % Ms. Katelyn Rowley Regulatory Affairs Leader 3000 N. Grandview Blvd. WAUKESHA WI 53188
Re: K201745
Trade/Device Name: Deep Learning Image Reconstruction for Gemstone Spectral Imaging Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK Dated: November 13, 2020 Received: November 16, 2020
Dear Ms. Rowley:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmp/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 devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see
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Page
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combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Section 4: Indications for Use
Deep Learning Image Reconstruction for Gemstone Spectral Imaging (GSI_DLIR)
3
Indications for Use
510(k) Number (if known)
K201745
Device Name
Deep Learning Image Reconstruction for Gemstone Spectral Imaging
Indications for Use (Describe)
The Deep Learning Image Reconstruction for Gemstone Spectral Imaging option is a deep learning based CT reconstruction method intended to produce cross-sectional images by computer reconstruction of dual energy X-ray transmission data acquired with Gemstone Spectral Imaging, for all ages. Deep Learning Image Reconstruction for Gemstone Spectral Imaging can be used for whole body, vascular, and contrast enhanced head 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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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: | June 25, 2020 |
|---|---|
| Submitter: | GE Medical Systems, LLC |
| 3000 North Grandview Blvd | |
| Waukesha, WI 53188 | |
| Primary Contact: | Katelyn Rowley |
| Regulatory Affairs Leader | |
| Phone 262-309-5888 | |
| Email: Katelyn.rowely@ge.com | |
| Secondary Contacts: | Helen Peng |
| Senior Regulatory Affairs Director | |
| GE Healthcare | |
| Tel: 262-424-8222 | |
| Email: hong.peng@med.ge.com | |
| Device Trade Name: | Deep Learning Image Reconstruction for Gemstone Spectral Imaging |
| 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: | K134640 cleared on March 25, 2014 |
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Regulation Number/ 21 CFR 892.1750 Computed tomography x-ray system / JAK Product Code:
Reference Devices Information
| Device Name: | Revolution CT |
|---|---|
| Manufacturer: | GE Medical Systems, LLC |
| 510(k) Number: | K163213 cleared on December 16, 2016 |
| Regulation Number/ | |
| Product Code: | 21 CFR 892.1750 Computed tomography x-ray system / JAK |
Device Description
Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI) is the next step in CT reconstruction advancement. Like its predicate device (DLIR), DLIR-GSI is an image reconstruction method that uses a dedicated Convolution Neural Network (CNN) that has been designed and trained specifically to reconstruct CT GSI Images to give an image appearance similar to traditional FBP images while maintaining or improving the performance of ASiR-V. The DLIR-GSI can generate monochromatic images (MC), material decomposition images (MD), and virtual unenhanced images (VUE). Multiple MD images such as lodine, Water, Calcium, Hydroxyapatite (HAP), Fat, Uric Acid can be prescribed by the user and generated by the subject device. DLIR-GSI demonstrates same or better Imaging performance as compared to ASiR-V in the following areas: low contrast detectability (LCD), image noise, contrast to noise ratio (CNR), high contrast spatial resolution, CT number accuracy, MD quantification accuracy and metal artifact reduction. Reconstruction times with DLIR-GSI support a normal throughput for routine CT.
The device is marketed as Deep Learning Image Reconstruction for Gemstone Spectral Imaging and the images produced are branded as "TrueFidelity™ CT Images".
Deep Learning Image Reconstruction for Gemstone Spectral Imaging is compatible with dual energy scan modes using the standard kernel and was trained specifically on the Revolution CT family of systems (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 DLR-GSI: Low, Medium, or High. The strength selection will vary with individual users' preference for the specific clinical need.
As compared to the predicate device, the intended use of Deep Learning Image Reconstruction for Gemstone Spectral Imaging does not change (head and whole-body CT image reconstruction). Both algorithms are designed to produce cross-sectional images of the head and body by computer reconstruction of X-ray transmission data, for all ages.
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Intended Use
The Deep Learning Image Reconstruction for Gemstone Spectral Imaging option is intended for whole body, vascular, and contrast enhanced head CT applications.
Indications for Use
The Deep Learning Image Reconstruction for Gemstone Spectral Imaging option is a deep learning based CT reconstruction method intended to produce cross-sectional images by computer reconstruction of dual energy X-ray transmission data acquired with Gemstone Spectral Imaging, for all ages. Deep Learning Image Reconstruction for Gemstone Spectral Imaging can be used for whole body, vascular, and contrast enhanced head CT applications.
Comparisons
The Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI) employs the same fundamental technology as that of the predicate device. In fact, the DLIR-GSI deep learning neural network was modified from that of the predicate. The DLIR-GSI option is substantially equivalent to the predicate device, DLIR reconstruction option. Because both the DLIR-GSI and the predicate device DLIR reconstruction for single energy are implemented on reference device, the Revolution CT family of Systems with GSI (K163213) they utilize the same hardware and software platform technology on which substantial equivalency is demonstrated. The table below summarizes the substantive feature/technological similarities and differences between the predicate device and the proposed device:
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GE Healthcare 510(k) Premarket Notification Submission – DLIR-GSI
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| Specification/ Attribute | Deep Learning Image
Reconstruction
(Predicate Device, K183202) | Deep Learning Image
Reconstruction for Gemstone
Spectral Imaging
(Proposed Device) |
|-------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Compatible Image Types | Compatible with single energy scan
modes and standard kernel. | Compatible with dual energy
scan modes and standard kernel. |
| IQ performance vs dose | Low contrast detectability (LCD),
image noise, contrast to noise ratio
(CNR), and high contrast spatial
resolution are as good or better
than ASiR-V when substituted using
raw data from the same scan. | Same |
| Technology | Utilizes a dedicated neural network
which is trained using single energy
acquired images on the CT Scanner
and designed specifically to
generate high quality CT images. | Utilizes a dedicated neural
network which is trained using
dual energy acquired images on
the CT Scanner and designed
specifically to generate high
quality CT images. |
| 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 | Utilizes a trained neural network
which models the scanned object
using information obtained from
extensive phantom and clinical
data. | Same |
| Clinical Workflow | Select recon type and strength
(Low, Medium, High). | Same |
Deep Learning Image Reconstruction for Gemstone Spectral imaging does not introduce any additional risks/hazards, warnings, or limitations.
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Image /page/8/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. The letters are white, creating a contrast against the blue background. The logo is simple, recognizable, and represents the multinational conglomerate corporation.
Determination of Substantial Equivalence
Summary of Non-Clinical Testing
Deep Learning Image Reconstruction for Gemstone Spectral Imaging 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 for Gemstone Spectral Imaging 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
- Technical Design Reviews
- . Formal Design Reviews
- . Code Review
- Software Unit Implementation
- Software Integrations and Integration Testing
- System Testing
- o Safety Testing (Verification)
- o Image Performance Testing (Verification)
- 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.
The substantial equivalence is also based on the software documentation for a "Moderate" level of concern.
Additional Non-Clinical Testing
Engineering bench testing was performed to support substantial equivalence and the product performance claims. The evaluation and analysis used the identical raw datasets obtained on GE's Revolution CT family of systems and then applies the Deep Learning Image Reconstruction for Gemstone Spectral Imaging or ASiR-V reconstruction. The resultant images were then compared for:
- Low contrast detectability (LCD)
- . Image noise (pixel standard deviation)
- . High contrast spatial resolution (MTF)
- Contrast to noise ratio (CNR)
- CT Number accuracy
- I CT Number uniformity
- . Material Decomposition accuracy
- 트 lodine detection
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- . Metal artifact reduction
- . Pediatric test with anthropomorphic phantom
Clinical Testing
The reader study used a total of 40 retrospectively collected cases which represent typical and challenging clinical cases. The raw data from each of these cases was reconstructed with both ASIR-V and Deep Learning Image Reconstruction for Gemstone Spectral Imaging and presented to each reader independently. The results of the study support substantial equivalence and performance claims.
These images were read by 5 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. Three readers read the cases primarily covering body and extremity anatomy, three readers read the cases primarily covering contrast enhanced head/neck anatomy. One reader was qualified to read both body and head/neck anatomy.
Additionally, the readers were asked to compare directly the ASiR-V and Deep Learning Image Reconstruction for Gemstone Spectral Imaging images according to the key metric of image noise texture.
The result of this reader study confirmed that the DLIR-GSI (the subject device) produce diagnostic quality images and have preferred noise texture than the reference device ASiR-V.
To further challenge the DLR-GSI reconstruction algorithm, 7 additional retrospectively collected clinically challenging cases containing small, low contrast objects were evaluated. These images were read by a board-certified radiologist with expertise in the specialty area that aligns with all cases. The reader confirmed that all object(s) were adequately visualized for diagnostic use using DLIR-GSI. This additional clinical evaluation further confirmed that DLIR-GS! produces diagnostic quality images, even for extremely clinically challenging cases.
Substantial Equivalence
The changes associated with Deep Learning Image Reconstruction for Gemstone Spectral Imaging do not change the Intended 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 for Gemstone Spectral Imaging 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 Deep Learning Image Reconstruction for Gemstone Spectral Imaging 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.
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Based on development under GE Healthcare's quality system, the successful verification and validation testing, including the additional engineering bench testing, and the clinical reader study, GE Healthcare believes that Deep Learning Image Reconstruction for Gemstone Spectral Imaging is substantially equivalent to the predicate device and hence is safe and effective for its intended use.