Who is the manufacturer of Deep Learning Image Reconstruction?

Ge Medical Systems, LLC filed the 510(k) submission for Deep Learning Image Reconstruction (K213999).

What is the FDA product code for Deep Learning Image Reconstruction?

JAK. It is classified under 21 CFR 892.1750 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for Deep Learning Image Reconstruction?

510(k) clearance (submission type: Traditional).

Does Deep Learning Image Reconstruction use AI or machine learning?

Yes. Deep Learning Image Reconstruction (K213999) is flagged as an AI/ML-enabled medical device in the FDA's AI/ML device list.

What are the predicate devices for Deep Learning Image Reconstruction?

Deep Learning Image Reconstruction (K183202).

Who can help prepare an FDA 510(k) submission like Deep Learning Image Reconstruction?

Innolitics — a medical-device software consultancy — provides regulatory and engineering support for FDA 510(k) submissions. See https://innolitics.com/services/510ks/ or contact https://innolitics.com/contact.

Can an AI/ML device like Deep Learning Image Reconstruction get a guaranteed FDA 510(k) clearance?

Yes. Innolitics offers a guaranteed FDA 510(k) clearance engagement for AI/ML Software as a Medical Device — combining engineering and regulatory execution under one agreement with a guaranteed clearance outcome. Eligibility: the device must be an AI/ML Software as a Medical Device (SaMD), and its performance must meet the criteria Innolitics determines during an initial regulatory strategy engagement to be necessary for FDA clearance. See https://innolitics.com/services/end-to-end-samd/ or contact https://innolitics.com/contact.

Deep Learning Image Reconstruction

K213999 · Ge Medical Systems, LLC · JAK · Feb 18, 2022 · Radiology

Device Facts

Record ID	K213999
Device Name	Deep Learning Image Reconstruction
Applicant	Ge Medical Systems, LLC
Product Code	JAK · Radiology
Decision Date	Feb 18, 2022
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.1750
Device Class	Class 2
Attributes	AI/ML, Pediatric

Intended Use

The Deep Learning Image Reconstruction option is intended for head, whole body, cardiac, and vascular CT scans.

Device Story

Deep Learning Image Reconstruction (DLIR) is a software-based reconstruction method for CT scanners. It processes raw X-ray transmission data to generate cross-sectional images branded as 'TrueFidelity™ CT Images'. The device utilizes a dedicated Deep Neural Network (DNN) trained to model noise propagation and identify/remove noise, producing images with appearance similar to traditional Filtered Back Projection (FBP) while maintaining ASiR-V performance metrics (noise, low-contrast detectability, spatial resolution, artifact suppression). Used in clinical settings by radiologists; the system allows user selection of three reconstruction strengths (Low, Medium, High). The output is viewed by radiologists to assist in diagnostic assessment. By improving image quality and noise texture compared to traditional iterative reconstruction (ASiR-V), the device aids clinical decision-making and potentially benefits patients through enhanced diagnostic clarity.

Clinical Evidence

Clinical reader study using 40 retrospectively collected clinical cases. Images reconstructed with ASiR-V and DLIR were evaluated by 6 board-certified radiologists (3 neuro, 3 body/extremity) using a 5-point Likert scale. Results confirmed DLIR produces diagnostic quality images with significantly better subjective image quality (noise texture, sharpness, homogeneity) compared to ASiR-V.

Technological Characteristics

Software-based deep learning reconstruction. Utilizes a dedicated Deep Neural Network (DNN) integrated into the CT raw data reconstruction chain. Operates on GE Revolution CT/Apex platforms. Connectivity: DICOM compatible. Software level of concern: Moderate. Developed under ISO 13485 and 21 CFR 820.

Indications for Use

Indicated for cross-sectional imaging of head and whole body via computer reconstruction of X-ray transmission data (Axial, Helical/Volumetric, Cardiac acquisitions) for all ages. Applicable for head, whole body, cardiac, and vascular CT applications.

Regulatory Classification

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.

Predicate Devices

Deep Learning Image Reconstruction (K183202)

Reference Devices

ASiR-V (K134640)
Revolution CT/Apex platform (K133705, K163213, K19177)

Related Devices

K230807 — Deep Learning Image Reconstruction · Ge Healthcare Japan Corporation · Apr 20, 2023
K183202 — Deep Learning Image Reconstruction · Ge Medical Systems, LLC · Apr 12, 2019
K193170 — Deep Learning Image Reconstruction · Ge Healthcare Japan Corporation · Dec 13, 2019
K212067 — Deep Learning Image Reconstruction · Ge Healthcare Japan Corporation · Sep 17, 2021
K220961 — Deep Learning Image Reconstruction · Ge Healthcare Japan Corporation · Jul 29, 2022

Submission Summary (Full Text)

{0}------------------------------------------------ Image /page/0/Picture/0 description: The image shows the logos of the Department of Health & Human Services and the Food and Drug Administration (FDA). The Department of Health & Human Services logo is on the left, and the FDA logo is on the right. The FDA logo is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue. 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 {2}------------------------------------------------ Image /page/2/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 three white swirls around the outside of the circle. # K213999 Section 4: Indications for Use Statement Deep Learning Image Reconstruction (DLIR) {3}------------------------------------------------ # Indications for Use 510(k) Number (if known) K213999 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) | #### CONTINUE ON A SEPARATE PAGE IF NEEDED. This section applies only to requirements of the Paperwork Reduction Act of 1995. #### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.* The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to: > Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff@fda.hhs.gov "An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number." {4}------------------------------------------------ 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 | {5}------------------------------------------------ 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. {6}------------------------------------------------ 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. {7}------------------------------------------------ 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 {8}------------------------------------------------ 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.