Who is the manufacturer of Deep Learning Image Reconstruction?

Ge Healthcare Japan Corporation filed the 510(k) submission for Deep Learning Image Reconstruction (K193170).

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: Special).

Does Deep Learning Image Reconstruction use AI or machine learning?

Yes. Deep Learning Image Reconstruction (K193170) 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

K193170 · Ge Healthcare Japan Corporation · JAK · Dec 13, 2019 · Radiology

Device Facts

Record ID	K193170
Device Name	Deep Learning Image Reconstruction
Applicant	Ge Healthcare Japan Corporation
Product Code	JAK · Radiology
Decision Date	Dec 13, 2019
Decision	SESE
Submission Type	Special
Regulation	21 CFR 892.1750
Device Class	Class 2
Attributes	AI/ML, Pediatric

Intended Use

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

Device Story

Deep Learning Image Reconstruction (DLIR) software processes raw CT X-ray transmission data to generate cross-sectional images. It utilizes a dedicated Deep Neural Network (DNN) to produce images with appearance similar to traditional Filtered Back Projection (FBP) while maintaining performance metrics (noise, low-contrast detectability, spatial resolution, artifact suppression) of ASiR-V. Used on GE Revolution EVO CT systems; operated by radiologists/technicians. Users select reconstruction strength (Low, Medium, High) based on clinical needs. Output is DICOM-compatible 'TrueFidelity' CT images. Software integrates into existing raw data-based reconstruction chain on the Edison Platform. Images assist clinicians in diagnostic assessment by providing improved image quality, potentially aiding in clinical decision-making and patient management.

Clinical Evidence

Retrospective clinical reader study of 60 cases. 7 board-certified radiologists independently evaluated images reconstructed with ASiR-V vs. DLIR using a 5-point Likert scale for diagnostic image quality. Readers also compared image noise texture, sharpness, and homogeneity. Results supported substantial equivalence and performance claims. Bench testing included LCD (MITA/FDA phantoms), MTF, streak artifact suppression, NPS, CT number accuracy, and pediatric/lung screening performance.

Technological Characteristics

Deep Neural Network (DNN) reconstruction algorithm. Deployed on GE Edison Platform. Compatible with Revolution EVO CT systems. Supports Axial, Helical, and Cardiac acquisition modes. Software-based image processing; DICOM output. Developed under ISO 13485 and 21 CFR 820 quality systems.

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) 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 (K133640)
Revolution CT systems (K133705, K163213)
Revolution EVO (K131576)

Related Devices

K212067 — Deep Learning Image Reconstruction · Ge Healthcare Japan Corporation · Sep 17, 2021
K220961 — Deep Learning Image Reconstruction · Ge Healthcare Japan Corporation · Jul 29, 2022
K183202 — Deep Learning Image Reconstruction · Ge Medical Systems, LLC · Apr 12, 2019
K213999 — Deep Learning Image Reconstruction · Ge Medical Systems, LLC · Feb 18, 2022
K230807 — Deep Learning Image Reconstruction · Ge Healthcare Japan Corporation · Apr 20, 2023

Submission Summary (Full Text)

{0}------------------------------------------------ December 13, 2019 Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue. GE Healthcare Japan Corporation % Ms. Helen Peng Sr. Regulatory Affairs Director GE Medical Systems, LLC 3000 North Grandview Blvd. WAUKESHA WI 53188 Re: K193170 Trade/Device Name: Deep Learning Image Reconstruction Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK Dated: November 14, 2019 Received: November 15, 2019 Dear Ms. Peng: We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading. If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register. Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for {1}------------------------------------------------ devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050. Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems. For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100). Sincerely. For Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health Enclosure {2}------------------------------------------------ #### DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration ## Indications for Use Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2020 See PRA Statement below. 510(k) Number (if known) #### K193170 Device Name Deep Learning Image Reconstruction Indications for Use (Describe) The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications. | Type of Use (Select one or both, as applicable) | |-------------------------------------------------| |-------------------------------------------------| | <div> [X] Prescription Use (Part 21 CFR 801 Subpart D) </div> | <div> Over-The-Counter Use (21 CFR 801 Subpart C) </div> | |------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| |------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| 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." {3}------------------------------------------------ Image /page/3/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters 'GE' in a stylized, cursive font, enclosed within a blue circle. Three water droplets surround the circle, positioned at the top, left, and right. The logo is simple and recognizable, representing the company's brand identity. ## K193170 # 510(k) SUMMARY OF SAFETY AND EFFECTIVNESS This 510(k) summary of Safety and Effectiveness information is submitted in accordance with the requirement of 21 CFR Part 807.87(h): | Date: | November 14, 2019 | |---------------------------------|--------------------------------------------------------------------------------------------------------------------------| | Submitter: | GE Healthcare Japan Corporation 7-127, Asahigaoka, 4-chome Hino-shi, Tokyo, 191-8503, Japan | | Primary Contact: | Tomohiro Ito Sr. Regulatory Affairs Leader Phone +81-42-585-5383 or +81-90-8346-6807 Email: tomohiro.ito@ge.com | | Secondary Contacts: | Helen Peng Sr. Regulatory Affairs Director GE Healthcare Tel: 262-424-8222 Email: hong.peng@ge.com | | Device Trade Name: | Deep Learning Image Reconstruction | | Device Classification | Class II | | Regulation Number/Product Code: | 21 CFR 892.1750 Computed tomography x-ray system / JAK | | Predicate Device Information | | | Device Name: | Deep Learning Image Reconstruction | | Manufacturer: | GE Medical System SCS (d.b.a GE Healthcare) | | 510(k) Number: | K183202 cleared on April 12, 2019 | | Regulation Number/Product Code: | 21 CFR 892.1750 Computed tomography x-ray system / JAK | | Reference Device Information | | | Device Name: | ASiR-V | | Manufacturer: | GE Medical System SCS (d.b.a GE Healthcare) | | 510(k) Number: | K133640 cleared on March 25, 2014 | {4}------------------------------------------------ Image /page/4/Picture/1 description: The image shows the logo for General Electric (GE). The logo is a blue circle with the letters "GE" in a stylized font in the center. There are three water droplets surrounding the letters. The logo is simple and recognizable. ### Device Description Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression. The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction software support a normal throughput for routine CT. The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity "" CT Images". The system allows user selection of three strengths of Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preferences and experience for the specific clinical need. Deep Learning Image Reconstruction software was initially introduced on the Revolution CT systems (K133705, K163213). The DLR algorithm is now ported to Revolution EVO (K131576), which offers 64 detector row and up to 40mm collimation, and ASIR-V reconstruction option. Compared to the predicate device, the intended use and indications for use of Deep Learning Image Reconstruction are identical. #### Intended Use The Deep Learning Image Reconstruction software is intended for head, whole body, cardiac, and vascular CT scans. #### Indications for Use The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications. #### Comparisons The GE Deep Learning Image Reconstruction software for the Revolution EVO is substantially equivalent to the unmodified predicate device DLIR reconstruction for Revolution fomily of CT systems. The fundamental technology, i.e the DLIR algorithm, remains unchanged from the {5}------------------------------------------------ Image /page/5/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters 'GE' written in a stylized cursive font, enclosed within a circle. There are three water droplet-like shapes evenly spaced around the outside of the circle. The logo is colored in a light blue hue. predicate. The table below summarizes the substantive feature/technological differences between the predicate device and the proposed device: | Specification/ Attribute | Deep Learning Image Reconstruction | Deep Learning Image Reconstruction | |-----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------| | | Predicate Device, (K183202) | (Proposed Device) | | Technology | Utilizes a dedicated Deep Neural Network (DNN) which is trained for the Revolution family CT Scanners and designed specifically to generate high quality CT images | Same | | Clinical Workflow | Select recon type and strength (High, Medium, Low) | Same | | Clinical Use | Routine Clinical Use | Same | | Reference protocols/dose | Using the same Reference protocols provided on the Revolution CT system for ASiR-V | Using the same Reference protocols provided on the Revolution EVO system for ASiR-V | | IQ performance vs dose | Image noise, low contrast detectability, spatial resolution, and low signal artifact suppression as good or better than ASIR-V on Revolution CT | Image noise, low contrast detectability, spatial resolution, and low signal artifact suppression as good or better than ASIR-V on Revolution EVO. | | Deployment Environment | On CT console | On GE's Edison Platform. | Deep Learning Image Reconstruction algorithm itself does not introduce any new risks/hazards, warnings, or limitations. The changes described above do not change the fundamental control mechanism, operating principle, and do not change the intended use from the predicate device. ## Determination of Substantial Equivalence #### Summary of Non-Clinical Testing Deep Learning Image Reconstruction software has successfully completed the design control testing per our quality system. No new hazards were identified, and no unexpected test results were obtained. Deep Learning Image Reconstruction was designed under the Quality System Regulations of 21CFR 820 and ISO 13485. GE believes that the extensive software testing, IQ {6}------------------------------------------------ Image /page/6/Picture/1 description: The image shows the logo for General Electric (GE). The logo is a blue circle with the letters "GE" in a stylized font in the center. There are three water droplet shapes surrounding the circle. The logo is simple and recognizable. bench testing, and the clinical reader study evaluation are sufficient for FDA's substantial equivalence determination. The following quality assurance measures have been applied to the development of the software: - . Risk Analysis and Control - . Requirement Reviews - . Design Reviews - . Software Development Lifecycle - . Verification Testing at unit, integration and system levels - . Design Validation Resting The testing and results did not raise different questions of safety and effectiveness than associated with predicate device. We consider the proposed device is substantially equivalent to the predicate device DLIR for Revolution CT family of products. The substantial equivalence is also based on the software documentation for a "Moderate" level of concern device #### Additional Non-Clinical Testing Engineering bench testing was performed to support substantial equivalence and the product performance claims. The evaluation and analysis used the identical raw datasets obtained on GE's Revolution EVO CT System and then applies the Deep Learning Image Reconstruction (DLIR) or ASiR-V reconstruction (hence the dose (CTDIvol) is identical for both). The resultant images were then compared for: - Low Contrast Detectability (LCD) using the head and body MITA/FDA low contrast phantoms and a model observer - Low Contrast Detectability (LCD) with statistical method - Image Noise (pixel standard deviation) using both head and body uniform phantoms ■ High-Contrast Spatial Resolution (MTF) using a quality assurance phantom with a small diameter tungsten wire surrounded by water inside the phantom to generate the point spread function ▪ Streak Artifact Suppression using an oval uniform polyethylene phantom with embedded high attenuation objects to produce the artifacts - Spatial Resolution, longitudinal (FWHM slice sensitivity profile) - Noise Power Spectrum (NPS) and Standard Deviation of noise - CT Number Accuracy and Uniformity - · Contrast to Noise (CNR) ratio - Artifact analysis metal objects, unintended motion, truncation - Pediatric Image Quality Performance - Low Dose Lung Cancer Screening {7}------------------------------------------------ Image /page/7/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters 'GE' in a stylized script, enclosed within a circle. There are water droplet-like shapes surrounding the circle. The logo is blue. ## Clinical Testing The reader study used a total of 60 retrospectively collected clinal cases. The raw data from each of these cases was reconstructed with both ASiR-V and Deep Learning Image Reconstruction (DLIR) and presented to each reader independently. The results of the study support substantial equivalence and performance claims. These images were read by 7 board-certified radiologists with expertise in the specialty areas that align with the anatomical region of each case. Each image was read by 3 different radiologists who provided an assessment of image quality related to diagnostic use according to a 5-point Likert Scale. The readers completed their evaluations independently and were blinded to the results of the other readers' assessments Additionally, the readers were asked to compare directly the ASIR-V and Deep Learning Image Reconstruction (DLIR) images according to three key metrics of image quality preference – image noise texture, image sharpness, and image noise texture homogeneity. ## Substantial Equivalence The changes associated with Deep Learning Image Reconstruction (DLIR) software do not change the Indications for Use from the predicate, and represent equivalent technological characteristics, with no impact on control mechanism, operating principle, and energy type. Deep Learning Image Reconstruction (DLIR) software for Revolution EVO was developed under GE Healthcare's quality system. Design verification, validation along with bench testing and the clinical reader study demonstrate that the Deep Learning Image Reconstruction (DLIR) software is substantially equivalent and hence as safe and as effective as the legally marketed predicate device. GE's quality system's design, verification, validation and risk management processes did not identify any new hazards, unexpected results, or adverse effects stemming from the changes to the predicate. Based on development under GE Healthcare's quality system, the successful verification and validation testing, the additional engineering bench testing, and the clinical reader study, GE Healthcare believes that the Deep Learning Image Reconstruction (DLIR) Software is substantially equivalent to the unmodified predicate device and hence is safe and effective for its intended use.