K183204 · GE Medical Systems SCS · JAK · Apr 8, 2019 · Radiology
Device Facts
Record ID
K183204
Device Name
Bone VCAR (BVCAR)
Applicant
GE Medical Systems SCS
Product Code
JAK · Radiology
Decision Date
Apr 8, 2019
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.1750
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Spine labeling
Deep learning technique
—
> 90% success rate
Dataset of CT exams representative of clinical scenarios
—
Representative set of clinical sample images
3 (board certified radiologists)
Indications for Use
Bone VCAR is a non-invasive image analysis software package which may be used in conjunction with CT images to aid in the assessment and reporting efficiency of images that include the spine.
Device Story
Bone VCAR is a post-processing software application for CT images, used on Advantage Workstation, CT scanners, or PACS. It utilizes a deep learning algorithm to automatically detect and label vertebrae in 3D space. Input consists of CT scan acquisitions (single energy or Gemstone Spectral Imaging). The software provides multiplanar reconstruction (MPR) views (axial, sagittal, coronal, oblique, x-section, curved) and various rendering modes (MIP, MinIP, VR). Clinicians use the tool to visualize anatomy, perform measurements (distance, area, Hounsfield units), and annotate images. By automating spine labeling and optimizing display settings, the device improves reading efficiency and assists in report dictation. It is intended for use by radiologists and clinicians in various care areas, including trauma and oncology.
Clinical Evidence
Bench testing and clinical assessment performed. Algorithm validation using a representative dataset of CT exams achieved >90% success rate for automated spine labeling. Clinical assessment by three board-certified radiologists using a 5-point Likert scale demonstrated that Bone VCAR's automated labeling is faster than manual labeling and increases reading/reporting efficiency while maintaining accurate identification.
Technological Characteristics
Software-based post-processing application. Utilizes deep learning for automated vertebra detection and labeling. Compatible with single energy and Gemstone Spectral Imaging (GSI) CT acquisitions. Features include MPR, MIP, MinIP, and VR display modes. Operates on Advantage Workstation, CT scanners, or PACS. Developed under ISO 13485 and 21 CFR 820 quality systems.
Indications for Use
Indicated for clinicians reviewing CT images including the spine. Supports visualization and identification of vertebrae to assist in report dictation and optimized display for fast image review. Applicable to trauma, oncology, and routine body exams; no specific disease state contraindications.
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.
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April 8, 2019
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GE Medical Systems SCS % Ms. Elizabeth Mathew Senior Regulatory Affairs Manager 283 rue de la Miniere Buc. 78530 FRANCE
Re: K183204
Trade/Device Name: Bone VCAR (BVCAR) Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK, LLZ Dated: March 18, 2019 Received: March 19, 2019
Dear Elizabeth Mathew:
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
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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/CombinationProducts/GuidanceRegulatoryInformation/ucm597488.html); 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 http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm.
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/DeviceRegulationandGuidance/) and CDRH Learn (http://www.fda.gov/Training/CDRHLearn). 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 (http://www.fda.gov/DICE) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
Michael D'Hara
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health
Enclosure
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## Indications for Use
510(k) Number (if known) K183204
Device Name
Bone VCAR
Indications for Use (Describe)
Bone VCAR is a post processing application for use in the analysis of CT images. The software is intended to support clinicians in the review of images that include the spine by providing tools to label the spine and optimize the display of anatomy within the CT image.
Bone VCAR is designed to support the clinician in visualization of the spine, by providing initial identification of vertebrae to assist in report dictation.
The software also assists the user by providing optimized display settings for easier identification of anatomy to facilitate fast image review and reporting of findings. Bone VCAR may be used for multiple care areas and is not specific to any disease state. It can be utilized during the review of exams including trauma, oncology, and routine body.
| Type of Use (Select one or both, as applicable) | |
|-----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
| <span style="font-family: Arial, sans-serif;">☑</span> Prescription Use (Part 21 CFR 801 Subpart D) | <span style="font-family: Arial, sans-serif;">☐</span> Over-The-Counter Use (21 CFR 801 Subpart C) |
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Image /page/3/Picture/0 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. The circle is surrounded by a decorative pattern of swirls and dots. The logo is simple and recognizable, and it is associated with a well-known and established company.
GE Healthcare 510(k) Premarket Notification Submission
### 510(k) Summary
K183204
In accordance with 21 CFR 807.92 the following summary of information is provided:
| Date: | April 8, 2019 |
|-------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Submitter: | GE Medical Systems SCS<br>Establishment Registration Number - 9611343<br>283 rue de la Miniere<br>78530 Buc, France |
| Primary Contact<br>Person: | Elizabeth Mathew<br>Senior Regulatory Affairs Manager<br>Phone: (262) 424-7774<br>Email: Elizabeth.Mathew@ge.com |
| Secondary Contact<br>Person: | Helen Peng<br>Regulatory Affairs Director<br>GE Healthcare, (GE Medical Systems, LLC)<br>3000 N Grandview Blvd.,<br>Waukesha, WI - 53188<br>Phone: 262-424-8222<br>Email: Hong.Peng@ge.com |
| Proposed Device: | |
| Device Name: | Bone VCAR |
| Common/Usual<br>Name: | BVCAR |
| Primary Regulation<br>number: | 21 CFR 892.1750 Computed tomography x-ray system |
| Primary Product<br>Code: | JAK |
| Secondary Product<br>Code: | LLZ |
| Classification: | Class II |
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Predicate Device:
| Device Name: | Syngo.CT Bone Reading |
|-------------------------------------|-----------------------------------------------------------|
| 510(k) number: | K123584 cleared on May 12, 2013 |
| Regulation number/<br>Product Code: | 21 CFR 892.1750 Computed tomography x-ray system /<br>JAK |
| Classification: | Class II |
| Manufacturer: | Siemens Medical Solutions, USA |
#### Device Description/
Technology:
Bone VCAR is a software analysis package utilizing a deep learning technique that assists in the analysis and visualization of CT data. It is intended to provide clinicians with an optimized display and quick access to tools that improve the reading experience and efficiency for anatomy. Bone VCAR is a post processing application option for the Advantage Workstation (AW) platform, CT Scanner, Cloud or PACS stations which can be used in the analysis of CT images.
Bone VCAR is designed to support the clinician in easy visualization of spine and to provide identification of those structures to assist in report dictation. This post processing solution combines the following tools and functionality:
· Multiplanar Reconstruction (MPR) displays of axial, sagittal, coronal, oblique, x-section and curved views which can be displayed in thin/thick, Average, Maximum Intensity Projection (MIP), Minimum intensity Projection (MinIP), Volume Rendering (VR) modes
· Display of anatomical labels automatically with editing capability of all labels such as the spine.
• Display of curved views automatically and editing capability of curved views through anatomical regions such as the spine for enhanced display options
· Access to all standard volume viewer tools for measuring distances, areas, Hounsfield unit values and annotating within the images
· Synchronization of views when multiple series are loaded with spine labeling for all series loaded.
The software will assist the user by providing optimized display settings to enable fast review of the images along with easy identification of anatomy to ease reporting of findings. Bone VCAR may be used for multiple care areas and is not specific to any disease state. It can be utilized during the review of various types of exams including trauma, oncology, and routine body.
Bone VCAR is compatible with both single energy and Gemstone Spectral Imaging (GSI) acquisition methods.
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# GE Healthcare 510(k) Premarket Notification Submission
| The labeling tool can be activated and deactivated by the user at any time during<br>the image review. Multiple volumes of data can be processed with the Bone<br>VCAR tool at the same time. Volumes can be from the same or different exams.<br>All labels can be moved, edited, hidden or deleted. | | |
|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| The dedicated display layout should assist in easy review of the anatomy. Users<br>have the flexibility for using all or a subset of the features within this application<br>as they find suitable. | | |
| <b>Intended Use:</b> | Bone VCAR is a non-invasive image analysis software package which may be<br>used in conjunction with CT images to aid in the assessment and reporting<br>efficiency of images that include the spine. | |
| <b>Indication for Use:</b> | Bone VCAR is a post processing application for use in the analysis of CT images.<br>The software is intended to support clinicians in the review of images that include<br>the spine by providing tools to label the spine and optimize the display of<br>anatomy within the CT image. | |
| | Bone VCAR is designed to support the clinician in visualization of the spine, by<br>providing initial identification of vertebrae to assist in report dictation. | |
| | The software also assists the user by providing optimized display settings for<br>easier identification of anatomy to facilitate fast image review and reporting of<br>findings. Bone VCAR may be used for multiple care areas and is not specific to<br>any disease state. It can be utilized during the review of various types of exams<br>including trauma, oncology, and routine body. | |
| <b>Technological<br/>Characteristic:</b> | The goal of the Bone VCAR software algorithm is to extract from a CT scan the<br>positions and labels of the patient vertebrae in the image. The algorithm takes as<br>input a CT scan acquisition and is compatible with a wide range of acquisitions<br>for dedicated spine work, trauma, oncology and routine exam. Bone VCAR<br>automated vertebra detection algorithm returns the list of detected vertebrae<br>position in 3D coordinates and associated labels. | |
| <b>Comparison:</b> | The table below summarizes the feature/technological comparison between the<br>predicate device and the proposed device: | |
| Specification | Syngo CT Bone Reading<br>(K123584) | Proposed Device:<br>Bone VCAR |
| Spine Labeling<br>Tool | Automated labeling with<br>manual editing capability | Automated labeling with<br>manual editing capability |
| Display of<br>curved spine<br>structures | Yes | Yes |
| Measurement<br>Tool | geometric<br>measurement<br>tools (distance line,<br>polyline, marker, arrow,<br>angle), HU measurement<br>tools (Pixel lens, ROI<br>circle, ROI polygonal, ROI<br>freehand, VOI sphere) | Access to all standard<br>Volume Viewer tools for<br>measuring distances, areas,<br>Hounsfield unit values and<br>annotating within the images |
| Image Display<br>formats | Multiplanar reconstruction<br>(MPR) thin/thick,<br>maximum intensity<br>projection (MIP) thin/thick,<br>inverted MIP thin/thick,<br>volume rendering technique<br>(VRT) | Multiplanar Reconstruction<br>(MPR) displays of axial,<br>sagittal, coronal, oblique, x-<br>section and curved views<br>which can be displayed in<br>thin/thick, Average,<br>Maximum Intensity<br>Projection (MIP), Minimum<br>intensity Projection (MinIP),<br>Volume Rendering (VR)<br>modes. |
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## GE Healthcare 510(k) Premarket Notification Submission
Determination of Substantial Equivalence:
Engineering has validated Bone VCAR algorithm's capability of using deep learning technique to automatically label spine using a dataset of CT exams. This database of exams is considered as a representative of the clinical scenarios where Bone VCAR is intended to be used, with consideration of acquisition parameters, image quality, pathologies and anatomy variations. The result of the algorithm validation provided a success rate greater than 90% for the capability of automatically labeling the spine.
A representative set of clinical sample images was assessed by three board certified radiologists using 5-point Likert scale. The assessment demonstrated that the capability of automatic labeling of spine by Bone VCAR is faster than
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Image /page/7/Picture/0 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. The circle has a decorative border with swirling lines. The logo is simple and recognizable, and it is associated with a well-known company.
manually labeling and it also increases reading and reporting efficiency whilst providing accurate identification of vertebra.
Bone VCAR has successfully completed the required design control testing per GE's quality system. Bone VCAR was designed and will be manufactured under the Quality System Regulations of 21CFR 820 and ISO 13485. The following quality assurance measures have been applied to the development of the device:
- Risk Analysis
- Requirements Reviews
- . Design Reviews
- Performance testing (Verification, Validation)
- Safety testing (Verification)
The substantial equivalence was also based on software documentation for a "Moderate" level of concern device.
#### Conclusion:
GE Healthcare considers Bone VCAR to be as safe, as effective, and performance is substantially equivalent to the predicate device.
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.