(179 days)
Not Found
Yes
The summary explicitly mentions "AI-Tomo" and describes clinical validation performed on this component, indicating the use of Artificial Intelligence.
No
The device is intended to enable segmentation and overlay of 3D CT datasets with fluoroscopic images to support catheter/device navigation, and to assist in the guidance of endobronchial tools. It provides information to aid in a procedure but does not directly treat a condition or restore a function.
No
Explanation: The device is intended to assist in tool/catheter navigation during pulmonary procedures, not to diagnose a condition. It uses previously acquired diagnostic images (3D CT datasets) but does not itself provide a diagnosis.
No
The device description explicitly mentions a "main unit" and a "tablet" as components of the system, indicating hardware is included.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens from the human body. The definition of an IVD involves testing samples like blood, urine, tissue, etc., outside of the body to provide information about a person's health.
- This device operates on medical images. The LungVision System processes previously acquired CT scans and live fluoroscopic images. It does not analyze biological specimens.
- The intended use is for image guidance during procedures. The system is designed to assist physicians in navigating tools within the lungs based on imaging data, not to diagnose or monitor a condition by analyzing biological samples.
The device is clearly a medical imaging software/system used for image processing and guidance during pulmonary procedures. This falls under the category of medical devices, but not specifically In Vitro Diagnostics.
No
The input explicitly states "Control Plan Authorized (PCCP) and relevant text: Not Found", which means the letter does not contain the required explicit approval or clearance language for a PCCP.
Intended Use / Indications for Use
The LungVision System is intended to enable users to segment previously acquired 3D CT datasets and overlay and register these 3D segmented data sets with fluoroscopic live X-ray images of the same anatomy in order to support catheter/device navigation during pulmonary procedures.
Product codes
QIH
Device Description
The Lung Vision System is designed to enable users to segment previously acquired 3D CT datasets and overlay and register these 3D segmented data sets with live X-ray images of the same anatomy in order to support catheter/device navigation during pulmonary procedures.
The System is intended to assist the guidance of endobronchial tools to areas of interest inside a patient's lungs. The System allows the user to mark lesion locations and pathways to marked lesions using a patient's CT scan. During the endoscopic procedure, the System overlays planning information on real-time fluoroscopic images to guide endobronchial tool navigation. The System also provides tomographic images for lesion identification, as well as 3D views for understanding tool and lesion proximity and orientation. The System is designed to be integrated with fluoroscopic imaging systems and external displays.
The Lung Vision system includes a main unit and a tablet. Image processing algorithms are executed on the main unit and the tablet is used as a primary method of interacting with the system.
System Components Overview
The following is a list of the LungVision System's main components: LungVision Main Unit LungVision Tablet LungVision Router LungVision Board (passive device) LungVision Software
Mentions image processing
Yes
Mentions AI, DNN, or ML
- AI-driven intraoperative tomographic imaging
- We find that this reflects the functionality of AI TOMO feature. The system receives CABT O output and transform them into tomographic imaging.
- For AI-Tomo the clinical validation was performed by physicians with data that was recorded from historical LungVision clinical cases.
- The clinical validation of AI Tomo confirmed that lesion marking accuracy was comparable when using CABT and AI Tomo images.
- The images are CABTs collected from real cases performed by physicians using the LungVision system in the USA, Italy, and Israel. The CABTs are used as input data to the AI-Tomo post processing, and the output is evaluated using the ground truthing methodology.
Input Imaging Modality
3D CT datasets, fluoroscopic live X-ray images, CABT, CBCT scans
Anatomical Site
Lungs
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
For AI-Tomo the clinical validation was performed by physicians with data that was recorded from historical LungVision clinical cases. Cases were selected to include a representative range of lesion sizes, types, and locations, as well as a range of tools.
Simulated cases: synthetically simulated test cases, based on real human's CT scans. With this method we can simulate a lot of different use cases, and use diverse data. This method uses simulated data in a simulated environment, and therefore measures the registration error only, without additional real-world inaccuracies: CT to body divergence, jig estimation errors, fluoroscope distortion etc.
Rigid body model cases: Laboratory test cases recordings and analysis in a patient simulated environment with a rigid lung model under real-time fluoroscopy.
CBCT scans cases: test cases based on real human's CT scans and real human's CBCT scans taken in real procedures. This method is based on real cases data and therefore represents a real-world scenario and influenced by real-world inaccuracies: CT to body divergence, jig estimation errors, fluoroscope distortion etc.
The images are CABTs collected from real cases performed by physicians using the LungVision system in the USA, Italy, and Israel. The CABTs are used as input data to the AI-Tomo post processing, and the output is evaluated using the ground truthing methodology.
Ground Truth methods included:
- Geometrv test .
- Lesion contrast on real data tests (with and without tool) .
Summary of Performance Studies
Bench Tests
- Testing includes verification testing of the requirements, testing of hazards mitigations and performance testing of the system.
- Testing has also been performed on physical and simulated lung models representing deformable tissue.
AI-Tomo Clinical Validation:
- Simulated cases: The mean accuracy calculated for 500 cases is 3.15 mm with 3.2 mm std.
- Rigid body model cases: The mean accuracy calculated for 93 cases is 3.64 mm with 1.57 mm std.
- CBCT scans cases: The mean accuracy calculated for 191 cases is 5.34 mm with 3.32 mm std
- The clinical validation of AI Tomo confirmed that lesion marking accuracy was comparable when using CABT and AI Tomo images. Therefore, regardless of the imaging modality used, the accuracy of the registration process remains consistent.
Key Metrics
Simulated cases: mean accuracy 3.15 mm with 3.2 mm std.
Rigid body model cases: mean accuracy 3.64 mm with 1.57 mm std.
CBCT scans cases: mean accuracy 5.34 mm with 3.32 mm std.
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
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Image /page/0/Picture/0 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 blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Body Vision Ltd. % Paul Dryden Consultant ProMedic Consulting LLC 131 Bay Point Dr. NE St. Petersburg, Florida 33704
October 1, 2024
Re: K240943
Trade/Device Name: LungVision Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: August 30, 2024 Received: August 30, 2024
Dear Paul Dryden:
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 (the 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 available 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.
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Page
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review. the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Re"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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
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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,
Jessica Lamb
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiologic Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
Form Approved: OMB No. 0910-0120 Expiration Date: 07/31/2026 See PRA Statement below.
Submission Number (if known)
Device Name
LungVision
Indications for Use (Describe)
The LungVision System is intended to enable users to segment previously acquired 3D CT datasets and overlay and register these 3D segmented data sets with fluoroscopic live X-ray images of the same anatomy in order to support catheter/device navigation during pulmonary procedures.
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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| Official Contact: | Benny Krauz – VP RA/QA
Body Vision Medical Ltd.
7 Hamada St.
Herzliya 4673341 Israel
Tel: +972-54-208-098 |
|----------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|
| Submission Correspondent: | Paul Dryden
ProMedic, LLC
St. Petersburg, FL 33704 |
| Proprietary or Trade Name: | LungVision System |
| Common/Usual Name: | System, image processing, radiological |
| Classification Name: | Automated radiological image processing software
QIH, CFR 892.2050
Picture archiving and communications system
LLZ, CFR 892.2050 |
| Predicate Device: | BodyVision - LungVision - K183593
Picture archiving and communications system
LLZ, CFR 892.2050 |
Description:
The Lung Vision System is designed to enable users to segment previously acquired 3D CT datasets and overlay and register these 3D segmented data sets with live X-ray images of the same anatomy in order to support catheter/device navigation during pulmonary procedures.
The System is intended to assist the guidance of endobronchial tools to areas of interest inside a patient's lungs. The System allows the user to mark lesion locations and pathways to marked lesions using a patient's CT scan. During the endoscopic procedure, the System overlays planning information on real-time fluoroscopic images to guide endobronchial tool navigation. The System also provides tomographic images for lesion identification, as well as 3D views for understanding tool and lesion proximity and orientation. The System is designed to be integrated with fluoroscopic imaging systems and external displays.
The Lung Vision system includes a main unit and a tablet. Image processing algorithms are executed on the main unit and the tablet is used as a primary method of interacting with the system.
System Components Overview
The following is a list of the LungVision System's main components: LungVision Main Unit LungVision Tablet LungVision Router LungVision Board (passive device) LungVision Software
The submission includes claims of:
- Transform 2D X-ray images into intraoperative tomographic scans ●
- AI-driven intraoperative tomographic imaging
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- We find that this reflects the functionality of AI TOMO feature. The system receives CABT O output and transform them into tomographic imaging.
- . CABT is a limited angle tomography based on the SIRT algorithm
Indications for Use:
The LungVision System is intended to enable users to segment previously acquired 3D CT datasets and overlay and register these 3D segmented data sets with fluoroscopic live X-ray images of the same anatomy in order to support catheter/device navigation during pulmonary procedures.
Contraindications:
None
Device Comparison
Table 1 compares the subject device to the predicate
| Feature | Proposed Device
LungVision | Primary Predicate
K183593
Body Vision LungVision |
|---------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Indications for Use | The LungVision System is intended to enable users to segment previously acquired 3D CT datasets and overlay and register these 3D segmented data sets with fluoroscopic live X-ray images of the same anatomy in order to support catheter/device navigation during pulmonary procedures. | The LungVision System is intended to enable users to segment previously acquired 3D CT datasets and overlay and register these 3D segmented data sets with fluoroscopic live X-ray images of the same anatomy in order to support catheter/device navigation during pulmonary procedures. |
| Classification | Automated radiological image processing software
QIH, CFR 892.2050
Picture archiving and communications system LLZ, CFR 892.2050 | Picture archiving and communications system LLZ, CFR 892.2050 |
| Target anatomy | Lungs | Lungs |
| Anatomy access | Bronchial airways | Bronchial airways |
| Windows OS | Windows 10 | Windows 10 |
| Medical imaging software | Yes | Yes |
| General Image 2D/3D review | Yes | Yes |
| 3D rendering view | Yes | Yes |
| Multi-modality Support | Yes | Yes |
| Image registration | Yes | Yes |
| Multi-planar reformatting (MPR) | Yes | Yes |
| DICOM import | Yes | Yes |
| Fluoroscopic video | Yes | Yes |
| Standard Image viewing tools | Yes | Yes |
| Segmentation tool | Yes | Yes |
| Video capture | Yes | Yes |
| Live Image overlays | Yes | Yes |
| Import prior plans | Yes | Yes |
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510(k) Summarv
| 30-Aug-24 | ||
|---|---|---|
| Feature | Proposed Device | |
| LungVision | Primary Predicate | |
| K183593 | ||
| Body Vision LungVision | ||
| Point | ||
| marking/ tagging | Yes | Yes |
| Navigation type | Visual | Visual |
| Feature | Proposed Device | |
| LungVision | Primary Predicate | |
| K183593 | ||
| Body Vision LungVision | ||
| Modifications | ||
| Virtual bronchoscopy | Yes | Yes |
| C-Arm based CT | Yes | Yes |
| Multi-view set-up | Yes | Yes |
| Real-time | ||
| compensation | N/A | Yes |
| 3D Guidance | Yes | Yes |
Substantial Equivalence Discussion
We will discuss the table above.
Indications for Use / Patient Population / Environment of Use: As in comparison of Indications For Use above, we can conclude that the indications for use for the LungVision and the predicate are substantially equivalent.
Discussion: The differences in proposed indications for use are minor. The minor differences do not raise new risk or safety concerns, and the subject device can be found substantially equivalent.
Prescriptive: Both the LungVision and predicate are prescription devices. Discussion: There are no differences.
Design and Technology: The LungVision utilizes similar technology as the predicate. This includes:
- software applications that provide 2D and 3D medical image acquisition including real-time video . image acquisition and visualization of the anatomy
- allow co-registration of real-time images to previously created 3D image sets based on previously collected DICOM CT images
- include image enhancements such as contrast and brightness, zoom and pan capabilities ●
- . addition of a Tablet to the PC but not to be used for diagnostic purposes
Discussion: The software modifications including virtual bronchoscopy, C-arm based Tomography, 3D Guidance, and Real-time compensation are similar to the reference device.
Performance and Specifications:
The performance and specifications demonstrate that the Lung Vision and predicate devices perform the same functions using the same technologies thus can be found substantially equivalent.
Discussion: There are no differences, thus the subject device can be found substantially equivalent.
Compliance with Standards:
LungVision includes hardware and complies with ANSI/AAMI/ES 60601-1:2005(2012) and IEC 60601-1-2:2014.
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Discussion: The proposed device complies with the latest standards and thus can be found substantially equivalent.
Non-clinical / Bench Performance Testing:
We have performed bench tests and found that the Lung Vision met all requirements specifications and was found to be equivalent in comparison to the predicate. Testing includes verification testing of the requirements, testing of hazards mitigations and performance testing of the system.
Testing has also been performed on physical and simulated lung models representing deformable tissue.
For AI-Tomo the clinical validation was performed by physicians with data that was recorded from historical LungVision clinical cases. Cases were selected to include a representative range of lesion sizes, types, and locations, as well as a range of tools.
Simulated cases: synthetically simulated test cases, based on real human's CT scans. With this method we can simulate a lot of different use cases, and use diverse data. This method uses simulated data in a simulated environment, and therefore measures the registration error only, without additional real-world inaccuracies: CT to body divergence, jig estimation errors, fluoroscope distortion etc.
The mean accuracy calculated for 500 cases is 3.15 mm with 3.2 mm std.
Rigid body model cases: Laboratory test cases recordings and analysis in a patient simulated environment with a rigid lung model under real-time fluoroscopy.
This method provides a lower amount of test cases, as it uses only one lung anatomy with one ground truth transform. It is closer to a real-world scenario as it is influenced by jig estimation errors and fluoroscope distortion, but still does not include CT to body divergence.
The mean accuracy calculated for 93 cases is 3.64 mm with 1.57 mm std.
CBCT scans cases: test cases based on real human's CT scans and real human's CBCT scans taken in real procedures. This method is based on real cases data and therefore represents a real-world scenario and influenced by real-world inaccuracies: CT to body divergence, jig estimation errors, fluoroscope distortion etc.
The mean accuracy calculated for 191 cases is 5.34 mm with 3.32 mm std
The clinical validation of AI Tomo confirmed that lesion marking accuracy was comparable when using CABT and AI Tomo images. Therefore, regardless of the imaging modality used, the accuracy of the registration process remains consistent.
The images are CABTs collected from real cases performed by physicians using the LungVision system in the USA, Italy, and Israel. The CABTs are used as input data to the AI-Tomo post processing, and the output is evaluated using the ground truthing methodology.
Ground Truth methods included:
- Geometrv test .
- Lesion contrast on real data tests (with and without tool) .
Biocompatibility: There are no patient contacting parts of the LungVision System
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Substantial Equivalence Conclusion
Based upon the foregoing performance testing and comparison to the legally marketed predicate devices for indications for use, technology, and performance we believe we have demonstrated that the LungVision System is substantially equivalent in safety and effectiveness to the predicate device.