Not Found
No
The description details image processing algorithms (edge detection, spline fitting, distance calculations) but does not mention AI or ML.
No.
The device is an image analysis software application intended to assist healthcare professionals in analyzing and reporting on liver morphology for assessment of chronic liver disease. It provides quantitative metrics and reporting capabilities but does not directly treat or diagnose a disease.
Yes
Explanation: The "Intended Use" section states that LSN is "intended to assist radiologists and other trained healthcare professionals in analyzing and reporting on the liver morphology depicted in computed tomography (CT) images for use in assessment of chronic liver disease," and helps with "evaluation and documentation of liver morphology." While it does not directly generate a diagnosis, it provides "image-related information that is interpreted by a trained professional," which is a core function of a diagnostic device.
Yes
The device is described as a "post-processing software application" that analyzes existing CT images. It does not include any hardware components or require specific hardware for its function beyond a standard computer platform.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body (like blood, urine, or tissue) to detect diseases, conditions, or infections.
- LSN's Function: LSN analyzes images (CT scans) of the liver. It does not process biological samples.
- Intended Use: The intended use clearly states it's an "image analysis software application" to assist in analyzing and reporting on liver morphology depicted in CT images.
- Device Description: The device description reinforces that it's a "post-processing software application which assists trained professionals in evaluating DICOM computed tomography image studies."
While LSN provides information that can be used in the assessment of chronic liver disease, it does so by analyzing images, not by performing tests on biological samples. Therefore, it falls outside the definition of an In Vitro Diagnostic device.
N/A
Intended Use / Indications for Use
LSN (Liver Surface Nodularity) is an image analysis software application intended to assist radiologists and other trained healthcare professionals in analyzing and reporting on the liver morphology depicted in computed tomography (CT) images for use in assessment of chronic liver disease. LSN is designed to assist the user in the evaluation and documentation of liver morphology, specifically liver surface nodularity, provided that the surface nodularity is adequately depicted on the CT images.
LSN provides quantitative metrics related to liver fibrosis by automating segmentation of the liver surface within user-defined Regions of Interest (ROIs) and calculating distances and means related to the liver surface nodularity. LSN also offers reporting capabilities for documenting user-confirmed results, thereby facilitating communication with other trained healthcare professionals and assessment of changes over time.
LSN is intended to provide image-related information that is interpreted by a trained professional, but it does not directly generate any diagnosis. The information provided by LSN should not be used in isolation when making patient management decisions.
LSN is not intended for use with or for the diagnostic interpretation of mammography images.
Product codes (comma separated list FDA assigned to the subject device)
JAK, LLZ
Device Description
LSN (Liver Surface Nodularity) is a post-processing software application which assists trained professionals in evaluating DICOM computed tomography image studies of patients with chronic liver disease. The software provides tools to enable the user to make quantitative measurements related to liver surface nodularity as depicted on CT images.
The generated information consists of a LSN Score (reported in tenths of a millimeter), a quantitative measure of the surface nodularity based on a set of user-defined ROIs sampling the liver surface. LSN calculates the distance between the detected liver edge and a smoothed polynomial line (spline) on a pixel-by-pixel basis inside ROIs and reports the mean of these distances on a per-slice basis as well as an overall LSN Score for the imaging series.
LSN provides the user with information the progression of chronic liver disease. LSN does not make clinical decisions and the information provided by LSN must not be used in isolation when making patient management decisions. The LSN Score may provide value by standardizing terminology used to describe surface neporting, thereby facilitating communications between radiologists and other clinicans invalient's treatment planning. In addition, standardized reporting metrics may also be helpful in assessing changes for the same patient over time.
LSN functions by displaying a DICOM CT abdominal series to the user paints a broad region of interest (RO)) delineating the liver edge on a subset of image slices. Then, for the painted region on each slice, the edge is detected using multiple algorithms. For each detected edge, a spline is fit to the edge and the shortes from each edge pixel to the spline are calculated and averaged, resulting in a potential LSN value. The maximum LSN value calculated for an edge is reported as the LSN values for all slices on which ROIs have been painted are then averaged to determine the overall LSN score.
The core LSN algorithms are implemented in platform-independent code, and have been integrated into both a standalone PC research application and a Mac-based viewer plugin for clinical use. Both platforms produce an equivalent LSN score; the sthe algorithm to require less re-work by the user. The clinical version also produces a report containing images of the scores for each slice, and the overall LSN score. The report is produced in both PDF and DICOM formats and is ready for upload to PACS.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Not Found
Input Imaging Modality
Computed Tomography (CT)
Anatomical Site
Liver
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Radiologists and other trained healthcare professionals.
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
Not Found
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Bench testing (functional and integration) was conducted for LSN during product development.
Bench testing included functional verfication, icensing, labeling, and feature functionality all met design requirements. Arithmetic and report accuracy was veilied and validated by comparison to alternative calculation mechanisms. And clinical operation was validated through usability testing.
Test results demonstrated that LSN output is repeatable for different CT imaging and reconstruction parameters, reproducible across different CT scanner types and vendors, and that the intra- and inter-observer measurement variability is low.
To obtain consistent output, images should be obtained at resolutions with sub-mm pixel sizes in the axial plane depend on slice thickness, and to a lesser extent, other imaging parameters such as contrast agent presence, so the user is instructed that comparisons to previous results published in the scientific literant CT exams should be undertaken with a knowledge of these dependencies.
The LSN risk analysis was completed and risk control mere implemented to mitigate unacceptable hazards. Verification testing results supported the claims of substantial equivalence.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.
0
October 29, 2020
Image /page/0/Picture/1 description: The image contains 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 includes the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
Imaging Biometrics, LLC % Mr. Timothy Dondlinger COO 13416 Watertown Plank Road, Suite 260 ELM GROVE WI 53122
Re: K201092
Trade/Device Name: LSN Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK, LLZ Dated: September 22, 2020 Received: September 24, 2020
Dear Mr. Dondlinger:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmp/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see
1
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 medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K201092
Device Name LSN
Indications for Use (Describe)
LSN (Liver Surface Nodularity) is an image analysis software application intended to assist radiologists and other trained healthcare professionals in analyzing and reporting on the liver morphology depicted in computed tomography (CT) images for use in assessment of chronic liver disease. LSN is designed to assist the user in the evaluation and documentation of liver morphology, specifically liver surface nodularity, provided that the surface nodularity is adequately depicted on the CT images.
LSN provides quantitative metrics related to liver fibrosis by automating segmentation of the liver surface within userdefined Regions of Interest (ROIs) and calculating distances and means related to the liver surface nodularity. LSN also offers reporting capabilities for documenting user-confirmed results, thereby facilitating communication with other trained healthcare professionals and assessment of changes over time.
LSN is intended to provide image-related information that is interpreted by a trained professional, but it does not directly generate any diagnosis. The information provided by LSN should not be used in isolation when making patient management decisions.
LSN is not intended for use with or for the diagnostic interpretation of mammography images.
| Type of Use (Select one or both, as applicable) |
|---|
| ------------------------------------------------- |
| X Prescription Use (Part 21 CFR 801 Subpart D) |
|---|
| □ Over-The-Counter Use (21 CFR 801 Subpart C) |
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3
510(k) Summary
510(k) Notification K201092
This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of SMDA 1990 and 21 CFR 807.92.
| Applicant /
Owner : | Imaging Biometrics, LLC
13416 Watertown Plank Road, Suite 260
Elm Grove, WI 53122 |
|----------------------------|-------------------------------------------------------------------------------------------------------|
| Contact Person: | Timothy Dondlinger
(262) 439-8252 (telephone)
(262) 439-8301 (fax)
tim@imagingbiometrics.com |
| Date Prepared: | April 17, 2020 |
| Common Name: | System, X-Ray, Tomography, Computed |
| Trade Name: | LSN |
| Classification
Name: | Computed Tomography X-Ray System |
| Review Panel: | Radiology |
| Regulation
Number: | 892.1750 |
| Device Class: | II |
| Product Code: | JAK |
| Subsequent
Product Code | LLZ (892.2050; System, Image Processing, Radiological; Picture Archiving and Communications System) |
Predicate Device Information
| Type | 510(k) | Trade Name | Manufacturer | Regulatory
Citation
(21 CFR) | Regulation Name | Regulatory
Class | Primary
Product
Code |
|------------------------|---------|--------------|------------------------|------------------------------------|----------------------------------------|---------------------|----------------------------|
| Primary Predicate | K133649 | Hepatic VCAR | GE Medical Systems SCS | 892.1750 | System, X-Ray, Tomography, Computed | II | JAK |
| Secondary
Predicate | K101342 | OsiriX MD | Pixmeo SARL | 892.2050 | System, Image Processing, Radiological | II | LLZ |
Device Description
LSN (Liver Surface Nodularity) is a post-processing software application which assists trained professionals in evaluating DICOM computed tomography image studies of patients with chronic liver disease. The software provides tools to enable the user to make quantitative measurements related to liver surface nodularity as depicted on CT images.
The generated information consists of a LSN Score (reported in tenths of a millimeter), a quantitative measure of the surface nodularity based on a set of user-defined ROIs sampling the liver surface. LSN calculates the distance between the detected liver edge and a smoothed polynomial line (spline) on a pixel-by-pixel basis inside ROIs and reports the mean of these distances on a per-slice basis as well as an overall LSN Score for the imaging series.
LSN provides the user with information the progression of chronic liver disease. LSN does not make clinical decisions and the information provided by LSN must not be used in isolation when making patient management decisions. The LSN Score may provide value by standardizing terminology used to describe surface neporting, thereby facilitating communications between radiologists and other clinicans invalient's treatment planning. In addition, standardized reporting metrics may also be helpful in assessing changes for the same patient over time.
LSN functions by displaying a DICOM CT abdominal series to the user paints a broad region of interest (RO)) delineating the liver edge on a subset of image slices. Then, for the painted region on each slice, the edge is detected using multiple algorithms. For each detected
4
edge, a spline is fit to the edge and the shortes from each edge pixel to the spline are calculated and averaged, resulting in a potential LSN value. The maximum LSN value calculated for an edge is reported as the LSN values for all slices on which ROIs have been painted are then averaged to determine the overall LSN score.
The core LSN algorithms are implemented in platform-independent code, and have been integrated into both a standalone PC research application and a Mac-based viewer plugin for clinical use. Both platforms produce an equivalent LSN score; the sthe algorithm to require less re-work by the user. The clinical version also produces a report containing images of the scores for each slice, and the overall LSN score. The report is produced in both PDF and DICOM formats and is ready for upload to PACS.
Indications for Use/Intended Use Statement
LSN (Liver Surface Nodularity) is an image analysis software application intended to assist radiologists and other trained healthicare professionals in analyzing and reporting on the liver morphology depicted in computed tomography (CT) images for use in assessment of chronic liver disease. LSN is designed to assist the user in the evaluation of liver morphology, specifically liver surface nodularity, provided that the surface nodularity is adequately depicted on the CT images.
LSN provides quantitative metrics related to liver fibrosis by automating segmentation of the liver surface within user-defined Regions of Interest (ROls) and calculating distances and means related to the liver surface nodularity. LSN also offers reporting userconfirmed results, thereby facilitating communication with other trained healthcare professionals and assessor time.
LSN is intended to provide information that is interpreted by a trained professional, but it does not directly generate any diagnoss. The information provided by LSN should not be used in isolation when making patient management decisions.
LSN is not intended for use with or for the diagnostic interpretation of mammography images.
Substantial Equivalence
The below table provides a comparison summary of the subject device's Indications for Use/Intended Use Statement to the primary and secondary predicate devices.
| Indications for Use /
Intended Use | Subject | Primary Predicate:
Hepatic VCAR | Secondary Predicate:
OsiriX MD | Rationale for
Substantial Equivalence |
|-------------------------------------------------------------------------------------------------------------------------------------|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
| LSN (Liver Surface
Nodularity): | | | | |
| 1. is an image analysis
software application | Yes | Yes | Yes | Same |
| 2. intended to assist
radiologists and other
trained healthcare
professionals | Yes | Yes
From p.16 of the User Manual: "Intended
Operator Profile: Hepatic VCAR is
designed for clinicians, specifically,
radiologists, interventional radiologists,
surgeons, and other trained healthcare
professionals (3D technologists).
Clinicians and healthcare professionals
using this software must be educated in
the reading and quantitative and
qualitative analysis of CT liver exams." | Yes | Same |
| 3. in analyzing and
reporting on computed
tomography (CT) images | Yes | Yes | Yes (multimodality; CT included) | Same |
| 4. for assessment | Yes | Yes | Yes | Same |
| 5. in chronic liver disease. | Yes | Yes (Not explicitly stated, but implied.
Chronic liver disease is frequently a
precursor for liver lesions, e.g.
hepatocellular carcinoma). | Yes (Not specifically claimed in the
Intended Use statement, but implied:
General image viewing and analysis
application for all patients). | Same. Chronic liver
disease is a subset of the
intended patient
population for Hepatic
VCAR, as well as for
OsiriX MD. |
| 6. is designed to assist the
user | Yes | Yes | Not specifically claimed in the Intended
Use statement, but implicit in the
functionality. | Same |
| | | | | |
| 7. in the evaluation and
documentation of liver
morphology, | Yes | Yes | Yes (Not specifically claimed in the
Intended Use statement, but implied:
General image viewing and analysis
application for all patients). Yes (Not
specifically claimed in the Intended Use
statement, but implied: General image
viewing and analysis application for all
patients). | Same |
| 8. specifically liver surface
nodularity, provided that
the surface nodularity is
adequately depicted on
the CT images. | Yes | Not specifically claimed in the Intended
Use statement; Hepatic VCAR is
intended for evaluating liver morphology
which may include aspects of surface
nodularity, but features are primarily
focused on evaluation and quantitation
of liver lesions, provided they are visible
on the CT images. | Not specifically claimed in the Intended
Use statement; OsiriX MD may be used
to evaluate liver surface nodularity. | Same |
| 9. provides quantitative
metrics related to liver
fibrosis | Yes | Not specifically claimed in the Intended
Use statement; Hepatic VCAR is used to
assess liver volumes which are in turn
used to asses hepatic fibrosis. | Not specifically claimed in the Intended
Use statement, but these types of
measurement tools are included in the
device features. | Same for secondary
predicate device |
| 10. by automating
segmentation of the liver
surface within user-
defined Regions of
Interest (ROIs) | Yes | Yes | No | Same for primary
predicate device |
| 11. and calculating
distances and means
related to the liver surface
nodularity. | Yes | No | Not specifically claimed in the Intended
Use statement, but these types of
measurement tools are included in the
device features. | Same for secondary
predicate device |
| 12. offers reporting
capabilities for
documenting user-
confirmed results | Yes | Yes | Yes | Same |
| 13. facilitating
communication with other
trained healthcare
professionals | Yes | Yes | Not specifically claimed in the Intended
Use statement, but implied by "Images
and data can be communicated." | Same |
| 14. facilitating assessment
of changes over time | Yes | Similar - See #8. Hepatic VCAR is
intended to assist with evaluating
changes in liver lesions over time. LSN
is intended to assist with evaluating
changes in liver surface nodularity over
time. | Not specifically claimed in the Intended
Use statement. In general, quantitative
measurements facilitate assessment of
morphological changes over time, so
this is an implicit benefit of the device
features. | Same |
| 15. intended to provide
image-related information
that is interpreted by a
trained professional | Yes | Yes | Yes. (Not explicitly stated, but implied). | Same |
| 16. does not directly
generate any diagnosis | Yes | Yes. (Not explicitly stated, but implied). | Yes. (Not explicitly stated, but implied). | Same |
| 17. The information
provided should not be
used in isolation when
making patient
management decisions | Yes | Yes. (Not explicitly stated, but implied by
device classification). | Yes. (Not explicitly stated, but implied
by device classification). | Same |
| 18. not intended for use
with or for the diagnostic
interpretation of
mammography images | Yes | Yes. (Not explicitly stated for Hepatic
VCAR, but implied by Volume Viewer
Plus's exclusion of mammography
images). | No. (allows viewing of mammography
images with an appropriate monitor) | Same for primary
predicate device |
5
Subject Device Indications for Use/Intended Use Statement: LSN
6
LSN (Liver Surface Nodularity) is an image analysis software application intended to assist radiologists and other trained healthicare professionals in analyzing and reporting on the liver morphology depicted in computed tomography (CT) images for use in assessment of chronic liver disease. LSN is designed to assist the user in the evaluation of liver morphology, specifically liver surface nodularity, provided that the surface nodularity is adequately depicted on the CT images.
LSN provides quantitative metrics related to liver fibrosis by automating segmentation of the liver surface within user-defined Regions of Interest (RQIs) and calculating distances and means related to the liver surface nodularity. LSN also offers reporting capabilities for documenting user-confirmed results, thereby facilitating communication with other professionals and assessment of changes over time.
L SN is intended to provide image-related information that is interpreted by a trained professional, but it does not directly generate any diagnosis. The information provided by LSN should not be used in isolation when making patient management decisions.
LSN is not intended for use with or for the diagnostic interpretation of mammography images.
Primary Predicate Indications for Use/Intended Use Statement: Hepatic VCAR
Hepatic VCAR is a CT image analysis software package(1) that allows the analysis and visualization of Liver CT data(3.5) derived from DICOM 3.0 compliant CT scans. Hepatic VCAR is designed for the purpose of assessing liver morphology(5.7). including liver lesion, provided the lesion has different CT appearance from surrounding liver time114) through automated tools for liver. liver lobe, liver segments and liver lesion seqment(10). It is intended for use by clinicians(2) to process, review, archive, print and distribute(3,12,13) liver CT studies.
This software will assist the user(6) by providing initial 3D secmentation(10), visualization, and quantitative analysis of liver anatomy(7). The user has the ability to adjust the contour and confirm the final segmentation(2.6).
Secondary Predicate Indications for Use/Intended Use Statement: OsiriX MD
OsiriX MD TM is a software device intended for viewing of images acquired from CT(3), MR, CR, OR, US and other DICOM compliant medical imaging systems when installed on suitable commercial standard hardware. Images and data can be captured, stored, communicated(12,13), processed, and displayed(1) within the system and or across computer networks at distributed locations. Lossy compressed mammographic images and digitized film screen images must not be reviewed for image interpretation.
For primary diagnosis(4), post process DICOM for presentation" images must be used. Mammographic images should only be viewed with a monitor approved by FDA for viewing mammographic images.(18) It is the User's responsibility to ensure monitor quality, ambient light conditions, and image compression ratios are consistent with the clinical application(2).
A comparison of the subject device's technical characteristics compared to the predicate devices is summarized in the following table:
| Technical Characteristic | Subject | Primary Predicate: | Secondary Predicate: | OsiriX MD |
|---|---|---|---|---|
| Hepatic VCAR | ||||
| 1. Designed to work as a plug-in software | ||||
| application that works with an external | ||||
| software platform application by leveraging | ||||
| the external platform's DICOM | ||||
| communications, image display, and | ||||
| general image processing capabilities | Yes | Similar - Works with Volume | ||
| Viewer Plus software application | ||||
| from GE Medical Systems | ||||
| (K041521) to provide liver-specific | ||||
| functionality, including semi- | ||||
| automated liver segmentation | No - OsiriX MD is the platform | |||
| application that provides DICOM | ||||
| communications, image display, | ||||
| and general image processing | ||||
| capabilities which are leveraged | ||||
| by the LSN application. | ||||
| 2. Works with DICOM-standard CT images | Yes | Yes | Multimodality including CT | |
| 3. No special CT acquisition required | Yes | Yes | Yes | |
| 4. User-defined ROIs | Yes (leverages OsiriX ROI tools | |||
| via published API) | Yes | Yes - offers support for a wide | ||
| range of ROI types and statistics | ||||
| 5. User may accept/reject any/all ROIs | ||||
| before finalizing measurements and | ||||
| creating a report | Yes | Yes | Yes | |
| 6. Semi-automated liver boundary detection | ||||
| inside user-defined ROIs | Yes | Yes (ROIs may be complete | ||
| images that user has identified as | ||||
| containing liver) | No | |||
| 7. Calculates and displays smoothed liver | ||||
| surface approximation using a polygonal | ||||
| approximation to the surface | Creates and displays a | |||
| smoothed polynomial (spline) | ||||
| from the bounding surface | ||||
| determined by a semi-automated | ||||
| edge detection based on | ||||
| threshold processing of 2D data | Not verified how the liver surface is | |||
| extracted and and displayed, but | ||||
| presumably using a smooth | ||||
| polynomial mesh. | Yes - It is possible to perform this | |||
| step manually. | ||||
| 8. User-customizable colors for ROI and | ||||
| text annotations on images | Yes | Yes | Yes |
7
| 9. Average spline-to-surface distance for
each ROI and overall LSN Score calculation. | Yes | No | LSN measurements for a segment
of liver surface may be performed
manually using native OsiriX
measurement tools with off-line
calculation of the overall LSN
Score |
|-----------------------------------------------------------------------------------------------------------|-----------------------------------------|------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 10. Display output of measurement tools for
user review and acceptance | Yes | Yes | Yes |
| 11. Report generation | Yes | Yes | Yes |
| 12. Runs on standard operating systems
and hardware | Yes: macOS, commercial
computers | Yes: Windows OS, commercial
computers | Yes: macOS, commercial
computers |
| Physical Characteristic | | | |
| 13. Post-processing application (non-real-
time, non-contacting, not life supporting or
sustaining) | Yes | Yes | Yes |
| 14. Sterilization and Shelf-life | Not applicable, software
application | Not applicable, software application | Not applicable, software
application |
| 15. Biocompatibility | Not applicable, software
application | Not applicable, software application | Not applicable, software
application |
The intended use and technical characteristics for LSN are similar to both predicate Device Information section above for a software application that provides a user interface to allow a clinician to load, display, measure, and identify regions of interests. The software algorithms calculate statistical information of the image pixels contained within user-identified regions of interest. The calculated values provide a clinician with relevant information for assessment of chronic liver disease.
LSN is a software-only device, is non-pating, and is used by highly-trained healthcare professionals such as radiologists and medical imaging technologists. LSN does not provide any interpretation of the statistical parameters displayed to the user. The trained professional is responsible for identifying, measuring, and interpreting the images and data being displayed.
LSN and both its predicate devices are substantially equivalent in the categories of technical characteristics and features. LSN does not raise any new potential safety risks and is equivalent in performance to the existing legally marketed devices.
Testing Information and Performance
All product specifications were verified and the product to meet user needs was validated. Testing was performed according to internal company procedures. Software testing and validation were conducted according to written testing was conducted. Test results were reviewed by designals before software proceeded to release. Validation test results support the conclusion that actual device performance satisfies the design intent. Testing was performed in accordance document, "General Principles of Software Validation," issued January 11, 2002, and documentation provided by FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices," issued May 11, 2005. Bench testing (functional and integration) was conducted for LSN during product development.
Bench testing included functional verfication, icensing, labeling, and feature functionality all met design requirements. Arithmetic and report accuracy was veilied and validated by comparison to alternative calculation mechanisms. And clinical operation was validated through usability testing.
Test results demonstrated that LSN output is repeatable for different CT imaging and reconstruction parameters, reproducible across different CT scanner types and vendors, and that the intra- and inter-observer measurement variability is low.
To obtain consistent output, images should be obtained at resolutions with sub-mm pixel sizes in the axial plane depend on slice thickness, and to a lesser extent, other imaging parameters such as contrast agent presence, so the user is instructed that comparisons to previous results published in the scientific literant CT exams should be undertaken with a knowledge of these dependencies.
The LSN risk analysis was completed and risk control mere implemented to mitigate unacceptable hazards. LSN relies upon user expertise to determine the suitability of CT images with excessive noise, artifacts, and/or confounding anatomy or pathology obscuring any portion of the liver surface to be analyzed should not be used. Verfication testing results supported the claims of substantial equivalence.
LSN has not been evaluated with images from patients of all ethnicities. It has been primarily evaluated with White and Black racial backgrounds. LSN has not been evaluated with images from pediatric patients.
Design and Development Standards and Guidance
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LSN was designed and developed using the following standards and guidance:
- · NEMA PS 3.1-3.20: Digital Imaging and Communications in Medicine (DICOM)
- ISO 14971: Medical devices Application of risk management to medical devices
- IEC 62304: Medical device software Software life cycle processes
- · ISO 13485: Medical devices Quality management systems
- FDA: Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices
- · FDA: Content of Premarket Submissions for Management of Cybersecurity in Medical Devices
- FDA: Off-The-Shelf Software Use in Medical Devices
Conclusion
LSN has the same intended use as the primary and secondary predicate devices as an image processing software application for DICOM-format CT images. There is significant overlap between LSN and the predicate devices in technological characteristics. Any differences between LSN and the predicate devices do not raise new or different questions of safety and effectiveness. The result of all testing conducted was found acceptable to support the claim of substantial equivalence.