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.

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.

AI/ML Overview

The provided text describes the acceptance criteria and a study to prove the device meets these criteria for the LSN (Liver Surface Nodularity) software.

Here's the breakdown of the information requested:

1. A table of acceptance criteria and the reported device performance:

The document describes the acceptance criteria in terms of the results of testing done. While it doesn't present a formal table of quantitative acceptance criteria and corresponding performance metrics, it states general criteria that were met.

Acceptance Criteria (Inferred from "Testing Information and Performance" section)	Reported Device Performance
All product specifications verified.	"All product specifications were verified."
Product meets user needs.	"the product to meet user needs was validated."
Testing performed according to internal company procedures.	"Testing was performed according to internal company procedures."
Software testing and validation conducted according to written testing procedures.	"Software testing and validation were conducted according to written testing was conducted."
Test results reviewed by design personnel before software release.	"Test results were reviewed by designals before software proceeded to release."
Validation test results support design intent.	"Validation test results support the conclusion that actual device performance satisfies the design intent."
Functional verification met design requirements.	"functional verfication...all met design requirements."
Licensing met design requirements.	"licensing...all met design requirements."
Labeling met design requirements.	"labeling...all met design requirements."
Feature functionality met design requirements.	"feature functionality all met design requirements."
Arithmetic accuracy verified and validated.	"Arithmetic ... accuracy was veilied and validated by comparison to alternative calculation mechanisms."
Report accuracy verified and validated.	"report accuracy was veilied and validated by comparison to alternative calculation mechanisms."
Clinical operation validated through usability testing.	"clinical operation was validated through usability testing."
LSN output is repeatable for different CT imaging and reconstruction parameters.	"LSN output is repeatable for different CT imaging and reconstruction parameters."
LSN output is reproducible across different CT scanner types and vendors.	"reproducible across different CT scanner types and vendors."
Intra-observer measurement variability is low.	"the intra- and inter-observer measurement variability is low."
Inter-observer measurement variability is low.	"the intra- and inter-observer measurement variability is low."
Risk analysis completed and risk control implemented to mitigate unacceptable hazards.	"The LSN risk analysis was completed and risk control mere implemented to mitigate unacceptable hazards."
Verification testing results supported claims of substantial equivalence.	"Verfication testing results supported the claims of substantial equivalence."

2. Sample size used for the test set and the data provenance:

Test Set Sample Size: The document does not explicitly state the sample size (number of cases or images) used for the testing/validation set.
Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective. It generally refers to "different CT imaging and reconstruction parameters" and "different CT scanner types and vendors."
Racial Backgrounds: "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."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

The document mentions "usability testing" and "user expertise" but does not specify a number of experts used to establish ground truth or their specific qualifications (e.g., "radiologist with 10 years of experience"). It only generally refers to "highly-trained healthcare professionals such as radiologists and medical imaging technologists."

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

The document does not describe any specific adjudication method (like 2+1 or 3+1) for establishing ground truth on the test set.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

The document does not describe an MRMC comparative effectiveness study directly measuring human reader improvement with AI assistance. The study focuses on the device's technical performance and consistency, stating that it "assists radiologists" and "does not directly generate any diagnosis."

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

The document implies that the device's output (LSN score calculation, segmentation, etc.) was tested for repeatability, reproducibility, and variability, suggesting a standalone component for these technical measurements. However, the overall device function is described as "intended to assist radiologists," meaning it's not purely standalone in its intended clinical use. The "arithmetic and report accuracy" validation could be considered aspects of standalone performance proof.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

The document speaks of "verification" and "validation" against "design requirements" and "alternative calculation mechanisms" for arithmetic accuracy. For "clinical operation," it mentions "usability testing." While it implies the existence of a 'correct' or 'intended' output, it does not explicitly state the specific type of ground truth (e.g., expert consensus readings, histopathology confirmation) used for validating the LSN score itself or the segmentation accuracy.

8. The sample size for the training set:

The document does not mention the sample size for any training set. It primarily discusses "bench testing" and "validation" of the final product.

9. How the ground truth for the training set was established:

Since no training set is mentioned or implied, no information is provided on how its ground truth might have been established. The focus of this document is on the validation of the device for regulatory submission, not its development process.

Summary

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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

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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

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Indications for Use

510(k) Number (if known) K201092

Device Name LSN

Indications for Use (Describe)

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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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, LLC13416 Watertown Plank Road, Suite 260Elm 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
ClassificationName:	Computed Tomography X-Ray System
Review Panel:	Radiology
RegulationNumber:	892.1750
Device Class:	II
Product Code:	JAK
SubsequentProduct Code	LLZ (892.2050; System, Image Processing, Radiological; Picture Archiving and Communications System)

Predicate Device Information

Type	510(k)	Trade Name	Manufacturer	RegulatoryCitation(21 CFR)	Regulation Name	RegulatoryClass	PrimaryProductCode
Primary Predicate	K133649	Hepatic VCAR	GE Medical Systems SCS	892.1750	System, X-Ray, Tomography, Computed	II	JAK
SecondaryPredicate	K101342	OsiriX MD	Pixmeo SARL	892.2050	System, Image Processing, Radiological	II	LLZ

Device Description

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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.

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 forSubstantial Equivalence
LSN (Liver SurfaceNodularity):
1. is an image analysissoftware application	Yes	Yes	Yes	Same
2. intended to assistradiologists and othertrained healthcareprofessionals	Yes	YesFrom p.16 of the User Manual: "IntendedOperator Profile: Hepatic VCAR isdesigned for clinicians, specifically,radiologists, interventional radiologists,surgeons, and other trained healthcareprofessionals (3D technologists).Clinicians and healthcare professionalsusing this software must be educated inthe reading and quantitative andqualitative analysis of CT liver exams."	Yes	Same
3. in analyzing andreporting on computedtomography (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 aprecursor for liver lesions, e.g.hepatocellular carcinoma).	Yes (Not specifically claimed in theIntended Use statement, but implied:General image viewing and analysisapplication for all patients).	Same. Chronic liverdisease is a subset of theintended patientpopulation for HepaticVCAR, as well as forOsiriX MD.
6. is designed to assist theuser	Yes	Yes	Not specifically claimed in the IntendedUse statement, but implicit in thefunctionality.	Same

7. in the evaluation anddocumentation of livermorphology,	Yes	Yes	Yes (Not specifically claimed in theIntended Use statement, but implied:General image viewing and analysisapplication for all patients). Yes (Notspecifically claimed in the Intended Usestatement, but implied: General imageviewing and analysis application for allpatients).	Same
8. specifically liver surfacenodularity, provided thatthe surface nodularity isadequately depicted onthe CT images.	Yes	Not specifically claimed in the IntendedUse statement; Hepatic VCAR isintended for evaluating liver morphologywhich may include aspects of surfacenodularity, but features are primarilyfocused on evaluation and quantitationof liver lesions, provided they are visibleon the CT images.	Not specifically claimed in the IntendedUse statement; OsiriX MD may be usedto evaluate liver surface nodularity.	Same
9. provides quantitativemetrics related to liverfibrosis	Yes	Not specifically claimed in the IntendedUse statement; Hepatic VCAR is used toassess liver volumes which are in turnused to asses hepatic fibrosis.	Not specifically claimed in the IntendedUse statement, but these types ofmeasurement tools are included in thedevice features.	Same for secondarypredicate device
10. by automatingsegmentation of the liversurface within user-defined Regions ofInterest (ROIs)	Yes	Yes	No	Same for primarypredicate device
11. and calculatingdistances and meansrelated to the liver surfacenodularity.	Yes	No	Not specifically claimed in the IntendedUse statement, but these types ofmeasurement tools are included in thedevice features.	Same for secondarypredicate device
12. offers reportingcapabilities fordocumenting user-confirmed results	Yes	Yes	Yes	Same
13. facilitatingcommunication with othertrained healthcareprofessionals	Yes	Yes	Not specifically claimed in the IntendedUse statement, but implied by "Imagesand data can be communicated."	Same
14. facilitating assessmentof changes over time	Yes	Similar - See #8. Hepatic VCAR isintended to assist with evaluatingchanges in liver lesions over time. LSNis intended to assist with evaluatingchanges in liver surface nodularity overtime.	Not specifically claimed in the IntendedUse statement. In general, quantitativemeasurements facilitate assessment ofmorphological changes over time, sothis is an implicit benefit of the devicefeatures.	Same
15. intended to provideimage-related informationthat is interpreted by atrained professional	Yes	Yes	Yes. (Not explicitly stated, but implied).	Same
16. does not directlygenerate any diagnosis	Yes	Yes. (Not explicitly stated, but implied).	Yes. (Not explicitly stated, but implied).	Same
17. The informationprovided should not beused in isolation whenmaking patientmanagement decisions	Yes	Yes. (Not explicitly stated, but implied bydevice classification).	Yes. (Not explicitly stated, but impliedby device classification).	Same
18. not intended for usewith or for the diagnosticinterpretation ofmammography images	Yes	Yes. (Not explicitly stated for HepaticVCAR, but implied by Volume ViewerPlus's exclusion of mammographyimages).	No. (allows viewing of mammographyimages with an appropriate monitor)	Same for primarypredicate device

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Subject Device Indications for Use/Intended Use Statement: LSN

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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:
		Hepatic VCAR
1. Designed to work as a plug-in softwareapplication that works with an externalsoftware platform application by leveragingthe external platform's DICOMcommunications, image display, andgeneral image processing capabilities	Yes	Similar - Works with VolumeViewer Plus software applicationfrom GE Medical Systems(K041521) to provide liver-specificfunctionality, including semi-automated liver segmentation	No - OsiriX MD is the platformapplication that provides DICOMcommunications, image display,and general image processingcapabilities which are leveragedby 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 toolsvia published API)	Yes	Yes - offers support for a widerange of ROI types and statistics
5. User may accept/reject any/all ROIsbefore finalizing measurements andcreating a report	Yes	Yes	Yes
6. Semi-automated liver boundary detectioninside user-defined ROIs	Yes	Yes (ROIs may be completeimages that user has identified ascontaining liver)	No
7. Calculates and displays smoothed liversurface approximation using a polygonalapproximation to the surface	Creates and displays asmoothed polynomial (spline)from the bounding surfacedetermined by a semi-automatededge detection based onthreshold processing of 2D data	Not verified how the liver surface isextracted and and displayed, butpresumably using a smoothpolynomial mesh.	Yes - It is possible to perform thisstep manually.
8. User-customizable colors for ROI andtext annotations on images	Yes	Yes	Yes

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9. Average spline-to-surface distance foreach ROI and overall LSN Score calculation.	Yes	No	LSN measurements for a segmentof liver surface may be performedmanually using native OsiriXmeasurement tools with off-linecalculation of the overall LSNScore
10. Display output of measurement tools foruser review and acceptance	Yes	Yes	Yes
11. Report generation	Yes	Yes	Yes
12. Runs on standard operating systemsand hardware	Yes: macOS, commercialcomputers	Yes: Windows OS, commercialcomputers	Yes: macOS, commercialcomputers
Physical Characteristic
13. Post-processing application (non-real-time, non-contacting, not life supporting orsustaining)	Yes	Yes	Yes
14. Sterilization and Shelf-life	Not applicable, softwareapplication	Not applicable, software application	Not applicable, softwareapplication
15. Biocompatibility	Not applicable, softwareapplication	Not applicable, software application	Not applicable, softwareapplication

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.

Regulation Number and Section

§ 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.