AVIEW LCS is intended for the review and analysis and reporting of thoracic CT images for the purpose of characterizing nodules in the lung in a single study, or over the time course of several thoracic studies. Characterizations include nodule type, location of the nodule and measurements such as size (major axis), estimated effective diameter from the volume of the nodule, the volume of the nodule, Mean HU (the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass (mass calculated from the CT pixel value), and volumetric measures (Solid Major, length of the longest diameter measured in 3D for a solid portion of the nodule. Solid 2nd Major: The length of the longest diameter of the solid part. measured in sections perpendicular to the solid portion of the nodule), VDT (Volume doubling time), and Lung-RADS (classification proposed to aid with findings). The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, also integrate with FDA certified Mevis CAD (Computer-aided detection) (K043617).

Device Description

AVIEW LCS is intended for use as diagnostic patient imaging which is intended for the review and analysis of thoracic CT images. Provides following features as semi-automatic nodule measurement (segmentation), maximal plane measure, 3D measure and volumetric measures, automatic nodules detection by integration with 3th party CAD. Also provides cancer risk based on PANCAN risk model which calculates the malignancy score based on numerical or Boolean inputs. Follow up support with automated nodule matching and automatically categorize Lung-RADS score which is a quality assurance tool designed to standardize lung cancer screening CT reporting and management recommendations that is based on type, size, size change and other findings that is reported.

-Nodule measurement
- Adding nodule by segmentation or by lines .
- Semi-automatic nodule measurement (segmentation) "
- . Maximal plane measure, 3D measure and volumetric measure.
- . Automatic large vessel removal.
- י Provides various features calculated per each nodule such as size, major(longest diameter measured in 2D/3D), minor (shortest diameter measured in 2D/3D), maximal plane, volume, mean HU, minimum HU, maximum HU for solid nodules and ratio of the longest axis for solid to non solid for paritla solid nodules.
- . Fully supporting Lung-RADS workflow: US Lung-RADS and KR Lung-RADS.
- . Nodule malignancy score (PANCAN model) calculation.
- . Importing from CAD results
-Follow-up
- ' Automatic retrieving the past data
- י Follow-up support with nodule matching and comparison
- Automatic calculation of VDT (volume doubling time)
Automatic nodule detection (CADe) -
- Seamless integration with Mevis Visia (FDA 510k Cleared) .
-Lungs and lobes segmentation
- Better segmentation of lungs and lobes based on deep-learning algorithms.
-Report
- PDF report generation .
. It saves or sends the pdf report and captured images in DICOM files.
. It provides structured report including following items such as nodule location and, also input finding on nodules.
Reports are generated using the results of all nodules nodules detected so far (Lung RADS) .
-Save Result
- . It saves the results in internal format

AI/ML Overview

The provided text describes the acceptance criteria and the study conducted to prove the AVIEW LCS device meets these criteria.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a formal table of acceptance criteria with corresponding reported device performance metrics in a single, clear format for all functionalities. Instead, it describes various tests and their success standards implicitly serving as acceptance criteria.

Based on the "Semi-automatic Nodule Segmentation" section, here's a reconstructed table for that specific functionality:

Acceptance Criteria (Semi-automatic Nodule Segmentation)	Reported Device Performance
Measured length should be less than one voxel size compared to the size of the sphere produced.	Implied "standard judgment" met through testing with spheres of various radii (2mm, 3mm, 6mm, 7mm, 8mm, 9mm, 10mm).
Measured volume should be within 10% error compared to the volume of the sphere created.	Implied "standard judgment" met through testing with spheres of various radii.

For "Nodule Matching test with Lung Registration":

Acceptance Criteria (Nodule Matching)	Reported Device Performance
Voxel Distance error between converted position and Nodule position of the Moving image (for evaluation of DVF).	Measured for 28 locations. Implied acceptance as the study "check the applicability of Registry" and "Start-up verification".
Accuracy evaluation of DVF subsampling for size optimization.	Implied acceptable accuracy for reducing DVF capacity.

For "Software Verification and Validation - System Test":

Acceptance Criteria (System Test)	Reported Device Performance
Not finding 'Major' defects.	Implied satisfied (device passed all tests).
Not finding 'Moderate' defects.	Implied satisfied (device passed all tests).

For "Auto segmentation (based on deep-learning algorithms) test":

Acceptance Criteria (Auto Segmentation - Korean Data)	Reported Device Performance
Auto-segmentation results identified by specialist and radiologist and classified as '2 (very good)'.	"The results of auto-segmentation are identified by a specialist and radiologist and classified as 0 (Not good), 1 (need adjustment), and 2(very good)". This suggests an evaluation was performed to categorize segmentation quality, but the specific percentage or number of "very good" classifications is not provided as a direct performance metric.

Acceptance Criteria (Auto Segmentation - NLST Data)	Reported Device Performance
Dice similarity coefficient between auto-segmentation and manual segmentation (performed by radiolographer and confirmed by radiologist).	"The dice similarity coefficient is performed to check how similar they are." The specific threshold or result of the DSC is not provided.

2. Sample Size Used for the Test Set and Data Provenance

Semi-automatic Nodule Segmentation:
- Sample Size: Not explicitly stated as a number of nodules or patients. Spheres of various radii (2mm, 3mm, 6mm, 7mm, 8mm, 9mm, 10mm) were created for testing.
- Data Provenance: Artificially generated spheres.
Nodule Matching test with Lung Registration:
- Sample Size: "28 locations" (referring to nodule locations for DVF calculation).
- Data Provenance: "deployed" experimental data, likely retrospective CT scans. Specific country of origin not mentioned but given the company's origin (Republic of Korea), it could involve Korean data.
Mevis CAD Integration test:
- Sample Size: Not explicitly stated. The test confirms data transfer and display.
- Data Provenance: Not specified, likely internal test data.
Brock Score (aka. PANCAN) Risk Calculation test:
- Sample Size:
  - Former paper: "PanCan data set, 187 persons had 7008 nodules, of which 102 were malignant," and "BCCA data set, 1090 persons had 5021 nodules, of which 42 were malignant."
  - Latter paper: "4431 nodules (4315 benign nodules and 116 malignant nodules of NLST data)."
- Data Provenance: Retrospective, from published papers. PANCAN data set, BCCA data set, and NLST (National Lung Screening Trial) data.
VDT Calculation test:
- Sample Size: Not explicitly stated.
- Data Provenance: Unit tests, implying simulated or internally generated data for calculation verification.
Lung RADS Calculation test:
- Sample Size: "10 cases were extracted."
- Data Provenance: Retrospective, "from the Lung-RADS survey table provided by the Korean Society of Thoracic Radiology."
Auto segmentation (based on deep-learning algorithms) test:
- Korean Data: "192 suspected COPD patients." (Retrospective, implicitly from Korea given the source "Korean Society of Thoracic Radiology").
- NLST Data: "80 patient's Chest CT data who were enrolled in NLST." (Retrospective, from the National Lung Screening Trial).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

Auto segmentation (based on deep-learning algorithms) test - Korean Data:
- Number of Experts: Not explicitly stated, but "a specialist and radiologist" suggests at least two experts.
- Qualifications: "specialist and radiologist." No specific years of experience or sub-specialty mentioned.
Auto segmentation (based on deep-learning algorithms) test - NLST Data:
- Number of Experts: At least two. "experienced radiolographer and confirmed by experienced radiologist."
- Qualifications: "experienced radiolographer" and "experienced radiologist." No specific years of experience or sub-specialty mentioned.
Brock Score (aka. PANCAN) Risk Calculation test: Ground truth established through the studies referenced; details on experts for those specific studies are not provided in this document.
Lung RADS Calculation test: Ground truth implicitly established by the "Lung-RADS survey table provided by the Korean Society of Thoracic Radiology." The experts who created this survey table are not detailed here.

4. Adjudication Method for the Test Set

The document does not explicitly describe a formal adjudication method (e.g., 2+1, 3+1) for establishing ground truth for any of the tests.

For the Auto segmentation (based on deep-learning algorithms) test, the "Korean Data" section mentions results are "identified by a specialist and radiologist and classified." This suggests independent review or consensus, but no specific adjudication rule is given. For the "NLST Data" section, manual segmentation was "performed by experienced radiolographer and confirmed by experienced radiologist," indicating a two-step process of creation and verification, rather than a conflict resolution method.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance was mentioned or conducted in this document. The device is for "review and analysis" and "reporting," and integrates with a third-party CAD, but its direct impact on human reader performance through an MRMC study is not detailed.

6. Standalone Performance Study (Algorithm only without human-in-the-loop performance)

Yes, standalone performance studies were conducted for several functionalities, focusing on the algorithm's performance without direct human-in-the-loop tasks:

Semi-automatic Nodule Segmentation: The test on artificial spheres evaluates the algorithm's measurement accuracy directly.
Nodule Matching test with Lung Registration: Evaluates the algorithm's ability to calculate DVF and match nodules.
Brock Score (aka. PANCAN) Risk Calculation test: Unit tests comparing calculated values from the algorithm against an Excel sheet.
VDT Calculation test: Unit tests to confirm calculation.
Lung RADS Calculation test: Unit tests to confirm implementation accuracy against regulations.
Auto segmentation (based on deep-learning algorithms) test: This is a standalone evaluation of the algorithm's segmentation performance against expert classification or manual segmentation (NLST data).

7. Type of Ground Truth Used

Semi-automatic Nodule Segmentation: Known physical properties of artificially generated spheres (e.g., precise radius and volume).
Nodule Matching test with Lung Registration: Nodule positions marked on "Fixed image" and "Moving image," implying expert identification on real CT scans.
Brock Score (aka. PANCAN) Risk Calculation test: Reference values from published literature (PanCan, BCCA, NLST data) which themselves are derived from ground truth of malignancy (pathology, clinical follow-up).
VDT Calculation test: Established mathematical formulas for VDT.
Lung RADS Calculation test: Lung-RADS regulations and a "Lung-RADS survey table provided by the Korean Society of Thoracic Radiology."
Auto segmentation (based on deep-learning algorithms) test - Korean Data: Expert classification ("0 (Not good), 1 (need adjustment), and 2(very good)") by a specialist and radiologist.
Auto segmentation (based on deep-learning algorithms) test - NLST Data: Manual segmentation performed by an experienced radiolographer and confirmed by an experienced radiologist.

8. Sample Size for the Training Set

The document does not explicitly state the sample size used for the training set(s) for the deep-learning algorithms or other components of the AVIEW LCS. It only details the test sets.

9. How the Ground Truth for the Training Set Was Established

Since the training set size is not provided, the method for establishing its ground truth is also not detailed in this document. However, given the nature of the evaluation for the test set (expert marking, classification), it is highly probable that similar methods involving expert radiologists or specialists would have been used to establish ground truth for any training data.

Summary

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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.

May 5, 2020

Coreline Soft Co., Ltd. % Hye Yi Park Deputy General Manager Strategic Business Dept. 4, 5F (Yeonnam-dong), 49, World Cup buk-ro 6-gil, Mapo-gu Seoul. 03991 REPUBLIC OF KOREA

Re: K193220

Trade/Device Name: AVIEW LCS Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ, JAK Dated: April 2, 2020 Received: April 6, 2020

Dear Hye Yi Park:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for

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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely.

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration

Indications for Use

Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2020 See PRA Statement below.

510(k) Number (if known)

K193220

Device Name AVIEW LCS

Indications for Use (Describe)

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)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

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K193220

510(k) Summary

SUBMITTER 1

Coreline Soft Co., Ltd. 4,5F (Yeonnam-dong), 49 World Cup buk-ro 6-gil, Mapo-gu, Seoul, 03991, Republic of Korea.

Phone: 82.2.517.7321 Fax: 82.2.571.7324

Contact Person: hyeyi. Park Date Prepared: 11.15.2019

DEVICE 2

Name of Device: AVIEW LCS Common or Usual Name: Image Processing Software Classification Name: System, image processing, radiological (21CFR 892.2050) Regulatory Class: II Product Code: LLZ, JAK

PREDICATE DEVICE 3

Lung Nodule Assessment and Comparison Option by Philips Medical System Nederland B.V. (K 162484)

Name of Device: Lung Nodule Assessment and Comparison Option (LNA) Common or Usual Name: Lung Nodule Assessment and Comparison Option (LNA) Classification Name: System, image processing, radiological (21CFR 892.1750) Regulatory Class: II Product Code: LLZ, JAK

This predicate has not been subject to a design-related recall

REFERENCE DEVICE 4

Lung Analysis Software by Vital Images, Inc. (K151283)

Name of Device: Lung Analysis Software Common or Usual Nmae: Radilolgical Image Processing Software Classification Name: System, Image Processing, Radiological Regulatory Class: II Product Code: JAK, LLZ

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AVIEW by Coreline Soft Co., Ltd. (K171199)

Name of Device: AVIEW Common or Usual Name: Image Processing Software Classification Name: System, image processing, radiological (21CFR 892.2050) Regulatory Class: II Product Code: LLZ

This reference device has not been subject to a design-related recall

DEVICEW DESCRIPTION 5

-Nodule measurement
- Adding nodule by segmentation or by lines .
- Semi-automatic nodule measurement (segmentation) "
- . Maximal plane measure, 3D measure and volumetric measure.
- . Automatic large vessel removal.
- י Provides various features calculated per each nodule such as size, major(longest diameter measured in 2D/3D), minor (shortest diameter measured in 2D/3D), maximal plane, volume, mean HU, minimum HU, maximum HU for solid nodules and ratio of the longest axis for solid to non solid for paritla solid nodules.
- . Fully supporting Lung-RADS workflow: US Lung-RADS and KR Lung-RADS.
- . Nodule malignancy score (PANCAN model) calculation.
- . Importing from CAD results
-Follow-up
- ' Automatic retrieving the past data
- י Follow-up support with nodule matching and comparison
- Automatic calculation of VDT (volume doubling time)
Automatic nodule detection (CADe) -
- Seamless integration with Mevis Visia (FDA 510k Cleared) .
-Lungs and lobes segmentation
- Better segmentation of lungs and lobes based on deep-learning algorithms.
-Report
- PDF report generation .

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. It saves or sends the pdf report and captured images in DICOM files.
. It provides structured report including following items such as nodule location and, also input finding on nodules.
Reports are generated using the results of all nodules nodules detected so far (Lung RADS) .
-Save Result
- . It saves the results in internal format

INDICATIONS FOR USE 6

AVIEW LCS is intended for the review and analysis and reporting of thoracic CT images for the purpose of characterizing nodules in the lung in a single study, or over the time course of several thoracic studies. Characterizations include nodule type, location of the nodule and measurements such as size (major axis), estimated effective diameter from the volume of the volume of the nodule, Mean HU (the average value of the CT pixel inside the nodule in HU), Minimum HU, Max (mass (mass calculated from the CT pixel value), and volumetric measures (Solid Major; length of the longest diameter measured in 3D for a solid portion of the nodule. Solid 2™ Major: The length of the longest diameter of the solid part, measured in sections perpendicular to the solid portion of the nodule), VDT (Volume doubling time), and Lung-RADS (classification proposed to aid with findings). The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, also integrate with FDA certified Mevis CAD (Computer-aided detection) (K043617).

COMPARISION OF TECHNOLOGICAL CHARACTERISTICS WITH 7 THE PREDICATE DEVCIE

AVIEW LCS has the same intended use and the principle of operation, and also has similar features to the predicate devices. Lung Nodule Assessment and Comparison Option (LNA) (K162484)

There might be slight differences in features and menu, but these differences between the predicate device and the proposed device are not so significant since they do not raise any new or potential safety risks to the user or patient and questions of safety or effectiveness. Based on the results of software validation and verification tests, we conclude that the proposed device is substantially equivalent to the predicate devices.

Characteristic	Subject Device	Primary PredicateDevice	Reference Device	Reference Device
Device Name	AVIEW LCS	Lung NoduleAssessment andComparison Option(LNA)	Lung AnalysisSoftware	AVIEW
ClassificationName	System, imageProcessingRadiological	System, imageProcessingRadiological	System, imageProcessingRadiological	System, imageProcessingRadiological
RegulatoryNumber	21 CFR 892.2050	21 CFR 892.2050	21 CFR 892.2050	21 CFR 892.2050
Product Code	LLZ, JAK	LLZ, JAK	LLZ, JAK	LLZ
Review Panel	Radiology	Radiology	Radiology	Radiology
510k Number	-	K162484	K151283	K171199
Indicationsfor use	AVIEW LCSAVIEW LCS is intended for the review and analysis and reporting of thoracic CT images for thepurpose of characterizing nodules in the lung in a single study, or over the time course of several

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thoracic studies. Characterizations include nodule type, location of the nodule and measurementssuch as size (major axis, minor axis), estimated effective diameter from the volume of the nodule,the volume of the nodule, Mean HU (the average value of the CT pixel inside the nodule in HU),Minimum HU, Max HU, mass (mass calculated from the CT pixel value), and volumetric measures(Solid Major; length of the longest diameter measured in 3D for a solid portion of the nodule. Solid2nd Major: The length of the longest diameter of the solid part, measured in sections perpendicularto the Major axis of the solid portion of the nodule), VDT (Volume doubling time), and Lung-RADS(classification proposed to aid with findings). The system automatically performs the measurement,allowing lung nodules and measurements to be displayed and, also integrate with FDA certifiedMevis CAD (Computer-aided detection) (K043617).Lung Nodule Assessment and Comparison Option (LNA)The Lung Nodule Assessment and Comparison Option is intended for use as a diagnostic patient-imaging tool. It is intended for the review and analysis of thoracic CT images, providing quantitativeand characterizing information about nodules in the lung in a single study, or over the time courseof several thoracic studies. Characterizations include diameter, volume and volume over time. Thesystem automatically performs the measurements, allowing lung nodules and measurements to bedisplayed.Toshiba's Lung Analysis SoftwareThe separately licensed Lung Analysis option is intended for the review and analysis of thoracic CTimages for the purpose of characterizing nodules in the lung in a single study, or over the timecourse of several thoracic studies. Characterization include diameter, volume and volume over time.The system automatically performs the measurement, allowing lung nodules and measurement tobe displayed.AVIEW
AVIEW provides CT values for pulmonary tissue from CT thoracic datasets. This software can beused to support the physician quantitatively in the diagnosis. Follow-up evaluation anddocumentation of CT lung tissue images by providing image segmentation of sub-structures in theleft and right lung (e.g., the five lobes and airway), volumetric and structural analysis, densityevaluations and reporting tools. AVIEW is also used to store, transfer, inquire and display CT datasets. AVIEW is not meant for primary image Interpretation in mammography.
Platform	IBM-compatible PC orPC network	IBM-compatible PC orPC network	IBM-compatible PC or PC network
User Interface	Monitor, Mouse,Keyboard	Monitor, Mouse,Keyboard	Monitor, Mouse,Keyboard
Image InputSources	Images can be scanned,loaded from cardreaders, or importedfrom a radiographicimaging device	Images can be scanned,loaded from cardreaders, or importedfrom a radiographicimaging device	Images can be scanned, loaded fromcard readers, or importedfrom a radiographicimaging device
Image format	DICOM	DICOM	DICOM
Intendedbody part	Chest	Chest	Chest
Type of scans	Thoracic CT images	Thoracic CT images	Thoracic CT images
GeneralDescription	AVIEW LCS is intended for use as diagnostic patient imaging which is intended for the review andanalysis of thoracic CT images. Provides following features as semi-automatic nodule measurement(segmentation), maximal plane measure, 3D measure and volumetric measures, automatic nodulesdetection by integration with 3rd party CAD. Also provides cancer risk based on PANCAN riskmodel which calculates the malignancy score based on numerical or Boolean inputs. Follow upsupport with automated nodule matching and automatically categorize Lung-RADS score which isa quality assurance tool designed to standardize lung cancer screening CT reporting and
KeyFunctions
	management recommendations that is based on type, size, size change and other findings that isreported.
	Lung Nodule Assessment and Comparison Option (LNA)
	The Lung Nodule Assessment and Comparison Option application is intended for use as adiagnostic patient imaging tool. It is intended for the review and analysis of thoracic CT images,providing quantitative and characterizing information about nodules in the lung in a single study,or over the time course of several thoracic studies. The system automatically performs themeasurements, allowing lung nodules and measurements to be displayed. The user interface andautomated tools help to determine growth patterns and compose comparative reviews. The Lung
	Nodule Assessment and Comparison Option application requires the user to identify a nodule andto determine the type of nodule in order to use the appropriate characterization tool. Lung NoduleAssessment and Comparison Option may be utilized in both diagnostic and screening evaluationssupporting Low Dose CT Lung Cancer Screening
	Lung Analysis Software
	Lung Analysis aids in measuring and characterizing lung nodules. The interface and automated toolshelp to efficiently determine growth patterns and compose comparative reviews. Lung Analysis isintended for the review and analysis of thoracic CT images for the purpose of characterizing nodulesin the lung in a single study, or over the time course of several thoracic studies. Characterizationsinclude diameter, volume and volume over time. The system automatically performs the
	measurements, allowing lung nodules and measurements to be displayed. The Lung AnalysisSoftware requires the user to identify a nodule and to determine whether it is a GGO or solid nodulein order to use the appropriate characterization too.
	AVIEW
	The AVIEW is a software product which can be installed on a PC. It shows images taken with theinterface from various storage devices using DICOM 3.0 which is the digital image andcommunication standard in medicine. It also offers functions such as reading. Manipulation,analyzing, post-processing, saving and sending images by using the software tools.
	Providing ray sumimage, axial, sagittal,coronal, and obliqueplanes.	Providing axial,sagittal, coronal, andoblique planes	Providing axial,sagittal, coronal, andoblique planes	Providing ray sumimage, axial, sagittal,coronal, and obliqueplanes
	Rotating to Anterior,Posterior, Left, Right,Head, and Footdirection	Rotating to Anterior,Posterior, Left, Right,Head, and Footdirection	Rotating to Anterior,Posterior, Left, Right,Head, and Footdirection	Rotating to Anterior,Posterior, Left,Right, Head, andFoot direction
	Providing VR (Volumerender), MIP(Maximum IntensityProjection), MinIP(Minimum IntensityProjection) image	Providing Average,MIP, VIP, MinIP,SurfaceMIP, Vol,Rend.	Providing Average,MIP, VIP, MinIP,SurfaceMIP, Vol,Rend.	Providing VR(Volume render),MIP (MaximumIntensity Projection),MinIP (MinimumIntensity Projection)image
	Changing the colorand transparency ofthe VR image byadjusting the OTF(Opacity TransferFunction) and savingas a preset to easilyapply in the VRsetting.	Changing the colorand transparency ofthe VR image	Changing the colorand transparency ofthe VR image	Changing the colorand transparency ofthe VR image byadjusting the OTF(Opacity TransferFunction) and savingas a preset to easilyapply in the VRsetting.
	2D and 3D images	2D and 3D images	2D and 3D images	2D and 3D images
review	review	review	review
2D and 3Dcomparative review2D and 3DmeasurementsSegmentation ofLungs and Lobes	2D and 3Dcomparative review2D measurementsSegmentation of lungairway, lungs and lunglobes	2D and 3Dcomparative review2D measurementsSegmentation of lungairway, lungs and lunglobes	2D and 3Dcomparative review2D and 3DmeasurementsSegmentation oflung airway, lungsand lung lobes
Nodule CharacteristicsAutomatic calculationof measurements foreach segmented nodule· Size of the Majoraxis and Minoraxis(mm)· Diameter of Major(3D), 2nd Major(3D), Major(2D),Minor(2D) (mm)· Volume(mm³)· Max, Min, Mean HUof the nodule((HU)· Cancer probability(%)	Nodule CharacteristicsAutomatic calculationof measurements foreach segmented nodule· Short axis-Longestdiameterperpendicular to thelong axis on theslice(mm)· Loung Axis-Longestdiameter on an axialslice(mm)· Average/Max3D/Effectivediameter(mm)· Volume(mm³) Meandensities(HU)	Nodule CharacteristicsAutomatic calculationof measurements foreach segmented nodule· Volume(mm³)· Meandiameter(mm)· Maximumdiameter(mm)· Short axisdiameter(mm)· Average/minimum/maximum densities(HU)	-
Comparison andMatchingComparison andmatching automaticcalculations betweeneach follow-up scanand the baseline scan· Doubling time indays· Indicated the changeof the size· Auto generate Lung-RADS	Comparison andMatchingComparison andmatching automaticcalculations betweeneach follow-up scanand the baseline scan· Doubling time indays· Percent (%) andabsolute change ofall numericalparameters (growthin nodule long axis,short axis, averagediameter, max 3Ddiameter, effectivediameter, volume,mean HU)	Comparison andMatchingComparison andmatching automaticcalculations betweeneach follow-up scanand the baseline scan· Elapsed time indaysDoubling time indays Percent (%)growth in nodulevolume	-
Loading multiplestudies	Loading multiplestudiesUp to 3 studies	Loading multiplestudiesUp to 3 studies	-
Workflow· Detect and Segment· Comparison andMatching	Workflow· Detect and Segment· Comparison andMatching	Workflow· Detect and Segment· Point-and-clickdetection	-
ResultsOption to integratewith 3rd party CADwhich automaticallydetects the nodules andgenerate report.		contouring Automatedmeasurements Manual correction
Supporting Low-doseCT	Supporting Low-doseCT	Supporting Low-doseCT	-
Reporting resultsThe results include thefollowing.Lung-RADS PANCAN riskcalculator Auto detect nodulelocation by lobe	Reporting resultsThe results include thefollowing.Patient relatedinformation Dictation Table withNodule result tableand additionalfindings Lung-RADS Risk Calculator	Reporting resultsThe results include thefollowing.Dictation Table withNodule result table Lung-RADS Fleischer Criteria	-
Printing Option	Printing Option	Printing Option	Printing Option

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PERFORMANCE DATA 8

8.1 Nonclinical Performance Testing

This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device. The substantial equivalence of the device is supported by the non-clinical testing

8.2 Software Verification and Validation

Verification, validation and testing activities were conducted to establish the performance, functionality and reliability characteristics of the modified device passed all of the tests based on pre-determined Pass/Fail criteria.

-Unit test
Conducting Unit Test using Google C++ Unit Test Framework on major software components identified by software development team. List of Unit Test includes Functional test condition for software component unit, Performance test condition, and part of algorithm analysis for image processing algorithm.
System test -
In accordance with the document 'integration Test Cases' discussed in advanced by software development team and test team, test is conducted by installing software to hardware with recommended system specification. Despite Test case recognized in advance was not in existence. New software error discovered by 'Exploratory Test' conducted by test team will be registered and managed as new test case after discussion between development team and test team.

Discovered software error will be classified into 3 categories as severity and managed.

Major defects, which are impacting the product's intended use and no workaround is available.

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く Moderate defects, which are typically related to user-interface or general quality of product, while workaround is available.
イ Minor defects, which aren't impacting the product's intended use. Not significant.

Success standard of System Test is not finding 'Major', 'Moderate' defect.

Nodule Matching test with Lung Registration
Experiments to check the accuracy of Nodule-Matching using Lung Registration Result in LungScreen Followup Study and to check the applicability of Registry

Based on the experimented data deployed, the steps below are performed for all 28 location.

1. Enter the Nodule position of the Fixed image
1. Convert to the position of the moving image using DVF
Measure the Voxel Distance error between the converted position and the Nodule position of the 3. Moving image.
1. Start-up verification of the cross-sectional images of the position and the converted position in the Fixed, Moving images.

Validation on DVF Size Optimization with Sub-sampling test To reduce the capacity of DVF calculated after LungRegistration, check the accuracy level of loss

when using DVF subsampling and check the possibility

For each Lung Part, three (Rigid, NonRigid, and LevelSet) DVF files are calculated for each Left and Right Lung. Because each DVF has about 600MB of files size, you will use 3.6GB (23600MB) per case. Therefore, accuracy needs to be explored how to optimize the size of the DVF at the expense of the loss.

Based on the experimental data deployed, the steps below ae performed for all 28 locations.

1. Enter the Nodule position of the Fixed image.
1. Use DisplacementVector placed on above position and replace with the Moving image.
1. Convert to the position of the moving image using the Mean DisplacementVector in the 3x3x3 area around that location
1. Measure the Voxel Distance error between each converted position and the Nodule position of the Moving image.
1. Measure the error of each converted position
Semi-automatic Nodule Segmentation

In order to check the accuracy of the measured length and volume value in the node added by Segmentation.

Create a sphere with a radius of 2mm, 3mm, 6mm, 7mm, 8mm, 9mm, 10mm. Providing test function to read measurement of split decision using the node segmentation function. Standard judgment:

The measured length should be less than one voxel size compared to the size of the sphere produced. The measured volume should be within 10 error compared to the volume of the sphere created.

Mevis CAD Integration test -

Confirm Data Transfer and CAD Results SR DICOM Analysis.

1. Confirm if CT DIOM is sent from AVIEW to MeVis CAD
1. Confirm that the CAD result SR DICOM is sent from the MeVis CAD to the AVIEW

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1. Confirm that it is displayed on the AIVEW Lung Screen by analyzing the contents of the MeVis CAD result SR DIOM
  Install AVIEW and MeVis CAD software on each of the two PCs, and set up each environment
Brock Score (aka. PANCAN) Risk Calculation test
Generate some sample data, conduct a unit test by comparing the calculate value calculated in a separate Excel sheet with the result value of the implemented code.

Summary of both publications related to this function: 'Probability of Cancer in Pulmonary Nodules Detected on First Screening CT' and 'The Vancouver Lung Cancer Risk Prediction Model: Assessment by Using a Subset of the National Lung Screening Trial Cohort' concludes that the risk calculator yielded a high discriminant value, which supports the user of risk calculator method as a valuable approach to distinguish between benign and malignant nodules.

Test Data used for each paper were as below

Former paper used PanCan data set, 187 persons had 7008 nodules, of which 102 were malignant, ● and in the BCCA data set, 1090 persons had 5021 nodules, of which 42 were malignant.
The latter used 4431 nodules (4315 benign nodules and 116 malignant nodules of NLST data)
VDT Calculation test Confirmed that the VDT calculation is going well by using unit tests.
-Lung RADS Calculation test

Test and verify 10 cases were extracted from the Lung-RADS survey table provided by the Korean Society of Thoracic Radiology.

Confirm that it was implemented in accordance with Lung-RADS regulations by using unit tests.

-Performance test
In order to check whether the non-functional requirement indicated in section 'Performance and Non-Functional Requirements is satisfied, operate a test according to the performance evaluation standard and method that has been determined with prior consultation between software development team and testing team
-Auto segmentation (based on deep-learning algorithms) test
Assessment method on Koeran Data
Chest CT data taken with 192 suspected COPD patients. "
. Automatic segmentation of lung and lobe is applied using AVIEW LCS to generate segmentation results.
. The results of auto-segmentation are identified by a specialist and radiologist and classified as 0 (Not good), 1 (need adjustment), and 2(very good)

Assessment method on NLST Data

80 patient's Chest CT data who were enrolled in NLST. "
י Automatic segmentation of lung and lobe is applied using AVIEW to generate segmentation results.
. Manual segmentation of lung and lobe is performed by experienced radiolograhper and confirmed by experienced radiologist.
The dice similarity coefficient is performed to check how similar they are.

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CONCLUSIONS 9

The new device and predicate device are substantially equivalent in the areas of technical characteristics, general functions, application, and intended use. The new device does not introduce a fundamentally new scientific technology, and the nonclinical tests demonstrate that the device is safe and effective. Therefore, it is our opinion that the AVIEW LCS described in this substantially equivalent to the predicate device.

Regulation Number and Section

§ 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).