AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This software can be used to support the physician providing quantitative analysis of CT images by image segmentation of sub-structures in the lung, lobe, airways, fissures completeness, cardiac, density evaluation, and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on-premises and as a cloud environment to allow users to connect by various environments such as mobile devices and Chrome browsers. Converts the sharp kernel for quantitative analysis of segmenting low attenuation areas of the lung. Characterizing nodules in the lung in a single study or over the time course of several thoracic studies. Characterizations include type, location of the nodule, and measurements such 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 measure in 3D for a solid portion of the nodule, Solid 2nd Major: The size of the solid part, measured in sections perpendicular to 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, integrate with FDA certified Mevis CAD (Computer aided detection) (K043617). It also provides the Agatston score, and mass score by the whole and each artery by segmenting four main arteries (right coronary artery, left main coronary, left anterior descending, and left circumflex artery). Based on the calcium score provides CAC risk based on age and gender. The device is indicated for adult patients only.

Device Description

The AVIEW is a software product that can be installed on a PC. It shows images taken with the interface from various storage devices using DICOM 3.0, the digital image and communication standard in medicine. It also offers functions such as reading, manipulation, analyzing, post-processing, saving, and sending images by using software tools. And is intended for use as a quantitative analysis of CT scanning. It provides the following features such as segmentation of lung, lobe, airway, fissure completeness, semi-automatic nodule management, maximal plane measures and volumetric measures, automatic nodule detection by integration with 3rd party CAD. It also provides the Brocks 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 are based on type, size, size, size, size, size, size, size, size, size, size change, and other findings that are reported. It also provides a calcium score by automatically analyzing coronary arteries from the segmented arteries

AI/ML Overview

The provided document does not contain specific acceptance criteria and detailed study results for the AVIEW device that would allow for the construction of the requested table and comprehensive answer. The document primarily focuses on demonstrating substantial equivalence to a predicate device and briefly mentions software verification and validation activities.

However, I can extract the information that is present and highlight what is missing.

Here's an analysis based on the provided text, indicating where information is present and where it is absent:

Acceptance Criteria and Device Performance (Partial)

The document mentions "pre-determined Pass/Fail criteria" for software verification and validation, but it does not explicitly list these criteria or the numerical results for them. It broadly states that the device "passed all of the tests."

Table of Acceptance Criteria and Reported Device Performance

Feature/Metric	Acceptance Criterion	Reported Device Performance
General Software Performance	Passed all tests based on pre-determined Pass/Fail criteria	Passed all tests
Unit Test	Successful functional, performance, and algorithm analysis for image processing algorithm components	Tests conducted using Google C++ Unit Test Framework
System Test (Defects)	No 'Major' or 'Moderate' defects found	No 'Major' or 'Moderate' defects found (implies 'Passed' for this criterion)
Kernel Conversion (LAA result reliability)	LAA result on kernel-converted sharp image should have higher reliability with soft kernel than LAA results on sharp kernel image not applying Kernel Conversion.	Test conducted on 96 total images (53 US, 43 Korean). (Result stated as 'A', indicating this was a test conducted but no specific performance metric is given for how much higher the reliability was).
Fissure Completeness	Compared to radiologists' assessment	Evaluated using Bland-Altman plots; Kappa and ICC reported. (Specific numerical results are not provided).

Detailed Breakdown of Study Information:

A table of acceptance criteria and the reported device performance:
- Acceptance Criteria: Not explicitly stated with numerical targets. The document mentions "pre-determined Pass/Fail criteria" for software verification and validation and "Success standard of System Test is not finding 'Major', 'Moderate' defect." For kernel conversion, the criterion is stated qualitatively (higher reliability). For fissure completeness, it's about comparison to radiologists.
- Reported Device Performance:
  - General: "passed all of the tests."
  - System Test: "Success standard... is not finding 'Major', 'Moderate' defect."
  - Kernel Conversion: "The LAA result on kernel converted sharp image should have higher reliability with the soft kernel than LAA results on sharp kernel image that is not Kernel Conversion applied." (This is more of a hypothesis or objective rather than a quantitative result here).
  - Fissure Completeness: "The performance was evaluated using Bland Altman plots to assess the fissure completeness performance compared to radiologists. Kappa and ICC were also reported." (Specific numerical values for Kappa/ICC are not provided).
Sample sizes used for the test set and the data provenance:
- Kernel Conversion: 96 total images (53 U.S. population and 43 Korean).
- Fissure Completeness: 129 subjects from TCIA (The Cancer Imaging Archive) LIDC database.
- Data Provenance: U.S. and Korean populations for Kernel Conversion, TCIA LIDC database for Fissure Completeness. The document does not specify if these were retrospective or prospective studies. Given they are from archives/databases, they are most likely retrospective.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not specified in the provided text. For Fissure Completeness, it states "compared to radiologists," but the number and qualifications of these radiologists are not detailed.
Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not specified in the provided text.
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:
- Not specified. The document mentions "compared to radiologists" for fissure completeness, but it does not detail an MRMC study comparing human readers with and without AI assistance for measuring an effect size of improvement.
If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, the performance tests described (e.g., Nodule Matching, LAA Comparative Experiment, Semi-automatic Nodule Segmentation, Fissure Completeness, CAC Performance Evaluation) appear to be standalone evaluations of the algorithm's output against a reference (ground truth or expert assessment), without requiring human interaction during the measurement process by the device itself.
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- For Fissure Completeness, the ground truth appears to be expert assessment/consensus from radiologists implied by "compared to radiologists."
- For other performance tests like "Nodule Matching," "LAA Comparative Experiment," "Semi-automatic Nodule Segmentation," "Brock Model Calculation," etc., the specific type of ground truth is not explicitly stated. It's likely derived from expert annotations or established clinical metrics but is not detailed.
The sample size for the training set:
- Not specified in the provided text. The document refers to "pre-trained deep learning models" for the predicate device, but gives no information on the training data for the current device.
How the ground truth for the training set was established:
- Not specified in the provided text.

Summary of Missing Information:

The document serves as an FDA 510(k) summary, aiming to demonstrate substantial equivalence to a predicate device rather than providing a detailed clinical study report. Therefore, specific quantitative performance metrics, detailed study designs (e.g., number and qualifications of readers, adjudication methods for ground truth, specifics of MRMC studies), and training set details are not included.

Summary

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services seal on the left and the FDA acronym and name on the right. The FDA acronym is in a blue square, and the full name "U.S. Food & Drug Administration" is in blue text.

Coreline Soft Co., Ltd. % Hye Yi Park RA Manager 4,5F(Yeonnam-dong), 49, World Cup buk-ro 6-gil Mapo-gu Seoul, 03991 KOREA

Re: K214036

Dec. 23, 2022

Trade/Device Name: AVIEW Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH, JAK Dated: November 25, 2022 Received: November 28, 2022

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.

Wenbo Li

for Jessica Lamb

Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Ouality Center for Devices and Radiological Health

Enclosure

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Indications for Use

Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2023 See PRA Statement below.

510(k) Number (if known)

K214036

Device Name AVIEW

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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510(k) Summary

K214036

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: 12.21.2022

DEVICE 2

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: QIH, JAK

3 PREDICATE DEVICE

AVIEW by Coraline Soft Co., Ltd. (K200714)

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, JAK

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

REFERENCE DEVICE 4

ClariCT.AI by ClariPI Inc.(K183460)

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

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Broncholab by Fluidda Inc.(K191550)

Name of Device: Broncholab Common or Usual Name: Image Processing Software Classification Name: System, X-Ray, Tomography, Computed (21CFR 892.1750) Regulatory Class: II Product Code: JAK

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

DEVICE DESCRIPTION 5

The AVIEW is a software product that can be installed on a PC. It shows images taken with the interface from various storage devices using DICOM 3.0, the digital image and communication standard in medicine. It also offers functions such as reading, manipulation, analyzing, post-processing, saving, and sending images by using software tools. And is intended for use as a quantitative analysis of CT scanning. It provides the following features such as segmentation of lung, lobe, airway, fissure completeness, semi-automatic nodule management, maximal plane measures and volumetric measures, automatic nodule detection by integration with 3rd party CAD. It also provides the Brocks 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 are based on type, size, size, size, size, size, size, size, size, size, size, size, size change, and other findings that are reported. It also provides a calcium score by automatically analyzing coronary arteries from the segmented arteries

INDICATIONS FOR USE 6

AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This software can be used to support the physician providing quantitative analysis of CT images by image segmentation of sub-structures in the lung. lobe, airways, fissures completeness, cardiac, density evaluation, and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on-premises and as a cloud environment to allow users to connect by various environments such as mobile devices and Chrome browsers. Converts the soft kernel for quantitative analysis of segmenting low attenuation areas of the lung. Characterizing nodules in the lung in a single study or over the time course of several thoracic studies. Characterizations include 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), Max HU, mass(mass calculated from the CT pixel value), and volumetric measures(Solid major, length of the longest diameter measure in 3D for a solid portion of the nodule, Solid 2nd Major: The size of the solid part, measured in sections perpendicular to the Major axis 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, integrate with FDA certified Mevis CAD (Computer aided detection) (K043617). It also provides the Agatston score, and mass score by the whole and each artery by segmenting four main arteries (right coronary artery, left anterior descending, and left circumflex artery). Based on the calcium score provides CAC risk based on age and gender. The device is indicated for adult patients only.

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COMPARISION OF TECHNOLOGICAL CHARACTERISTICS WITH 7 THE PREDICATE DEVCIE

AVIEW has the same intended use and the principle of operation and has similar features to the predicate devices. AVIEW (K200714)

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	Predicate Device	Reference Device	Reference Device
Device Name	AVIEW	AVIEW	ClariCT.AI	Broncholab
ClassificationName	System, imageProcessingRadiological	System, imageProcessingRadiological	System, imageProcessingRadiological	System, X-Ray,Tomography,Computed
RegulatoryNumber	21 CFR 892.2050	21 CFR 892.2050	21 CFR 892.2050	21 CFR 892.1750
Product Code	QIH, JAK	LLZ, JAK	LLZ	JAK
Review Panel	Radiology	Radiology	Radiology	Radiology
510k Number	-	K200714	K183460	K191550
Indications foruse	AVIEWAVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. Thissoftware can be used to support the physician providing quantitative analysis of CT images byimage segmentation of sub-structures in the lung, lobe, airways, fissures completeness, cardiac,density evaluation, and reporting tools. AVIEW is also used to store, transfer, inquire anddisplay CT data set on-premises and as a cloud environment to allow users to connect byvarious environments such as mobile devices and Chrome browsers. Converts the sharp kernelto soft kernel for quantitative analysis of segmenting low attenuation areas of the lung.Characterizing nodules in the lung in a single study or over the time course of several thoracicstudies. Characterizations include nodule type, location of the nodule, and measurements suchas 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 inHU), Minimum HU, Max HU, mass(mass calculated from the CT pixel value), and volumetricmeasures(Solid major; length of the longest diameter measure in 3D for a solid portion of thenodule, Solid 2nd Major: The size of the longest diameter of the solid part, measured in sectionsperpendicular to the Major axis of the solid portion of the nodule), VDT (Volume doublingtime), and Lung-RADS (classification proposed to aid with findings.) ). The systemautomatically performs the measurement, allowing lung nodules and measurements to bedisplayed and, integrate with FDA certified Mevis CAD (Computer aided detection)(K043617). It also provides the Agatston score, volume score, and mass score by the whole andeach artery by segmenting four main arteries (right coronary artery, left main coronary, leftanterior descending, and left circumflex artery). Based on the calcium score provides CAC riskbased on age and gender. The device is indicated for adult patients only.

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GeneralDescription	evaluation and documentation of CT lung tissue images by providing image segmentation ofsub-structures in lung, lobe, airways and cardiac, registration of inspiration and expirationwhich could analyze quantitative information such as air trapping volume, air trapped indexand inspiration/expiration ratio. And, volumetric and structure analysis, density evaluation andreporting tools. AVIEW is also used to store, transfer, inquire and display CT data set onpremise and as cloud environment as well to allow users to connect by various environmentsuch as mobile devices and chrome browser. Characterizing nodules in the lung in a singlestudy, or over the time course of several thoracic studies. Characterizations include nodule type,location of the nodule and measurements such as size (major axis, minor axis), estimatedeffective diameter from the volume of the nodule, volume of the nodule, Mean HU(the averagevalue of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass(mass calculatedfrom the CT pixel value), and volumetric measures(Solid major; length of the longest diametermeasured in 3D for solid portion of the nodule, Solid 2nd Major: The length of the longestdiameter of the solid part, measured in sections perpendicular to the Major axis of the solidportion of the nodule), VDT (Volume doubling time), and Lung-RADS (classification proposedto aid with findings). The system automatically performs the measurement, allowing lungnodules and measurements to be displayed and, integrate with FDA certified Mevis CAD(Computer aided detection) (K043617).It also provides CAC analysis by segmentation of fourmain artery (right coronary artery, left main coronary, left anterior descending and leftcircumflex artery then extracts calcium on coronary artery to provide Agatston score, volumescore and mass score by whole and each segmented artery type. Based on the score, providesCAC risk based on age and gender.
	ClariCT.AIClariCT.AI, is a software device intended for networking, communication, processing, andenhancement of CT images in DICOM format regardless of the manufacturer of CT scanner ormodel.
	Broncholab
	Broncholab provides physicians with reproducible CT values for pulmonary tissue forproviding quantitative support for diagnosis and follow-up examination. Broncholab can beused to support physicians in the diagnosis and documentation of pulmonary tissues images(e.g., abnormalities) from CT thoracic datasets. Three-D segmentation and isolation ofsubcompartments, volumetric analysis, density evaluations, low density cluster analysis,fissure evaluation and reporting tools are combined with a dedicated workflow.
	AVIEW
GeneralDescription	The AVIEW is a software product that can be installed on a PC. It shows images taken with theinterface from various storage devices using DICOM 3.0, the digital image and communicationstandard in medicine. It also offers functions such as reading, manipulation, analyzing, post-processing, saving, and sending images by using software tools. And is intended for use as aquantitative analysis of CT scanning. It provides the following features such as segmentationof lung, lobe, airway, fissure completeness, semi-automatic nodule management, maximalplane measure, 3D measures and volumetric measures, automatic nodule detection byintegration with 3rd party CAD. It also provides the Brocks model, which calculates themalignancy score based on numerical or Boolean inputs. Follow-up support with automatednodule matching and automatically categorize Lung-RADS score, which is a quality assurancetool designed to standardize lung cancer screening CT reporting and managementrecommendations that are based on type, size, size change, and other findings that are reported.It also provides a calcium score by automatically analyzing coronary arteries from thesegmented arteries.
	AVIEWThe AVIEW is a software product which can be installed on a PC. It shows images taken withthe interface from various storage devices using DICOM 3.0 which is the digital image and
communication standard in medicine. It also offers functions such as reading, manipulation,
analyzing, post-processing, saving, and sending images by using the software tools. And is
intended for use as diagnostic patient imaging which is intended for the review and analysis of
CT scanning. Provides following features as semi-automatic nodule management, maximal
plane measure, 3D measures and columetric measures, automatic nodule detection by
integration with 3rd party CAD. Also provides Brocks model which calculated 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. It also automatically
analyzes coronary artery calcification which support user to detect cardiovascular disease in
early stage and reduce the burden of medical.
Callery of Children Company of Children

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ClariCT.AI

ClariCT.AI software is intended for denoise processing and enhancement of CT DICOM images when higher image quality and/or lower dose acquisitions are desired. ClariCT.AI software can be used to reduce noises in CT images of the head, chest, and abdomen, in particular in CT images with a lower radiation dose. ClariCT.AI may also improve the image quality of low dose nonpdiagnostic Filtered Back Projection images as well as Iterative Reconstruction images.

The system enables the receipt of DICOM images from CT imaging devices (modalities), enables their denoise processing and enhancement, and transmission to a PACS workstation.

Broncholab

Broncholab is a SaMD (Software as Medical Device) which provides quantitative CT values that are intended to support the physician in the diagnosis and documentation of pulmonary tissues images (abnormalities) from CT scans. The CT scan images are transformed into 3D models of the patient-specific lungs using several image processing steps. Broncholab can be used to assess the effectiveness of therapy based on CT scan data. It is used along with the following accessories:

Web Portal: Enables the uploading of CT scans and patient data
. Client Report: Enables the conversion of the output of Broncholab (CT values) into a desired digital format (PDF

Report) and transfers this Report to the physician via email.

Inspiratory CT scan images uploaded by the users are converted into quantitative CT values using a combination of software tools. Quality checks (both manual and automated) are implemented to assure the quality of the final data.

The outputs are provided as absolute values and as a percentage of the total airway volume/ lung volume/ lobar volume depending on the parameter. The device can be used on a computer with a web browser installed and consists of two accessories:

An online portal to upload the CT scans
. An accessory that enables the creation of the Report

The CT values include:

Lung and Lobar Volume is the volume of the 3D model of each lung lobe. 1)
1. Airway Volume is defined as the region from the trachea until the segmental bronchi.
Lung Density Scores/ Volumes is defined as all the intrapulmonary voxels with 3) Hounsfield Units between -1024

and -950 using the inspiratory scans:

. Low attenuation areas below -950 HU (LAA-950HU)
. 15th percentile of density histogram: Percentile density (PD) can also be used to express Emphysema.
Blood vessel density: Blood vessel density can be determined through segmentation o and 3-D reconstruction of the blood vessels. The segmentation is based on local

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geometry features and HU thresholds and is performed on the inspiratory CT scan.4) Fissure Analysis (fissure integrity) is the percentage of completeness of the fissure.Lung fissures are a doublefold of visceral pleura that either completely orincompletely separates the lungs into lung lobes.
CT scan images must be DICOM 3.0 compliant.
Platform	IBM-compatible PC or PC network	same
User Interface	Monitor, Mouse, Keyboard	same
Image Input Sources	Images can be scanned, loaded from card readers, or imported from a radiographic imaging device	same
Image format	DICOM	same
Image Measurement Tools	Ruler (line and 3D), Tapeline (curve, poly and3D), Angle (3-point, 4point, and 3D), pixel values, area of ROI (rectangle, circle, ellipse), volume	same
Image viewing	Axial, sagittal, and coronal image, oblique slice, cube view	same
Image manipulation	Panning, rotating, zooming, windowing, inverting, Coloring, Oblique, Note (text overlay), Coloring	same
DICOM	This receives DICOM data from CT by DICOM communicationConducts DICOM data communication with PACS. It also imports DICOM file directly, saves by using export function.	same
Lung Analysis Functions	Fully automatic lungs, lobes and airways segmentation using deep-learning algorithms	Fully automatic lungs, lobes and airways segmentation using deep-learning algorithms
	Semi-automatic segmentation of lungs, lobes, and airways.	same
	Visualization of multi-	same
planar reconstructed(MPR) images and 3Drendered images, withcolor-definedHounsfield Unit (HU)ranges.
Calculation of LAA(Lower AttenuationArea) index with HUdensity histogram.Volume measurementsand percentile index	same
Calculation of LAAcluster sizedistribution with D-slope	same
Graphicalvisualization of theabove quantificationresults for reporting	same
Export ofquantification resultsto CSV tables	same
Visualization ofLAA% for each of 5lobes	same
Measurements of theairway branches, suchas, lumen area and wallarea	same
Analyzes Air TrappingIndex by registrationof inspiration andexpiration data. Couldcompare both IN/EXafter the registration	same
Fully automaticINSP/EXP registration(non-rigid elastic)algorithm.	same
Quantiatively evaluatefissure integrity ratioand display it byprojected fissureimage and 3D screenand chart.		Fissure Analysis(fissure integrity) isthe percentage ofcompleteness of thefissure. Lung fissuresare a doublefoldof visceral pleurathat eithercompletely orincompletelyseparates the lungsinto lung lobes

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core:line__

	Nodule Characteristics
Lung CancerScreening	Automatic calculationof measurements for each segmented nodule Size of the Major axis and Minor axis(mm) Diameter of Major (3D), 2nd Major (3D), Major(2D), Minor(2D) (mm) Volume(mm³) Max, Min, Mean HU of the nodule((HU) Cancer probability (%)	same
	Comparison and MatchingComparison and matching automatic calculations between each follow-up scan and the baseline scan Doubling time in days Indicated the change of the size Auto generate Lung-RADS	same
	Loading multiple studies	same
	Workflow Detect and Segment Comparison and Matching Results Option to integrate with 3rd party CAD which automatically detects the nodules and generate report Supporting Low-dose CT	same
	Reporting resultsThe results include the following. Lung-RADS	same
	PANCAN risk calculator Auto detect nodule location by lobe
	Supports kernel conversion of LAA on LCS Page		Noise reduction is performed with the use of pre-trained deep learning models.
Cardiac (CAC)	Extracting Calcium on Coronary Artery and provides Agatston score, volume score and mass score.	same
	Automatically segments calcium area of coronary artery based on deep learning.	same
Thin client service	Connected from anywhere, anyplace, anytime Supports mobile view through various mobile devices served by ios and Android. Comparable with Chrome browser	same


Easy processing management	Rule-based automatic processing server (APS)	same

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8 PERFORMANCE DATA

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

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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 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. >
V Moderate defects, which are typically related to user-interface or general quality of product, while workaround is available.

V Minor defects, which aren't impacting the product's intended use. Not significant. Success standard of System Test is not finding 'Major', 'Moderate' defect.

Performance Test -
- · Nodule Matching Experiment Using Lung Registration
- · LAA Comparative Experiment for Performance Evaluation of Denoised CT
- Semi-automatic Nodule Segmentation .
- Brock Model (ask PANCAN) Calculation .
- . VDT Calculation
- Lung RADS Calculation .
- . MeVis CAD Integration
- Validation LAA Analysis ●
- Validation LAA Size Analysis ●
- . Size analysis algorithm of LAA clusters
- Lung Registration .
- Reliability Test for Airway wall Measurement .
- . Fissure Completeness
- CAC Performance Evaluation of Denoised CT ●
- Validation on DVF Size Optimization with Sub-sampling .
- Airway Segmentatation ●
- · Auto Lung & Lobe Segmentation
- Kernel Conversion .
  - A The LAA result on kernel converted sharp image should have higher reliability with the soft kernel than LAA results on sharp kernel image that is not Kernel Conversion applied. Of the 96 total, 53 are U.S. population and 43 are Korean.
- . Fissure Completeness
  - A Fissure completeness was validated using a total of 129 subjects from TCIA (the Cancer Imaging Archive) LIDC database. The performance was evaluated using Bland Altman plots to assess the fissure completeness performance compared to radiologists. Kappa and ICC were also reported.

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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 described in this submission is 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).