The AVIEW Modeler is intended for use as an image review and segmentation system that operates on DICOM imaging information obtained from a medical scanner. It is also used as a pre-operative software for surgical planning. 3D printed models generated from the output file are for visualization and educational purposes only and not for diagnostic use.

Device Description

The AVIEW Modeler is a software product which can be installed on a separate PC, it displays patient medical images on the screen by acquiring it from image Acquisition Device. The image on the screen can be checked edited, saved and received.

-Various displaying functions
- Thickness MPR., oblique MPR, shaded volume rendering and shaded surface rendering.
- . Hybrid rendering of simultaneous volume-rendering and surface-rendering.
-Provides easy to-use manual and semi-automatic segmentation methods
- Brush, paint-bucket, sculpting, thresholding and region growing. "
- . Magic cut (based on Randomwalk algorithm)
-Morphological and Boolean operations for mask generation.
Mesh generation and manipulation algorithms. -
- Mesh smoothing, cutting, fixing and Boolean operations.
-Exports 3d-printable models in open formats, such as STL.
DICOM 3.0 compliant (C-STORE, C-FIND) -

AI/ML Overview

The provided text describes the 510(k) Summary for AVIEW Modeler, focusing on its substantial equivalence to predicate devices, rather than a detailed performance study directly addressing specific acceptance criteria. The document emphasizes software verification and validation activities.

Therefore, I cannot fully complete all sections of your request concerning acceptance criteria and device performance based solely on the provided text. However, I can extract information related to software testing and general conclusions.

Here's an attempt to answer your questions based on the available information:

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

The document does not provide a quantitative table of acceptance criteria with corresponding performance metrics like accuracy, sensitivity, or specificity for the segmentation features. Instead, it discusses the successful completion of various software tests.

Acceptance Criteria (Implied)	Reported Device Performance
Functional Adequacy	"passed all of the tests based on pre-determined Pass/Fail criteria."
Performance Adequacy	Performance tests conducted "according to the performance evaluation standard and method that has been determined with prior consultation between software development team and testing team" to check non-functional requirements.
Reliability	System tests concluded "not finding 'Major'. 'Moderate' defect."
Compatibility	STL data created by AVIEW Modeler was "imported into Stratasys printing Software, Object Studio to validate the STL before 3d-printing with Objet260 Connex3." (implies successful validation for 3D printing)
Safety and Effectiveness	"The new device does not introduce a fundamentally new scientific technology, and the nonclinical tests demonstrate that the device is safe and effective."

2. Sample sizes used for the test set and the data provenance

The document does not specify the sample size (number of images or patients) used for any of the tests (Unit, System, Performance, Compatibility). It also does not explicitly state the country of origin of the data or whether the data was retrospective or prospective.

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

The document does not provide any information about the number or qualifications of experts used to establish ground truth for a test set. The focus is on internal software validation and comparison to a predicate device.

4. Adjudication method for the test set

The document does not mention any adjudication method for a test set, as it does not describe a clinical performance study involving human readers.

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

No, the provided text does not describe an MRMC comparative effectiveness study involving human readers with or without AI assistance. The study described is a software verification and validation, concluding substantial equivalence to a predicate device.

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

The document describes various software tests (Unit, System, Performance, Compatibility) which could be considered forms of standalone testing for the algorithm's functionality and performance. However, it does not present quantitative standalone performance metrics typical of an algorithm-only study (e.g., precision, recall, Dice score for segmentation). It focuses on internal software quality and compatibility.

7. The type of ground truth used

The type of "ground truth" used is not explicitly defined in terms of clinical outcomes or pathology. For the software validation, the "ground truth" would likely refer to pre-defined correct outputs or expected behavior of the software components, established by the software development and test teams. For example, for segmentation, it would be the expected segmented regions based on the algorithm's design and previous validation efforts (likely through comparison to expert manual segmentations or another validated method, though not detailed here).

8. The sample size for the training set

The document does not mention a training set or its sample size. This is a 510(k) summary for a medical image processing software (AVIEW Modeler), and while it mentions a "Magic cut (based on Randomwalk algorithm)," it does not describe an AI model that underwent a separate training phase with a specific dataset, nor does it classify the device as having "machine learning" capabilities in the context of FDA regulation. The focus is on traditional software validation.

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

As no training set is mentioned (see point 8), there is no information on how its ground truth would have been established.

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, with the letters "FDA" in a blue box, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue. The words "FOOD & DRUG" are larger than the other words.

December 20, 2019

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

Re: K192040

Trade/Device Name: AVIEW Modeler Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: October 25, 2019 Received: November 26, 2019

Dear Hyeyi 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 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-reporting

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combination-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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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) K192040

Device Name AVIEW Modeler

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."

FORM FDA 3881 (7/17)

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K192040

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: July 30.2019

DEVICE 2

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

PREDICATE DEVICE 3

D2P by 3D Systems, Inc. (K161841) This predicate has not been subject to a design-related recall

REFERENCE DEVICE 4

Mimics inPrint by Materialise N.V. (K173619) AVIEW by Coreline Soft Co., Ltd. (K171199) This reference device has not been subject to a design-related recall

5 DEVICE DESCRIPTION

-Various displaying functions
- Thickness MPR., oblique MPR, shaded volume rendering and shaded surface rendering.
- . Hybrid rendering of simultaneous volume-rendering and surface-rendering.
-Provides easy to-use manual and semi-automatic segmentation methods
- Brush, paint-bucket, sculpting, thresholding and region growing. "
- . Magic cut (based on Randomwalk algorithm)

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-Morphological and Boolean operations for mask generation.
Mesh generation and manipulation algorithms. -
- Mesh smoothing, cutting, fixing and Boolean operations.
-Exports 3d-printable models in open formats, such as STL.
DICOM 3.0 compliant (C-STORE, C-FIND) -

INDICATIONS FOR USE 6

3D printed models generated from the output file are for visualization and educational purposes only and not for diagnostic use.

COMPARISION OF TECHNOLOGICAL CHARACTERISTICS WITH 7 THE PREDICATE DEVCIE

AVIEW Modeler has the same intended use and principle of operation, and also has similar features to the predicate devices, D2P(K161841)

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 Predicate Device	Reference Device	Reference Device
Device Name	AVIEW Modeler	D2P	Mimics inPrint	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	LLZ	LLZ	LLZ
Review Panel	Radiology	Radiology	Radiology	Radiology
510k Number	-	K161841	K173619	K171199
Indications foruse	The AVIEW Modeleris intended for use as asoftware interface andimage segmentationsystem that sendDICOM imaginginformation throughoutput file on amedical scanner. 3Dmodel and 3D printedmodels generated byour software can also	The D2P Software isintended for use as asoftware interface andimage segmentationsystem for the transferof imaginginformation from amedical scanner suchas a CT scanner to anoutput file it is alsointended as pre-operative software for	Mimics inPrint isintended for use as asoftware interface andimage segmentationsystem for thetransfer of DICOMimaging informationfrom a medicalscanner to an outputfile it is also used aspre-operativesoftware for treatment	AVIEW provides CTvalues for pulmonarytissue from CTthoracic datasets. Thissoftware can be usedto support thephysicianquantitatively in thediagnosis. Follow-upevaluation anddocumentation of CTlung tissue images by
	be used for a surgicalplan and simulationuse.	surgical planning. 3Dprinted modelsgenerated from theoutput file are meantfor visual, non-diagnostic use.	planning for thispurpose, the Mimicsoutput file can beused for thefabrication ofphysical replicas ofthe output file usingtraditional or additivemanufacturingmethods.	providing imagesegmentation of sub-structures in the leftand right lung (e.g.,the five lobes andairway), volumetricand structuralanalysis, densityevaluations andreporting tools.AVIEW is also used tostore, transfer, inquireand display CT datasets. AVIEW is notmeant for primaryimage Interpretationin mammography.
Platform	IBM-compatible PCor PC network	IBM-compatible PCor PC network	IBM-compatible PCor PC network	IBM-compatible PCor PC network
User Interface	Monitor, Mouse,Keyboard	Monitor, Mouse,Keyboard	Monitor, Mouse,Keyboard	Monitor, Mouse,Keyboard
Image InputSources	Images can bescanned, loaded fromcard readers, orimported from aradiographic imagingdevice	Images can bescanned, loaded fromcard readers, orimported from aradiographic imagingdevice	Images can bescanned, loaded fromcard readers, orimported from aradiographic imagingdevice	Images can bescanned, loaded fromcard readers, orimported from aradiographic imagingdevice
32bit/64bit	64bit	64bit	32t/4bit	64bit
Image format	DICOM	DICOM	DICOM	DICOM
Image viewing	Axial, sagittal, andcoronal image,oblique slice, 3D	Axial, sagittal andcoronal images,oblique slice, 3D	Axial, sagittal andcoronal images,oblique slice, 3D	Axial, sagittal andcoronal images,oblique slice, cubeview, 3D
Imagemanipulation	Panning, rotating,zooming, windowing,Coloring, Oblique,Note (text overlay),Coloring (volume ofinterest overlay)	Panning, rotating,zooming, windowing,region of interestoverlay (ROI)	Panning, rotating,zooming, windowing,region of interestoverlay (ROI)	Panning, rotating,zooming, windowing,inverting, Coloring,Oblique, Note (textoverlay), Coloring(volume of interestoverlay)
GeneralDescription	The AVIEW Modeleris a software productwhich can be installedon a separate PC, itdisplays patientmedical images on thescreen by acquiring itfrom ImageAcquisition Device.The image on thescreen can be checkededited, saved and	The D2P software is astand-alone modularsoftware package thatallows easy to use andquick digital 3Dmodel preparation forprinting or use bythird partyapplications. Thesoftware is aimed atusage by medicalstaff, technicians,	Materialise'sInteractive Medicalimage ControlSystem (mimics) is asoftware tool forvisualizing andsegmenting medicalimages (such as CTand MRI) andrendering 3D objects.Mimics inPrint maybe used as a medical	The AVIEW is asoftware productwhich can be installedon a PC. It showsimages taken with theinterface from variousstorage devices usingDICOM 3.0 which isthe digital image andcommunicationstandard in medicine.It also offers functions
	received.	nurses, researchers orlab technicians thatlab technicians thatwish to create patientspecificdigitalanatomical models forvariety of uses such astraining, education,and pre-operativesurgical planning. Thepatient specific digitalanatomical modelsmay be further used asan input to a 3Dprinter to createphysical models forvisual, non-diagnosticuse. This modularpackage includes, butis not limited to thefollowing functions:•DICOM viewer andanalysis•Automatedsegmentation•Editing and pre-printing•Seamlessintegration with 3DSystems printers•Seamlessintegration with 3DSystems softwarepackages	device. Within thelimits of the describedbelow intended usestatement.Mimics may be usedto load and processstack of 2D imagesfrom numerousformats includingDICOM 3.0 format,BMP, TIFF, JPG andraw images. Onceimages are processed,they can be used	such as reading.Manipulation,analyzing, post-processing, saving andsending images byusing the softwaretools.
DICOM	This receives DICOMdata from CT or MRIby DICOMcommunicationConducts DICOMdata communicationwith PACS. It alsoimports DICOM filedirectly, saves byusing export function.	Retrieve image dataover the network viaDICOM	Retrieve image dataover the network viaDICOM	This receives DICOMdata from CT or MRIby DICOMcommunicationConducts DICOMdata communicationwith PACS. It alsoimports DICOM filedirectly, saves byusing export function.
3D ModelingFunctions	Providing ray sumimage, axial, sagittal,coronal, and obliqueplanes.Rotating to Anterior,Posterior, Left, Right,Head, and Footdirection	Providing axial,sagittal, coronal, andoblique planesRotating to Anterior,Posterior, Left, Right,Head, and Footdirection	Providing axial,sagittal, coronal,Rotating to Anterior,Posterior, Left, Right,Head, and Footdirection	Providing ray sumimage, axial, sagittal,coronal, and obliqueplanesRotating to Anterior,Posterior, Left, Right,Head, and Footdirection
	Providing VR	Providing VR	Providing VR	Providing VR
(Volume render), MIP (Maximum Intensity Projection), MinIP (Minimum Intensity Projection) image	(Volume render)	(Volume render)	(Volume render), MIP (Maximum Intensity Projection), MinIP (Minimum Intensity Projection) image
Changing the color and transparency of the VR image by adjusting the OTF (Opacity Transfer Function) and saving as a preset to easily apply in the VR setting.	Changing the color and transparency of the VR image by adjusting the OTF (Opacity Transfer Function) and saving as a preset to easily apply in the VR setting.	-	Changing the color and transparency of the VR image by adjusting the OTF (Opacity Transfer Function) and saving as a preset to easily apply in the VR setting.
Providing options of ambient, diffuse, specular, gradient, shininess, lighting and transient quality in the Camera setting	-	-	Providing options of ambient, diffuse, specular, gradient, shininess, lighting and transient quality in the Camera setting
Saving the segmented region from the whole volume and converting to the surface model	Saving the segmented region from the whole volume and converting to the surface model	Saving the segmented region from the whole volume and converting to the surface model	-
Saving the surface model in STL format	Saving the surface model in STL format	Saving the surface model in STL format	-
The decimation and smoothing options can be applied when saving in STL format	The fixed decimation and smoothing options can be applied when saving in STL format	The decimation and smoothing options can be applied when saving in STL format	-
Providing a model fixing function that can detect and correct errors on the surface model.	-	Model fixing of Surface model	-
Boolean operation (Union, Subtract, Intersect) and segmentation function between models.	-	Boolean operation (Union, Subtract, Intersect)	-
Creating Box, Sphere, Pipe, or Cone shape surface model	Creating Pipe shape surface model	Creating Box, Sphere, Pipe, or Cone shape surface model	-
AVIEW Modeler provide segmentation function in the CT image. Key Features; Threshold, Region Grow 3D Pick ROI	The D2P provide segmentation function in the CT image. Key Features; Threshold, Brush, (3D) Pick	The Mimics inPrint provide segmentation function in the CT image. Key Features; Threshold, Brush, Lasso, 3D	-

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Draw, Erase, SelectiveBrush, (3D) Sculpt &Paint, (2D,3D) Pick,Fill hole, Magiccut(semi-auto)	interpolates,split(semi-auto),Combine, Unite,Intersect, Subtract

PERFORMANCE DATA 8

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.
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.

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
Compatibility test -
All 3D printer software's should validate imported STL file before 3D printing. STL data which is created by AVIEW Modeler imported into Stratasys printing Software, Object Studio to validate the STL before 3d-printing with Objet260 Connex3. (data compatibility between two software's)

9 CONCLUSIONS

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 Modeler 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).