AVIEW Lung Nodule CAD is a Computer-Aided Detection (CAD) software designed to assist radiologists in the detection of pulmonary nodules (with diameter 3-20 mm) during the review of CT examinations of the chest for asymptomatic populations. AVIEW Lung Nodule CAD provides adjunctive information to alert the radiologists to regions of interest with suspected lung nodules that may otherwise be overlooked. AVIEW Lung Nodule CAD may be used as a second reader after the radiologist has completed their initial read. The algorithm has been validated using non-contrast CT images, the majority of which were acquired on Siemens SOMATOM CT series scanners; therefore, limiting device use to use with Siemens SOMATOM CT series is recommended.

Device Description

The AVIEW Lung Nodule CAD is a software product that detects nodules in the lung. The lung nodule detection model was trained by Deep Convolution Network (CNN) based algorithm from the chest CT image. Automatic detection of lung nodules of 3 to 20mm in chest CT images. By complying with DICOM standards, this product can be linked with the Picture Archiving and Communication System (PACS) and provides a separate user interface to provide functions such as analyzing, identifying, storing, and transmitting quantified values related to lung nodules. The CAD's results could be displayed after the user's first read, and the user could select or de-select the mark provided by the CAD. The device's performance was validated with SIEMENS’ SOMATOM series manufacturing. The device is intended to be used with a cleared AVIEW platform.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for the AVIEW Lung Nodule CAD, as derived from the provided document:

Acceptance Criteria and Reported Device Performance

Criteria (Standalone Performance)	Acceptance Criteria	Reported Device Performance
Sensitivity (patient level)	> 0.8	0.907 (0.846-0.95)
Sensitivity (nodule level)	> 0.8	Not explicitly stated as separate from patient level, but overall sensitivity is 0.907.
Specificity	> 0.6	0.704 (0.622-0.778)
ROC AUC	> 0.8	0.961 (0.939-0.983)
Sensitivity at FP/scan < 2	> 0.8	0.889 (0.849-0.93) at FP/scan=0.504

Study Details

1. Acceptance Criteria and Reported Device Performance (as above)

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

Test Set Size: 282 cases (140 cases with nodule data and 142 cases without nodule data) for the standalone study.
Data Provenance:
* Geographically distinct US clinical sites.
* All datasets were built with images from the U.S.
* Anonymized medical data was purchased.
* Included both incidental and screening populations.
* For the Multi-Reader Multi-Case (MRMC) study, the dataset consisted of 151 Chest CTs (103 negative controls and 48 cases with one or more lung nodules).

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

Number of Experts: Three (for both the MRMC study and likely for the standalone ground truth, given the consistency in expert involvement).
Qualifications: Dedicated chest radiologists with at least ten years of experience.

4. Adjudication method for the test set:

Not explicitly stated for the "standalone study" ground truth establishment.
For the MRMC study, the three dedicated chest radiologists "determined the ground truth" in a blinded fashion. This implies a consensus or majority vote, but the exact method (e.g., 2+1, 3+1) is not specified. It does state "All lung nodules were segmented in 3D" which implies detailed individual expert review before ground truth finalization.

5. Multi-Reader Multi-Case (MRMC) comparative effectiveness study:

Yes, an MRMC study was performed.
Effect size of human readers improving with AI vs. without AI assistance:
* AUC: The point estimate difference was 0.19 (from 0.73 unassisted to 0.92 aided).
* Sensitivity: The point estimate difference was 0.23 (from 0.68 unassisted to 0.91 aided).
* FP/scan: The point estimate difference was 0.24 (from 0.48 unassisted to 0.28 aided), indicating a reduction in false positives.
Reading Time: "Reading time was decreased when AVIEW Lung Nodule CAD aided radiologists."

6. Standalone (algorithm only without human-in-the-loop performance) study:

Yes, a standalone study was performed.
The acceptance criteria and reported performance for this study are detailed in the table above.

7. Type of ground truth used:

Expert consensus by three dedicated chest radiologists with at least ten years of experience. For the standalone study, it is directly compared against "ground truth," which is established by these experts. For the MRMC study, the experts "determined the ground truth." The phrase "All lung nodules were segmented in 3D" suggests a thorough and detailed ground truth establishment.

8. Sample size for the training set:

Not explicitly stated in the provided text. The document mentions the lung nodule detection model was "trained by Deep Convolution Network (CNN) based algorithm from the chest CT image," but does not provide details on the training set size.

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

Not explicitly stated in the provided text.

Summary

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February 24, 2023

Image /page/0/Picture/1 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.

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

Re: K221592

Trade/Device Name: AVIEW Lung Nodule CAD Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: OEB, LLZ Dated: January 25, 2023 Received: January 26, 2023

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

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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 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.

Lu Jiang

Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known) K221592

Device Name AVIEW Lung Nodule CAD

Indications for Use (Describe)

AVIEW Lung Nodule CAD is a Computer-Aided Detection (CAD) software designed to assist radiologists in the detection of pulmonary nodules (with diameter 3-20 mm) during the review of CT examinations of the chest for asymptomatic populations. AVIEW Lung Nodule CAD provides adjunctive information to alert the radiologists to regions of interest with suspected lung nodules that may otherwise be overlooked. AVIEW Lung Nodule CAD may be used as a second reader after the radiologist has completed their initial read. The algorithm has been validated using non-contrast CT images, the majority of which were acquired on Siemens SOMATOM CT series scanners: therefore, limiting device use to use with Siemens SOMATOM CT series is recommended.

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)

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510(k) Summary K221592

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: 05.31.2022

DEVICE 2

Name of Device: AVIEW Lung Nodule CAD Classification Name: Medical Image Management and Processing System. Classification Panel: Radiology CFR Section: (21CFR 892.2050) Regulatory Class: II Product Code: OEB, LLZ

PREDICATE DEVICE 3

Syngo.CT Lung CAD(VD20) by Siemens Healthcare GmbH (K203258) Name of Device: syngo. CT Lung CAD (VD20) Classification Name: Medical Image Management and Processing System. Classification Panel: Radiology CFR Section: (21CFR 892.2050) Regulatory Class: II Product Code: OEB

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

REFERENCE DEVICE 4

InferRead Lung CT.AI by Beijing Infervision Technology Co., Ltd. (K192880) Name of Device: InferRead Lung CT.AI Classification Name: Medical Image Management and Processing System Classification Panel: Radiology CFR Section: (21CFR 892.2050) Regulatory Class: II

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Product Code: OEB, LLZ

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

Name of Device: AVIEW Classification Name: Medical Image Management and Processing System Classification Panel: Radiology CFR Section: (21CFR 892.2050) Regulatory Class: II Product Code: LLZ, JAK

All reference devices have not been subject to a design-related recall.

DEVICE DESCRIPTION 5

The AVIEW Lung Nodule CAD is a software product that detects nodules in the lung. The lung nodule detection model was trained by Deep Convolution Network (CNN) based algorithm from the chest CT image. Automatic detection of lung nodules of 3 to 20mm in chest CT images. By complying with DICOM standards, this product can be linked with the Picture Archiving and Communication System (PACS) and provides a separate user interface to provide functions such as analyzing, identifying, storing, and transmitting quantified values related to lung nodules. The CAD's results could be displayed after the user's first read, and the user could select or de-select the mark provided by the CAD. The device's performance was validated with SIEMENS' SOMATOM series manufacturing. The device is intended to be used with a cleared AVIEW platform.

INDICATIONS FOR USE 6

7 COMPARISION OF TECHNOLOGICAL CHARACTERISTICS WITH THE PREDICATE DEVICE

AVIEW Lung Nodule CAD has the same intended use and the principle of operation and has similar features to the predicate devices. Snygo.CT Lung CAD (VD20) (K203258). 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.

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Characteristic	Subject Device	Predicate Device	Reference Device	Reference Device
Device Name	AVIEW LungNodule CAD	syngo. CT LungCAD (VD20)	InferReadLung CT.AI	AVIEW
Classification	Medical ImageManagement andProcessing System	Medical ImageManagement andProcessing System	Medical ImageManagement andProcessing System	Medical ImageManagement andProcessing System
RegulatoryNumber	21 CFR 892.2050	21 CFR 892.2050	21 CFR 892.2050	21 CFR 892.2050
Product CodeReview Panel	OEB, LLZRadiology	OEBRadiology	OEB, LLZRadiology	LLZ, JAKRadiology
510k Number	K221592	K203258	K192880	K200714
Indications foruse	AVIEW Lung Nodule CAD is a Computer-Aided Detection (CAD) software designed to assistradiologists in the detection of pulmonary nodules (with diameter 3-20 mm) during the review ofCT examinations of the chest for asymptomatic populations. AVIEW Lung Nodule CAD providesadjunctive information to alert the radiologists to regions of interest with suspected lung nodulesthat may otherwise be overlooked. AVIEW Lung Nodule CAD may be used as a second readerafter the radiologist has completed their initial read. The algorithm has been validated using non-contrast CT images, the majority of which were acquired on Siemens SOMATOM CT seriesscanners; therefore, limiting device use to use with Siemens SOMATOM CT series isrecommended.syngo. CT Lung CAD (VD20)The syngo. CT Lung CAD device is a Computer-Aided Detection (CAD) tool designed to assistradiologists in the detection of solid and subsolid (part-solid and ground glass) pulmonary nodulesduring review of multi-detector computed tomography (MDCT) from multivendor examinationsof the chest. The software is an adjunctive tool to alert the radiologist to regions of interest (ROI)that may otherwise be overlooked.The syngo. CT Lung CAD device may be used as a concurrent first reader followed by a fullreview of the case by the radiologist or as second reader after the radiologist has completed his/herinitial read.The software device is an algorithm which does not have its own user interface component fordisplaying of CAD marks.The Hosting Application incorporating syngo. CT Lung CAD is responsible for implementing auser interface.InferRead Lung CT.AIInferRead Lung CT.AI is comprised of computer-assisted reading tools designed to aid theradiologist in the detection of pulmonary nodules during the review of CT examinations of thechest on an asymptomatic population. Infer Read Lung CT.AI requires that both lungs be in thefield of view. InferRead Lung CT.AI provides adjunctive information and is not intended to beused without the original CT series.AVIEWAVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. Thissoftware could be used to support the physician quantitatively in the diagnosis, follow upevaluation and documentation of CT lung tissue images by providing image segmentation of sub-structures in lung, lobe, airways and cardiac, registration of inspiration and expiration which couldanalyze quantitative information such as air trapping volume, air trapped index, andinspiration/expiration ratio. And, volumetric and structure analysis, density evaluation andreporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on premiseand as cloud environment as well to allow users to connect by various environment such as mobiledevices and chrome browser. Characterizing nodules in the lung in a single study, or over the time

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	and measurements such as size (major axis, minor axis), estimated effective diameter from thevolume of the nodule, volume of the nodule, Mean HU(the average value of the CT pixel insidethe nodule in HU), Minimum HU, Max HU, mass(mass calculated from the CT pixel value), andvolumetric measures(Solid major; length of the longest diameter measured in 3D for solid portionof the nodule, Solid 2nd Major: The length of the longest diameter of the solid part, measured insections perpendicular to the Major axis of the solid portion of the nodule), VDT (Volume doublingtime), and Lung-RADS (classification proposed to aid with findings). The system automaticallyperforms the measurement, allowing lung nodules and measurements to be displayed and, integratewith FDA certified Mevis CAD (Computer aided detection) (K043617). It also provides CACanalysis by segmentation of four main artery (right coronary artery, left main coronary, left anteriordescending and left circumflex artery then extracts calcium on coronary artery to provide Agatstonscore, volume score and mass score by whole and each segmented artery type. Based on the score,provides CAC risk based on age and gender.
	AVIEW Lung Nodule CAD
	The AVIEW Lung Nodule CAD is a software product that detects nodules in the lung. The lungnodule detection model was trained by Deep Convolution Neural Network (CNN) based algorithmfrom the chest CT image. Automatic detection of lung nodules of 3 to 20mm in chest CT images.By complying with DICOM standards, this product can be linked with the Picture Archiving andCommunication System (PACS) and provides a separate user interface to provide functions suchas analyzing, identifying, storing, and transmitting quantified values related to lung nodules. TheCAD's results could be displayed after the user's first read, and the user could select or de-selectthe mark provided by the CAD. The device's performance was validated with SIEMENS’SOMATOM series manufacturing. The device is intended to be used with a cleared AVIEWplatform.
	syngo. CT Lung CAD (VD20)
GeneralDescription	Simens Healthcare GmbH intends to market the syngo. CT Lung CAD which is a medical devicethat is designed to perform CAD processing in thoracic CT examinations for the detection of solidpulmonary nodules (between 3.0 mm and 30.0mm) and subsolid (part-solid and ground glass)nodules (between 5.0mm and 30.0mm) in average diameter. The device processes image acquiredwith multi-detector CT scanners with 16 or more detector rows.The syngo. CT Lung CAD device supports the full range of nodule locations (central, pe-ripheral)and contours (round, irregular).The syngo. CT Lung CAD sends a list of nodule candidate locations to a visualization application,such as syngo MM Oncology, or a visualization rendering component, whichgenerates output images series with the CAD marks superimposed on the input thoracic CT imagesto enable the radiologist's review. syngo MM Oncology (FDA clearance K191309) is deployed onthe syngo.via platform (FDA clearance K191040), which provides a common framework forvarious other applications implementing specific clinical workflows (but are not part of thisclearance) to display the CAD marks. The syngo. CT Lung CAD device may be used either as aconcurrent first reader, followed by a review of the case, or as a second reader only after the initialread is completedThe subject device and predicate device have the same basic technical characteristics. This doesnot introduce new types of safety or effectiveness concerns as demonstrated by the statisticalanalyses and results of the reader study and additional evaluations results documented in theStatistical Analysis.
	InferRead Lung CT.AI
	InferRead Lung CT.AI uses the Browser/Server architecture and is provided as Software as aService (SaaS) via a URL. The system integrates algorithm logic and database in the same serverto ensure the simplicity of the system and the convenience of system maintenance. The server isable to accept chest CT images from a PACS system, Radiological Information System (RISsystem) or directly from a CT scanner, analyze the images and provide output annotationsregarding lung nodules. Users are then able to use an existing PACS system to view the annotations

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	on their workstations. Dedicated servers can be located at hospitals and are directly.connected to the hospital networks. The software consists of 4 modules which are Image reception(Docking Toolbox), Image predictive processing (DLServer), Image storage (RePACS) and Imagedisplay (NeoViewer).
	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. And isintended for use as diagnostic patient imaging which is intended for the review and analysis of CTscanning. Provides following features as semi-automatic nodule management, maximal planemeasure, 3D measures and columetric measures, automatic nodule detection by integration with3nd party CAD. Also provides Brocks model which calculated the malignancy score based onnumerical or Boolean inputs. Follow up support with automated nodule matching andautomatically categorize Lung-RADS score which is a quality assurance tool designed tostandardize lung cancer screening CT reporting and management recommendations that is basedon 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 andreduce the burden of medical.
Detectiontarget(s)	pulmonary nodules innon-contrast chest CTacquisitions	Solidandsubsolid(part-solidandground-glass)pulmonary nodules inscreeninganddiagnostic chest CTacquisitions.	solidpulmonarynodules in diagnosticchest CT acquistions
NoduleCharacteristics	Diameter:· Pulmanoarynodules ≥ 3 mmand <20 mmLocations:· Full range: central,peripheralContours:· round, irregular	Diameter:solid ≥ 3mm and<30mmSubsolid(part-solid andgroundglass) ≥ 5mm and ≤30mmLocations:· Full range: central,peripheralContours:round, irregular	Solidnodules人3mm and 10mm andrande,central,fullperipheralround.irregular
Image format	DICOM	DICOM	DICOM	DICOM
HostingPlatform	AVIEW	syngo.via	-
HostingApplication	AVIEW LCS	Syngo MM Oncology

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core:line

Outputs	DICOM GSPS(Grayscale SoftcopyPresentation State) XML (Coordinate ofdetected nodules)Able to view results on AVIEW, AVIEW LCS viewer page	Generates output images series with theCAD makrs superimposed.Able to view results syngo MM Oncology viewer page.
Type of Scans	CT	CT	CT	CT
	ScannersSiemens SOMATOM CT Scanners	ScannersMulti-vendor and multi-detector CT (MDCT) scanners(Siemens, GE, Philips, and Toshiba)
	Detector rows16 or more detector rows	Detector rows16 or more detector rows
	Voltage100~140 kVp	Voltage100~140 kVp
	ExposureNone	ExposureNone
Input scanningparameters	Collimation1mm or less	Collimation1mm or less
	Slice ThicknessUp to and including 2.5mm, it isrecommended that <=1.25mm be usedfor the detection of smaller nodules (e.g., 4.0mm)	Slice ThicknessUp to and including 2.5mm, it isrecommended that <=1.25mm be used forthe detection of smaller nodules (e.g., 3.0mm)
	Slice Overlap0~50%Note: Reconstruction overlap is allowed, but gaps are not permitted	Slice Overlap0~50%Note: Reconstruction overlap is allowed, but gaps are not permitted
	Number of imagesNone	Number of imagesNone

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core:line

Kernel	Kernel
Consistent withthoracic CT protocolsand in line with patientsafety guidelines.Kernels were groupedas to their profile.Typical kernelsvalidated by the readerstudy were:Smooth: B, B30f,Standard, FC10Medium: C, B45f,Br49d, B50f, I50f,Br60f, Lung, FC50,FC51Sharp: D, B70f, I70f,B80s, I80s, Bone	Consistent withthoracic CT protocolsand in line with patientsafety guidelines.Kernels were groupedas to their profile.Typical kernelsvalidated by the readerstudy were:Smooth: B, B30f,Standard, FC10.Medium: C B45f,B50f, Lung, FC50,FC51, Bv49d_2,I50f_2, B60f.Sharp: D, B70f, Bone,FC52
ContrastNone	ContrastNone
DoseConsistent withthoracic CT protocolsand in line with patientsafety guidelines.Typical values are:CTDIvol < 8.0 mGy(milligray) indiagnostic protocolsandCTDIvol of = 3.0mGy in screeningprotocols.These values aredefined for standardsized patient—5 ft 7in., 154 lb (170cm, 70 kg)—based ona 32-cm referencephantom withappropriate reductionsinCTDIvol for smallerpatients andappropriateincreases in CTDIvolfor larger patients.	DoseConsistent withthoracic CT protocolsand in line with patientsafety guidelines.Typical values are:CTDIvol < 8.0 mGy(milligray) indiagnostic protocolsandCTDIvol of = 3.0 mGyin screening protocols.These values aredefined for standardsized patient—5 ft 7in., 154 lb (170cm, 70 kg)—based ona 32-cm referencephantom withappropriate reductionsinCTDIvol for smallerpatients andappropriateincreases in CTDIvolfor larger patients.

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PERFORMANCE DATA 8

8.1 Clinical performance evaluation

A HIPAA-compliant multi-case, multi-reader, retrospective study design was utilized. An image viewer without or with AI algorithms, AVIEW Lung Nodule CAD program, for lung nodule detection and measurement were used for chest CT reads. Three dedicated chest radiologists with at least ten years of experience determined the ground truth using a dataset of 151 Chest CTs with 103 negative controls and 48 cases with one or more lung nodules. All lung nodules were segmented in 3D. In a blinded fashion, eleven board-certified radiologists interpreted the same cases unassisted, followed by AI assistance after randomization and a 4-week washout period. Data were analyzed in a random reader, random case context, and one-sided tests at the 5% significance level, and 95% confidence intervals were constructed for all estimates. Reading time analyses were performed with mixed-effect Gaussian regression with a fixed effect for AI assistance and with clustering at the reader level. Post estimation marginal estimates were calculated for unassisted versus assisted read times.

The multi-reader multi-case demonstrated that aided radiologist performance for lung nodule detection was improved with statistical significance compared to unaided. Reading time was decreased when AVIEW Lung Nodule CAD aided radiologists. Also, both incidental and screening populations was included on the test dataset. We have performed subgroup analyses for several key subgroups to demonstrate generalizability. This includes assessment of performance for challenging and/or confounding cases.

. Performance Testing Results
- 1. Overall unaided/aider reader performacne comparison

	Unaided	Aided	Difference in point estimate
AUC	0.73 (0.66 - 0.79)	0.92 (0.89 -0.95)	0.19
Sensitivity	0.68 (0.62 - 0.73)	0.91 (0.89 -0.94)	0.23
FP/scan	0.48 (0.28 - 0.69)	0.28 (0.15-0.42)	0.24

8.1.1 Test Report

Clinical Study Report for AVIEW Lung Nodule CAD .

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.

8.2.1 System Test

In accordance with the document 'integration Test Cases' discussed in advance by the software development team and test team, the test is conducted by installing software with recommended system specifications. Despite the Test case recognized in advance was not in existence. New software error discovered by the 'Exploratory Test' conducted by the test team will be registered and managed as a new test case after discussion between the development team and test team.

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

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

8.2.2 Performance Test

DICOM Test Report
Performance Test Report
DICOM Conformance Statement ●
Thin Cient Server Compatibility Test Report .
AVIEW Lung Nodule CAD Integration Test Report
Standalone study for AVIEW Lung Nodule CAD ●

The standalone study of AI-based lung nodule detection software compared to ground truth was evaluated with sensitivity, specificity, ROC, and FROC. We consider that the software performs successfully when the sensitivity for lung nodule detection performance at the patient level and nodule level exceeds 0.8 and the specificity exceeds 0.6, the ROC AUC for lung nodule detection performance exceeds 0.8 and the sensitivity for lung nodule detection performance exceeds 0.8 in false positive (FP)/scan < 2. Dataset are collected from three geographically distinct US clinical sites. The total number of data is 282 (140 cases with nodule data and 142 cases without nodule data). All datasets were built with images of U.S., and by gender, there were 132 males and 150 females. We validated this test by purchasing anonymized medical data. So, any data used for AI training or internal validation was not used for this test. Also, both incidental and screening populations was included on the test dataset. We have performed subgroup analyses for several key subgroups to demonstrate generalizability. This includes assessment of performance for challenging and/or confounding cases.

ゃ Performance Testing Results
- Overall AUC (with CI): 0.961(0.939-0.983) 1.
- 1. Overall Sensitivity (with CI): 0.907(0.846-0.95)
- 1. Overall Specificity (with CI): 0.704(0.622-0.778)
- 1. Overall sensitivity (with CI) at FP/scan<2: 0.889(0.849-0.93) at FP/scan=0.504

CONCLUSIONS ல்

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 clinical tests demonstrate that the device is safe and effective. Therefore, it is our opinion that the AVIEW Lung Nodule CAD 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).