Who is the manufacturer of AVIEW Lung Nodule CAD?

Coreline Soft Co., Ltd. filed the 510(k) submission for AVIEW Lung Nodule CAD (K221592).

What is the FDA product code for AVIEW Lung Nodule CAD?

OEB. It is classified under 21 CFR 892.2050 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for AVIEW Lung Nodule CAD?

510(k) clearance (submission type: Traditional).

Does AVIEW Lung Nodule CAD use AI or machine learning?

Yes. AVIEW Lung Nodule CAD (K221592) is flagged as an AI/ML-enabled medical device in the FDA's AI/ML device list.

What are the predicate devices for AVIEW Lung Nodule CAD?

syngo.CT Lung CAD (VD20) (K203258).

Who can help prepare an FDA 510(k) submission like AVIEW Lung Nodule CAD?

Innolitics — a medical-device software consultancy — provides regulatory and engineering support for FDA 510(k) submissions. See https://innolitics.com/services/510ks/ or contact https://innolitics.com/contact.

Can an AI/ML device like AVIEW Lung Nodule CAD get a guaranteed FDA 510(k) clearance?

Yes. Innolitics offers a guaranteed FDA 510(k) clearance engagement for AI/ML Software as a Medical Device — combining engineering and regulatory execution under one agreement with a guaranteed clearance outcome. Eligibility: the device must be an AI/ML Software as a Medical Device (SaMD), and its performance must meet the criteria Innolitics determines during an initial regulatory strategy engagement to be necessary for FDA clearance. See https://innolitics.com/services/end-to-end-samd/ or contact https://innolitics.com/contact.

AVIEW Lung Nodule CAD

K221592 · Coreline Soft Co., Ltd. · OEB · Feb 24, 2023 · Radiology

Device Facts

Record ID	K221592
Device Name	AVIEW Lung Nodule CAD
Applicant	Coreline Soft Co., Ltd.
Product Code	OEB · Radiology
Decision Date	Feb 24, 2023
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.2050
Device Class	Class 2
Attributes	AI/ML, Software as a Medical Device, Real-World Evidence

Real-World Evidence

Submission	Device	Sponsor	RWD Sources	RWE Use Summary	Key Tags
K221592 · Feb 24, 2023	AVIEW Lung Nodule CAD	Coreline Soft Co., Ltd.	Anonymized clinical CT scan datasets from three US clinical sites	The sponsor used a retrospective dataset of 282 clinical CT examinations to conduct a standalone performance validation study of the AVIEW Lung Nodule CAD software, evaluating sensitivity, specificity, ROC, and FROC against ground truth.	Retrospective study; Clinical CT images; Standalone validation; Performance evaluation

Clinical Evidence

Study Design	Population	Comparator	Key Endpoints
Standalone study for AVIEW Lung Nodule CAD; Retrospective standalone performance validation	Asymptomatic and incidental populations undergoing chest CT; Sample Size: 282 (140 cases with nodules, 142 cases without); Number of Sites: 3	Not applicable for this study	Sensitivity, specificity, ROC AUC, FROC (at FP/scan < 2)

AI Performance

Output	Algorithm	Acceptance	Observed	Dev DS	Dev Readers	Test DS	Test Readers
Pulmonary nodule detection	Deep Convolution Neural Network (CNN)	Sensitivity > 0.8, Specificity > 0.6, ROC AUC > 0.8, Sensitivity at FP/scan < 2 > 0.8	AUC: 0.961, Sensitivity: 0.907, Specificity: 0.704, Sensitivity at FP/scan < 2: 0.889	—	—	Standalone study: 282 cases (140 with nodules, 142 without nodules)	—
Pulmonary nodule detection	Deep Convolution Neural Network (CNN)	—	AUC: 0.92 (aided) vs 0.73 (unaided), Sensitivity: 0.91 (aided) vs 0.68 (unaided), FP/scan: 0.28 (aided) vs 0.48 (unaided)	—	—	Multi-case, multi-reader, retrospective study: 151 Chest CTs (103 negative, 48 positive)	3 (chest radiologists) + 11 (board-certified radiologists)

Indications for Use

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 Story

AVIEW Lung Nodule CAD is a software-based CAD tool for pulmonary nodule detection in chest CT images. It processes non-contrast CT scans to identify nodules (3-20 mm); utilizes a Deep Convolutional Neural Network (CNN) to analyze images and highlight regions of interest. The device integrates with PACS via DICOM standards; provides a dedicated user interface for radiologists to review, select, or de-select CAD-generated marks. Used as a second reader after initial radiologist assessment; assists in identifying nodules that might otherwise be overlooked. By providing adjunctive information, the device aims to improve detection sensitivity and reduce reading time, potentially leading to earlier diagnosis and improved patient outcomes. Operates within a clinical environment; intended for use by radiologists.

Clinical Evidence

Retrospective multi-reader multi-case study (151 CTs, 103 negative, 48 positive) with 11 radiologists. Aided performance showed statistically significant improvement: AUC 0.92 (vs 0.73 unaided), sensitivity 0.91 (vs 0.68 unaided), and reduced FP/scan (0.28 vs 0.48). Standalone validation on 282 independent US datasets (140 positive, 142 negative) showed AUC 0.961, sensitivity 0.907, and specificity 0.704. Sensitivity at FP/scan < 2 was 0.889.

Technological Characteristics

Software-based CAD; utilizes Deep Convolutional Neural Network (CNN). Compatible with DICOM standards. Validated for non-contrast chest CT images, specifically Siemens SOMATOM series scanners. Operates on AVIEW platform. Connectivity via PACS integration.

Indications for Use

Indicated for asymptomatic populations undergoing chest CT examinations to assist radiologists in detecting pulmonary nodules 3-20 mm in diameter. Used as a second reader after initial radiologist review. Recommended for use with Siemens SOMATOM CT series scanners.

Regulatory Classification

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.

Special Controls

*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).

Predicate Devices

syngo.CT Lung CAD (VD20) (K203258)

Reference Devices

InferRead Lung CT.AI (K192880)
AVIEW (K200714)
Mevis CAD (K043617)

Submission Summary (Full Text)

{0}------------------------------------------------ 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 {1}------------------------------------------------ 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 {2}------------------------------------------------ ## 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) ## 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." {3}------------------------------------------------ ## 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 {4}------------------------------------------------ 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 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. ## 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. {5}------------------------------------------------ | Characteristic | Subject Device | Predicate Device | Reference Device | Reference Device | |------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------|------------------------------------------------------|------------------------------------------------------| | Device Name | AVIEW Lung Nodule CAD | syngo. CT Lung CAD (VD20) | InferRead Lung CT.AI | AVIEW | | Classification | Medical Image Management and Processing System | Medical Image Management and Processing System | Medical Image Management and Processing System | Medical Image Management and Processing System | | Regulatory Number | 21 CFR 892.2050 | 21 CFR 892.2050 | 21 CFR 892.2050 | 21 CFR 892.2050 | | Product Code Review Panel | OEB, LLZ Radiology | OEB Radiology | OEB, LLZ Radiology | LLZ, JAK Radiology | | 510k Number | K221592 | K203258 | K192880 | K200714 | | Indications for use | 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. syngo. CT Lung CAD (VD20) The syngo. CT Lung CAD device is a Computer-Aided Detection (CAD) tool designed to assist radiologists in the detection of solid and subsolid (part-solid and ground glass) pulmonary nodules during review of multi-detector computed tomography (MDCT) from multivendor examinations of 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 full review of the case by the radiologist or as second reader after the radiologist has completed his/her initial read. The software device is an algorithm which does not have its own user interface component for displaying of CAD marks. The Hosting Application incorporating syngo. CT Lung CAD is responsible for implementing a user interface. InferRead Lung CT.AI InferRead Lung CT.AI is comprised of computer-assisted reading tools designed to aid the radiologist in the detection of pulmonary nodules during the review of CT examinations of the chest on an asymptomatic population. Infer Read Lung CT.AI requires that both lungs be in the field of view. InferRead Lung CT.AI provides adjunctive information and is not intended to be used without the original CT series. AVIEW AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This software could be used to support the physician quantitatively in the diagnosis, follow up evaluation 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 could analyze quantitative information such as air trapping volume, air trapped index, and inspiration/expiration ratio. And, volumetric and structure analysis, density evaluation and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on premise and as cloud environment as well to allow users to connect by various environment such as mobile devices and chrome browser. Characterizing nodules in the lung in a single study, or over the time | | | | {6}------------------------------------------------ | | and measurements such as size (major axis, minor axis), estimated effective diameter from the volume of the nodule, volume of the nodule, Mean HU(the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass(mass calculated from the CT pixel value), and volumetric measures(Solid major; length of the longest diameter measured in 3D for solid portion of the nodule, Solid 2nd Major: The length of the longest diameter 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 CAC analysis by segmentation of four main artery (right coronary artery, left main coronary, left anterior descending and left circumflex artery then extracts calcium on coronary artery to provide Agatston score, 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 lung nodule detection model was trained by Deep Convolution Neural 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. | | | syngo. CT Lung CAD (VD20) | | General Description | Simens Healthcare GmbH intends to market the syngo. CT Lung CAD which is a medical device that is designed to perform CAD processing in thoracic CT examinations for the detection of solid pulmonary 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 acquired with 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, which generates output images series with the CAD marks superimposed on the input thoracic CT images to enable the radiologist's review. syngo MM Oncology (FDA clearance k191309) is deployed on the syngo.via platform (FDA clearance k191040), which provides a common framework for various other applications implementing specific clinical workflows (but are not part of this clearance) to display the CAD marks. The syngo. CT Lung CAD device may be used either as a concurrent first reader, followed by a review of the case, or as a second reader only after the initial read is completed The subject device and predicate device have the same basic technical characteristics. This does not introduce new types of safety or effectiveness concerns as demonstrated by the statistical analyses and results of the reader study and additional evaluations results documented in the Statistical Analysis. | | | InferRead Lung CT.AI | | | InferRead Lung CT.AI uses the Browser/Server architecture and is provided as Software as a Service (SaaS) via a URL. The system integrates algorithm logic and database in the same server to ensure the simplicity of the system and the convenience of system maintenance. The server is able to accept chest CT images from a PACS system, Radiological Information System (RIS system) or directly from a CT scanner, analyze the images and provide output annotations regarding lung nodules. Users are then able to use an existing PACS system to view the annotations | {7}------------------------------------------------ | | 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 Image display (NeoViewer). | | | | |---------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|-------| | | AVIEW | | | | | | The AVIEW is a software product which can be installed on a PC. It shows images taken with the 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 3nd 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. | | | | | Detection target(s) | pulmonary nodules in non-contrast chest CT acquisitions | Solid and subsolid (part-solid and ground-glass) pulmonary nodules in screening and diagnostic chest CT acquisitions. | solid pulmonary nodules in diagnostic chest CT acquistions | | | Nodule Characteristics | Diameter: · Pulmanoary nodules ≥ 3 mm and <20 mm Locations: · Full range: central, peripheral Contours: · round, irregular | Diameter: solid ≥ 3mm and <30mm Subsolid (part- solid and ground glass) ≥ 5mm and ≤ 30mm Locations: · Full range: central, peripheral Contours: round, irregular | Solid nodules 人 3mm and 10mm and rande, central, full peripheral round. irregular | | | Image format | DICOM | DICOM | DICOM | DICOM | | Hosting Platform | AVIEW | syngo.via | - | | | Hosting Application | AVIEW LCS | Syngo MM Oncology | | | {8}------------------------------------------------ # core:line | | Outputs | DICOM GSPS (Grayscale Softcopy Presentation State) XML (Coordinate of detected nodules) Able to view results on AVIEW, AVIEW LCS viewer page | Generates output images series with the CAD makrs superimposed. Able to view results syngo MM Oncology viewer page. | | | |--|------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|----|----| | | Type of Scans | CT | CT | CT | CT | | | | Scanners Siemens SOMATOM CT Scanners | Scanners Multi-vendor and multi-detector CT (MDCT) scanners (Siemens, GE, Philips, and Toshiba) | | | | | | Detector rows 16 or more detector rows | Detector rows 16 or more detector rows | | | | | | Voltage 100~140 kVp | Voltage 100~140 kVp | | | | | | Exposure None | Exposure None | | | | | Input scanning parameters | Collimation 1mm or less | Collimation 1mm or less | | | | | | Slice Thickness Up to and including 2.5mm, it is recommended that <=1.25mm be used for the detection of smaller nodules (e.g., 4.0mm) | Slice Thickness Up to and including 2.5mm, it is recommended that <=1.25mm be used for the detection of smaller nodules (e.g., 3.0mm) | | | | | | Slice Overlap 0~50% Note: Reconstruction overlap is allowed, but gaps are not permitted | Slice Overlap 0~50% Note: Reconstruction overlap is allowed, but gaps are not permitted | | | | | | Number of images None | Number of images None | | | {9}------------------------------------------------ # core:line | Kernel | Kernel | | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--| | Consistent with thoracic CT protocols and in line with patient safety guidelines. Kernels were grouped as to their profile. Typical kernels validated by the reader study were: Smooth: B, B30f, Standard, FC10 Medium: C, B45f, Br49d, B50f, I50f, Br60f, Lung, FC50, FC51 Sharp: D, B70f, I70f, B80s, I80s, Bone | Consistent with thoracic CT protocols and in line with patient safety guidelines. Kernels were grouped as to their profile. Typical kernels validated by the reader study were: Smooth: B, B30f, Standard, FC10. Medium: C B45f, B50f, Lung, FC50, FC51, Bv49d_2, I50f_2, B60f. Sharp: D, B70f, Bone, FC52 | | | Contrast None | Contrast None | | | Dose Consistent with thoracic CT protocols and in line with patient safety guidelines. Typical values are: CTDIvol < 8.0 mGy (milligray) in diagnostic protocols and CTDIvol of = 3.0 mGy in screening protocols. These values are defined for standard sized patient—5 ft 7 in., 154 lb (170 cm, 70 kg)—based on a 32-cm reference phantom with appropriate reductions in CTDIvol for smaller patients and appropriate increases in CTDIvol for larger patients. | Dose Consistent with thoracic CT protocols and in line with patient safety guidelines. Typical values are: CTDIvol < 8.0 mGy (milligray) in diagnostic protocols and CTDIvol of = 3.0 mGy in screening protocols. These values are defined for standard sized patient—5 ft 7 in., 154 lb (170 cm, 70 kg)—based on a 32-cm reference phantom with appropriate reductions in CTDIvol for smaller patients and appropriate increases in CTDIvol for larger patients. | | {10}------------------------------------------------ ### 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. {11}------------------------------------------------ - 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 A - 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. - 2. Overall Sensitivity (with CI): 0.907(0.846-0.95) - 3. Overall Specificity (with CI): 0.704(0.622-0.778) - 4. 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 tes…

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