(267 days)
Yes
The device description explicitly states that the lung nodule detection model was trained by a "Deep Convolution Network (CNN) based algorithm," which is a type of deep learning, a subset of machine learning and AI.
No
The device is a Computer-Aided Detection (CAD) software intended to assist radiologists in detecting pulmonary nodules, not to directly treat or diagnose a condition.
Yes
This device is a Computer-Aided Detection (CAD) software designed to assist radiologists in the detection of pulmonary nodules during the review of CT examinations, providing "adjunctive information to alert the radiologists to regions of interest with suspected lung nodules that may otherwise be overlooked." This function is inherently diagnostic as it aids in identifying potential medical conditions.
Yes
The device description explicitly states "The AVIEW Lung Nodule CAD is a software product". While it interacts with CT images and PACS, it is presented as a software application without mention of proprietary hardware components.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The intended use clearly states that the device is a "Computer-Aided Detection (CAD) software designed to assist radiologists in the detection of pulmonary nodules... during the review of CT examinations of the chest". It provides "adjunctive information" and is used as a "second reader". This describes a tool that aids in the interpretation of medical images, not a test performed on biological samples to diagnose or monitor a condition.
- Device Description: The description reinforces that it's a "software product that detects nodules in the lung" from "chest CT images". It links with PACS and provides a user interface for analyzing and displaying results. This aligns with image analysis software, not an IVD.
- Input Imaging Modality: The input is CT images, which are medical images, not biological samples.
- Anatomical Site: The anatomical site is the lung, which is the area being imaged, not a source of a biological sample for testing.
- Performance Studies: The performance studies focus on the accuracy of nodule detection in CT images (AUC, Sensitivity, FP/scan, Specificity) and the impact on radiologist performance and reading time. These are metrics relevant to image analysis and interpretation, not IVD performance metrics like accuracy in detecting analytes in biological samples.
IVD devices are designed to perform tests on biological samples (like blood, urine, tissue) to provide information for the diagnosis, monitoring, or treatment of a disease or condition. This device operates on medical images to assist in their interpretation.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / 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.
Product codes (comma separated list FDA assigned to the subject device)
OEB, LLZ
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
CT
Anatomical Site
Chest / Lung
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Radiologists / Not Found
Description of the training set, sample size, data source, and annotation protocol
The lung nodule detection model was trained by Deep Convolution Network (CNN) based algorithm from the chest CT image.
Description of the test set, sample size, data source, and annotation protocol
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.
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.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Clinical performance evaluation: A HIPAA-compliant multi-case, multi-reader, retrospective study design was utilized. Sample Size: 151 Chest CTs (103 negative controls, 48 with nodules). 11 board-certified radiologists.
Overall unaided/aided reader performance comparison:
AUC: Unaided 0.73 (0.66 - 0.79), Aided 0.92 (0.89 -0.95), Difference 0.19
Sensitivity: Unaided 0.68 (0.62 - 0.73), Aided 0.91 (0.89 -0.94), Difference 0.23
FP/scan: Unaided 0.48 (0.28 - 0.69), Aided 0.28 (0.15-0.42), Difference 0.24
Key Results: 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.
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. Sample Size: 282 cases (140 with nodules, 142 without).
Overall AUC (with CI): 0.961(0.939-0.983)
Overall Sensitivity (with CI): 0.907(0.846-0.95)
Overall Specificity (with CI): 0.704(0.622-0.778)
Overall sensitivity (with CI) at FP/scan
§ 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).
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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
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)
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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
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.
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| 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 |
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| | 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 |
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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 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 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