(365 days)
Yes
The summary explicitly mentions "automatic nodule detection by integration with 3rd party CAD" and "integrate with FDA certified Mevis CAD (Computer aided detection) (K043617)". While the summary doesn't detail the internal workings of the CAD system, Computer-Aided Detection (CAD) systems in medical imaging often utilize AI/ML techniques for pattern recognition and detection. The summary also mentions "automatically categorize Lung-RADS score" and "automatically analyzing coronary arteries from the segmented arteries," which are tasks that can be performed using AI/ML. The "Mentions AI, DNN, or ML" field is also marked as "Yes".
No.
Explanation: The device is a software product that provides quantitative analysis of CT images by image segmentation, characterization, and measurement of various features in the lung and heart. It aids physicians in diagnosis and assessment but does not directly treat or prevent a disease or condition itself.
Yes
The device provides quantitative analysis of CT images, characterizes nodules, calculates Agatston and mass scores, and provides CAC risk, which are all activities associated with diagnosing medical conditions.
Yes
The device description explicitly states "The AVIEW is a software product that can be installed on a PC." and details its functions as software tools for image processing, analysis, and management. There is no mention of accompanying hardware components required for its primary function as a medical device.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In vitro diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- AVIEW's Function: AVIEW processes and analyzes medical images (CT scans) of the human body. It does not perform tests on biological samples. Its purpose is to provide quantitative analysis and support for physicians in interpreting these images.
Therefore, AVIEW falls under the category of medical imaging software or a medical device that aids in the interpretation of medical images, rather than an in vitro diagnostic device.
No
The clearance letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device. The "Control Plan Authorized (PCCP) and relevant text" section states "Not Found".
Intended Use / Indications for Use
AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This software can be used to support the physician providing quantitative analysis of CT images by image segmentation of sub-structures in the lung, lobe, airways, fissures completeness, cardiac, density evaluation, and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on-premises and as a cloud environment to allow users to connect by various environments such as mobile devices and Chrome browsers. Converts the sharp kernel for quantitative analysis of segmenting low attenuation areas of the lung. Characterizing nodules in the lung in a single study or over the time course of several thoracic studies. Characterizations include type, location of the nodule, and measurements such as size (major axis, minor axis), estimated effective diameter from the volume of the nodule, the volume of the nodule, Mean HU(the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass (mass calculated from the CT pixel value), and volumetric measures(Solid major, length of the longest diameter measure in 3D for a solid portion of the nodule, Solid 2nd Major: The size of the solid part, measured in sections perpendicular to the Major axis of the solid portion of the nodule), VDT (Volume doubling time), and Lung-RADS (classification proposed to aid with findings.) ). The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, integrate with FDA certified Mevis CAD (Computer aided detection) (K043617). It also provides the Agatston score, and mass score by the whole and each artery by segmenting four main arteries (right coronary artery, left main coronary, left anterior descending, and left circumflex artery). Based on the calcium score provides CAC risk based on age and gender. The device is indicated for adult patients only.
Product codes
QIH, JAK
Device Description
The AVIEW is a software product that can be installed on a PC. It shows images taken with the interface from various storage devices using DICOM 3.0, the digital image and communication standard in medicine. It also offers functions such as reading, manipulation, analyzing, post-processing, saving, and sending images by using software tools. And is intended for use as a quantitative analysis of CT scanning. It provides the following features such as segmentation of lung, lobe, airway, fissure completeness, semi-automatic nodule management, maximal plane measures and volumetric measures, automatic nodule detection by integration with 3rd party CAD. It also provides the Brocks model, which calculates the malignancy score based on numerical or Boolean inputs. Follow-up support with automated nodule matching and automatically categorize Lung-RADS score, which is a quality assurance tool designed to standardize lung cancer screening CT reporting and management recommendations that are based on type, size, size change, and other findings that are reported. It also provides a calcium score by automatically analyzing coronary arteries from the segmented arteries.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Fully automatic lungs, lobes and airways segmentation using deep-learning algorithms
Automatically segments calcium area of coronary artery based on deep learning.
Input Imaging Modality
CT thoracic and cardiac datasets, CT scanning
Anatomical Site
Pulmonary tissue, lung, lobe, airways, cardiac, coronary arteries
Indicated Patient Age Range
Adult patients only.
Intended User / Care Setting
Physician, on-premises and as a cloud environment, mobile devices and Chrome browsers.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Fissure completeness was validated using a total of 129 subjects from TCIA (the Cancer Imaging Archive) LIDC database. The performance was evaluated using Bland Altman plots to assess the fissure completeness performance compared to radiologists. Kappa and ICC were also reported.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Nonclinical Performance Testing: "This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device. The substantial equivalence of the device is supported by the non-clinical testing"
Software Verification and Validation:
- Unit Test: Conducting Unit Test using Google C++ Unit Test Framework on major software components identified by software development team. List of Unit Test includes Functional test condition for software component unit, Performance test condition, and part of algorithm analysis for image processing algorithm.
- System Test: "In accordance with the document 'integration Test Cases' discussed in advanced by software development team and test team, test is conducted by installing software with recommended system specification. Despite Test case recognized in advance was not in existence. New software error discovered by 'Exploratory Test' conducted by test team will be registered and managed as new test case after discussion between development team and test team." "Discovered software error will be classified into 3 categories as severity and managed." "Success standard of System Test is not finding 'Major', 'Moderate' defect."
- Performance Test:
- Nodule Matching Experiment Using Lung Registration
- LAA Comparative Experiment for Performance Evaluation of Denoised CT
- Semi-automatic Nodule Segmentation
- Brock Model (ask PANCAN) Calculation
- VDT Calculation
- Lung RADS Calculation
- MeVis CAD Integration
- Validation LAA Analysis
- Validation LAA Size Analysis
- Size analysis algorithm of LAA clusters
- Lung Registration
- Reliability Test for Airway wall Measurement
- Fissure Completeness
- CAC Performance Evaluation of Denoised CT
- Validation on DVF Size Optimization with Sub-sampling
- Airway Segmentatation
- Auto Lung & Lobe Segmentation
- Kernel Conversion: "The LAA result on kernel converted sharp image should have higher reliability with the soft kernel than LAA results on sharp kernel image that is not Kernel Conversion applied. Of the 96 total, 53 are U.S. population and 43 are Korean."
- Fissure Completeness: "Fissure completeness was validated using a total of 129 subjects from TCIA (the Cancer Imaging Archive) LIDC database. The performance was evaluated using Bland Altman plots to assess the fissure completeness performance compared to radiologists. Kappa and ICC were also reported."
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found (Kappa and ICC are mentioned for Fissure Completeness, but values are not provided)
Predicate Device(s)
Reference Device(s)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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).
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services seal on the left and the FDA acronym and name on the right. The FDA acronym is in a blue square, and the full name "U.S. Food & Drug Administration" is in blue text.
Coreline Soft Co., Ltd. % Hye Yi Park RA Manager 4,5F(Yeonnam-dong), 49, World Cup buk-ro 6-gil Mapo-gu Seoul, 03991 KOREA
Re: K214036
Dec. 23, 2022
Trade/Device Name: AVIEW Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH, JAK Dated: November 25, 2022 Received: November 28, 2022
Dear Hye Yi Park:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
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 mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
Wenbo Li
for Jessica Lamb
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Ouality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2023 See PRA Statement below.
510(k) Number (if known)
Device Name AVIEW
Indications for Use (Describe)
AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This software can be used to support the physician providing quantitative analysis of CT images by image segmentation of sub-structures in the lung, lobe, airways, fissures completeness, cardiac, density evaluation, and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on-premises and as a cloud environment to allow users to connect by various environments such as mobile devices and Chrome browsers. Converts the sharp kernel for quantitative analysis of segmenting low attenuation areas of the lung. Characterizing nodules in the lung in a single study or over the time course of several thoracic studies. Characterizations include type, location of the nodule, and measurements such as size (major axis, minor axis), estimated effective diameter from the volume of the nodule, the volume of the nodule, Mean HU(the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass (mass calculated from the CT pixel value), and volumetric measures(Solid major, length of the longest diameter measure in 3D for a solid portion of the nodule, Solid 2nd Major: The size of the solid part, measured in sections perpendicular to the Major axis of the solid portion of the nodule), VDT (Volume doubling time), and Lung-RADS (classification proposed to aid with findings.) ). The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, integrate with FDA certified Mevis CAD (Computer aided detection) (K043617). It also provides the Agatston score, and mass score by the whole and each artery by segmenting four main arteries (right coronary artery, left main coronary, left anterior descending, and left circumflex artery). Based on the calcium score provides CAC risk based on age and gender. The device is indicated for adult patients only.
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
SUBMITTER 1
Coreline Soft Co., Ltd. 4,5F (Yeonnam-dong), 49 World Cup buk-ro 6-gil, Mapo-gu, Seoul, 03991, Republic of Korea.
Phone: 82.2.517.7321 Fax: 82.2.571.7324
Contact Person: hyeyi. Park Date Prepared: 12.21.2022
DEVICE 2
Name of Device: AVIEW Common or Usual Name: Image Processing Software Classification Name: System, image processing, radiological (21CFR 892.2050) Regulatory Class: II Product Code: QIH, JAK
3 PREDICATE DEVICE
AVIEW by Coraline Soft Co., Ltd. (K200714)
Name of Device: AVIEW Common or Usual Name: Image Processing Software Classification Name: System, image processing, radiological (21CFR 892.2050) Regulatory Class: II Product Code: LLZ, JAK
This predicate has not been subject to a design-related recall
REFERENCE DEVICE 4
ClariCT.AI by ClariPI Inc.(K183460)
Name of Device: ClariCT.AI Common or Usual Name: Image Processing Software Classification Name: System, image processing, radiological (21CFR 892.2050) Regulatory Class: II Product Code: LLZ
4
Broncholab by Fluidda Inc.(K191550)
Name of Device: Broncholab Common or Usual Name: Image Processing Software Classification Name: System, X-Ray, Tomography, Computed (21CFR 892.1750) Regulatory Class: II Product Code: JAK
This reference device has not been subject to a design-related recall
DEVICE DESCRIPTION 5
The AVIEW is a software product that can be installed on a PC. It shows images taken with the interface from various storage devices using DICOM 3.0, the digital image and communication standard in medicine. It also offers functions such as reading, manipulation, analyzing, post-processing, saving, and sending images by using software tools. And is intended for use as a quantitative analysis of CT scanning. It provides the following features such as segmentation of lung, lobe, airway, fissure completeness, semi-automatic nodule management, maximal plane measures and volumetric measures, automatic nodule detection by integration with 3rd party CAD. It also provides the Brocks model, which calculates the malignancy score based on numerical or Boolean inputs. Follow-up support with automated nodule matching and automatically categorize Lung-RADS score, which is a quality assurance tool designed to standardize lung cancer screening CT reporting and management recommendations that are based on type, size, size, size, size, size, size, size, size, size, size, size, size change, and other findings that are reported. It also provides a calcium score by automatically analyzing coronary arteries from the segmented arteries
INDICATIONS FOR USE 6
AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This software can be used to support the physician providing quantitative analysis of CT images by image segmentation of sub-structures in the lung. lobe, airways, fissures completeness, cardiac, density evaluation, and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on-premises and as a cloud environment to allow users to connect by various environments such as mobile devices and Chrome browsers. Converts the soft kernel for quantitative analysis of segmenting low attenuation areas of the lung. Characterizing nodules in the lung in a single study or over the time course of several thoracic studies. Characterizations include type, location of the nodule, and measurements such as size (major axis), estimated effective diameter from the volume of the nodule, the volume of the nodule, Mean HU(the average value of the CT pixel inside the nodule in HU), Max HU, mass(mass calculated from the CT pixel value), and volumetric measures(Solid major, length of the longest diameter measure in 3D for a solid portion of the nodule, Solid 2nd Major: The size of the solid part, measured in sections perpendicular to the Major axis of the nodule), VDT (Volume doubling time), and Lung-RADS (classification proposed to aid with findings.)). The system automatically performs the measurement. allowing lung nodules and measurements to be displayed and, integrate with FDA certified Mevis CAD (Computer aided detection) (K043617). It also provides the Agatston score, and mass score by the whole and each artery by segmenting four main arteries (right coronary artery, left anterior descending, and left circumflex artery). Based on the calcium score provides CAC risk based on age and gender. The device is indicated for adult patients only.
5
COMPARISION OF TECHNOLOGICAL CHARACTERISTICS WITH 7 THE PREDICATE DEVCIE
AVIEW has the same intended use and the principle of operation and has similar features to the predicate devices. AVIEW (K200714)
There might be slight differences in features and menu, but these differences between the predicate device and the proposed device are not so significant since they do not raise any new or potential safety risks to the user or patient and questions of safety or effectiveness. Based on the results of software validation and verification tests, we conclude that the proposed device is substantially equivalent to the predicate devices.
| Characteristic | Subject Device | Predicate Device | Reference Device | Reference Device |
|---|---|---|---|---|
| Device Name | AVIEW | AVIEW | ClariCT.AI | Broncholab |
| Classification | ||||
| Name | System, image | |||
| Processing | ||||
| Radiological | System, image | |||
| Processing | ||||
| Radiological | System, image | |||
| Processing | ||||
| Radiological | System, X-Ray, | |||
| Tomography, | ||||
| Computed | ||||
| Regulatory | ||||
| Number | 21 CFR 892.2050 | 21 CFR 892.2050 | 21 CFR 892.2050 | 21 CFR 892.1750 |
| Product Code | QIH, JAK | LLZ, JAK | LLZ | JAK |
| Review Panel | Radiology | Radiology | Radiology | Radiology |
| 510k Number | - | K200714 | K183460 | K191550 |
| Indications for | ||||
| use | AVIEW | |||
| AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This | ||||
| software can be used to support the physician providing quantitative analysis of CT images by | ||||
| image segmentation of sub-structures in the lung, lobe, airways, fissures completeness, cardiac, | ||||
| density evaluation, and reporting tools. AVIEW is also used to store, transfer, inquire and | ||||
| display CT data set on-premises and as a cloud environment to allow users to connect by | ||||
| various environments such as mobile devices and Chrome browsers. Converts the sharp kernel | ||||
| to soft kernel for quantitative analysis of segmenting low attenuation areas of the lung. | ||||
| Characterizing nodules in the lung in a single study or over the time course of several thoracic | ||||
| studies. Characterizations include nodule type, location of the nodule, and measurements such | ||||
| as size (major axis, minor axis), estimated effective diameter from the volume of the nodule, | ||||
| the volume of the nodule, Mean HU(the average value of the CT pixel inside the nodule in | ||||
| HU), Minimum HU, Max HU, mass(mass calculated from the CT pixel value), and volumetric | ||||
| measures(Solid major; length of the longest diameter measure in 3D for a solid portion of the | ||||
| nodule, Solid 2nd Major: The size of the 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 the Agatston score, volume score, and mass score by the whole and | ||||
| each artery by segmenting four main arteries (right coronary artery, left main coronary, left | ||||
| anterior descending, and left circumflex artery). Based on the calcium score provides CAC risk | ||||
| based on age and gender. The device is indicated for adult patients only. |
6
| General
Description | 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 course of several thoracic studies. Characterizations include nodule type,
location of the nodule 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. |
|-----------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | ClariCT.AI
ClariCT.AI, is a software device intended for networking, communication, processing, and
enhancement of CT images in DICOM format regardless of the manufacturer of CT scanner or
model. |
| | Broncholab |
| | Broncholab provides physicians with reproducible CT values for pulmonary tissue for
providing quantitative support for diagnosis and follow-up examination. Broncholab can be
used to support physicians in the diagnosis and documentation of pulmonary tissues images
(e.g., abnormalities) from CT thoracic datasets. Three-D segmentation and isolation of
subcompartments, volumetric analysis, density evaluations, low density cluster analysis,
fissure evaluation and reporting tools are combined with a dedicated workflow. |
| | AVIEW |
| General
Description | The AVIEW is a software product that can be installed on a PC. It shows images taken with the
interface from various storage devices using DICOM 3.0, the digital image and communication
standard in medicine. It also offers functions such as reading, manipulation, analyzing, post-
processing, saving, and sending images by using software tools. And is intended for use as a
quantitative analysis of CT scanning. It provides the following features such as segmentation
of lung, lobe, airway, fissure completeness, semi-automatic nodule management, maximal
plane measure, 3D measures and volumetric measures, automatic nodule detection by
integration with 3rd party CAD. It also provides the Brocks model, which calculates the
malignancy score based on numerical or Boolean inputs. Follow-up support with automated
nodule matching and automatically categorize Lung-RADS score, which is a quality assurance
tool designed to standardize lung cancer screening CT reporting and management
recommendations that are based on type, size, size change, and other findings that are reported.
It also provides a calcium score by automatically analyzing coronary arteries from the
segmented arteries. |
| | 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 3rd party CAD. Also provides Brocks model which calculated the malignancy | |
| score based on numerical or Boolean inputs. Follow up support with automated nodule | |
| matching and automatically categorize Lung-RADS score which is a quality assurance tool | |
| designed to standardize lung cancer screening CT reporting and management recommendations | |
| that is based on type, size, size change and other findings that is reported. It also automatically | |
| analyzes coronary artery calcification which support user to detect cardiovascular disease in | |
| early stage and reduce the burden of medical. | |
| Callery of Children Company of Children | |
7
ClariCT.AI
ClariCT.AI software is intended for denoise processing and enhancement of CT DICOM images when higher image quality and/or lower dose acquisitions are desired. ClariCT.AI software can be used to reduce noises in CT images of the head, chest, and abdomen, in particular in CT images with a lower radiation dose. ClariCT.AI may also improve the image quality of low dose nonpdiagnostic Filtered Back Projection images as well as Iterative Reconstruction images.
The system enables the receipt of DICOM images from CT imaging devices (modalities), enables their denoise processing and enhancement, and transmission to a PACS workstation.
Broncholab
Broncholab is a SaMD (Software as Medical Device) which provides quantitative CT values that are intended to support the physician in the diagnosis and documentation of pulmonary tissues images (abnormalities) from CT scans. The CT scan images are transformed into 3D models of the patient-specific lungs using several image processing steps. Broncholab can be used to assess the effectiveness of therapy based on CT scan data. It is used along with the following accessories:
- Web Portal: Enables the uploading of CT scans and patient data
- . Client Report: Enables the conversion of the output of Broncholab (CT values) into a desired digital format (PDF
Report) and transfers this Report to the physician via email.
Inspiratory CT scan images uploaded by the users are converted into quantitative CT values using a combination of software tools. Quality checks (both manual and automated) are implemented to assure the quality of the final data.
The outputs are provided as absolute values and as a percentage of the total airway volume/ lung volume/ lobar volume depending on the parameter. The device can be used on a computer with a web browser installed and consists of two accessories:
- An online portal to upload the CT scans
- . An accessory that enables the creation of the Report
The CT values include:
- Lung and Lobar Volume is the volume of the 3D model of each lung lobe. 1)
-
- Airway Volume is defined as the region from the trachea until the segmental bronchi.
- Lung Density Scores/ Volumes is defined as all the intrapulmonary voxels with 3) Hounsfield Units between -1024
and -950 using the inspiratory scans:
- . Low attenuation areas below -950 HU (LAA-950HU)
- . 15th percentile of density histogram: Percentile density (PD) can also be used to express Emphysema.
- Blood vessel density: Blood vessel density can be determined through segmentation o and 3-D reconstruction of the blood vessels. The segmentation is based on local
8
| geometry features and HU thresholds and is performed on the inspiratory CT scan.
4) Fissure Analysis (fissure integrity) is the percentage of completeness of the fissure.
Lung fissures are a doublefold of visceral pleura that either completely or
| incompletely separates the lungs into lung lobes. | |||
|---|---|---|---|
| CT scan images must be DICOM 3.0 compliant. | |||
| Platform | IBM-compatible PC or PC network | same | |
| User Interface | Monitor, Mouse, Keyboard | same | |
| Image Input Sources | Images can be scanned, loaded from card readers, or imported from a radiographic imaging device | same | |
| Image format | DICOM | same | |
| Image Measurement Tools | Ruler (line and 3D), Tapeline (curve, poly and3D), Angle (3-point, 4point, and 3D), pixel values, area of ROI (rectangle, circle, ellipse), volume | same | |
| Image viewing | Axial, sagittal, and coronal image, oblique slice, cube view | same | |
| Image manipulation | Panning, rotating, zooming, windowing, inverting, Coloring, Oblique, Note (text overlay), Coloring | same | |
| DICOM | This receives DICOM data from CT by DICOM communication | ||
| Conducts DICOM data communication with PACS. It also imports DICOM file directly, saves by using export function. | same | ||
| Lung Analysis Functions | Fully automatic lungs, lobes and airways segmentation using deep-learning algorithms | Fully automatic lungs, lobes and airways segmentation using deep-learning algorithms | |
| Semi-automatic segmentation of lungs, lobes, and airways. | same | ||
| Visualization of multi- | same | ||
| planar reconstructed | |||
| (MPR) images and 3D | |||
| rendered images, with | |||
| color-defined | |||
| Hounsfield Unit (HU) | |||
| ranges. | |||
| Calculation of LAA | |||
| (Lower Attenuation | |||
| Area) index with HU | |||
| density histogram. | |||
| Volume measurements | |||
| and percentile index | same | ||
| Calculation of LAA | |||
| cluster size | |||
| distribution with D- | |||
| slope | same | ||
| Graphical | |||
| visualization of the | |||
| above quantification | |||
| results for reporting | same | ||
| Export of | |||
| quantification results | |||
| to CSV tables | same | ||
| Visualization of | |||
| LAA% for each of 5 | |||
| lobes | same | ||
| Measurements of the | |||
| airway branches, such | |||
| as, lumen area and wall | |||
| area | same | ||
| Analyzes Air Trapping | |||
| Index by registration | |||
| of inspiration and | |||
| expiration data. Could | |||
| compare both IN/EX | |||
| after the registration | same | ||
| Fully automatic | |||
| INSP/EXP registration | |||
| (non-rigid elastic) | |||
| algorithm. | same | ||
| Quantiatively evaluate | |||
| fissure integrity ratio | |||
| and display it by | |||
| projected fissure | |||
| image and 3D screen | |||
| and chart. | Fissure Analysis | ||
| (fissure integrity) is | |||
| the percentage of | |||
| completeness of the | |||
| fissure. Lung fissures | |||
| are a doublefold | |||
| of visceral pleura | |||
| that either | |||
| completely or | |||
| incompletely | |||
| separates the lungs | |||
| into lung lobes |
9
10
core:line__
| Nodule Characteristics | ||||
|---|---|---|---|---|
| Lung Cancer | ||||
| Screening | Automatic calculation | |||
| of measurements for each segmented nodule Size of the Major axis and Minor axis(mm) Diameter of Major (3D), 2nd Major (3D), Major(2D), Minor(2D) (mm) Volume(mm³) Max, Min, Mean HU of the nodule((HU) Cancer probability (%) | same | |||
| Comparison and Matching | ||||
| Comparison and matching automatic calculations between each follow-up scan and the baseline scan Doubling time in days Indicated the change of the size Auto generate Lung-RADS | same | |||
| Loading multiple studies | same | |||
| Workflow Detect and Segment Comparison and Matching Results Option to integrate with 3rd party CAD which automatically detects the nodules and generate report Supporting Low-dose CT | same | |||
| Reporting results | ||||
| The results include the following. Lung-RADS | same | |||
| PANCAN risk calculator Auto detect nodule location by lobe | ||||
| Supports kernel conversion of LAA on LCS Page | Noise reduction is performed with the use of pre-trained deep learning models. | |||
| Cardiac (CAC) | Extracting Calcium on Coronary Artery and provides Agatston score, volume score and mass score. | same | ||
| Automatically segments calcium area of coronary artery based on deep learning. | same | |||
| Thin client service | Connected from anywhere, anyplace, anytime Supports mobile view through various mobile devices served by ios and Android. Comparable with Chrome browser | same | ||
| Easy processing management | Rule-based automatic processing server (APS) | same |
11
8 PERFORMANCE DATA
8.1 Nonclinical Performance Testing
This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device. The substantial equivalence of the device is supported by the non-clinical testing
8.2 Software Verification and Validation
Verification, validation and testing activities were conducted to establish the performance, functionality and reliability characteristics of the modified device passed all of the tests based on pre-determined Pass/Fail criteria.
- -Unit Test
Conducting Unit Test using Google C++ Unit Test Framework on major software components identified by
12
software development team. List of Unit Test includes Functional test condition for software component unit, Performance test condition, and part of algorithm analysis for image processing algorithm.
- System Test
In accordance with the document 'integration Test Cases' discussed in advanced by software development team and test team, test is conducted by installing software with recommended system specification. Despite Test case recognized in advance was not in existence. New software error discovered by 'Exploratory Test' conducted by test team will be registered and managed as new test case after discussion between development team and test team.
Discovered software error will be classified into 3 categories as severity and managed.
- Major defects, which are impacting the product's intended use and no workaround is available. >
- V Moderate defects, which are typically related to user-interface or general quality of product, while workaround is available.
V Minor defects, which aren't impacting the product's intended use. Not significant. Success standard of System Test is not finding 'Major', 'Moderate' defect.
- Performance Test -
- · Nodule Matching Experiment Using Lung Registration
- · LAA Comparative Experiment for Performance Evaluation of Denoised CT
- Semi-automatic Nodule Segmentation .
- Brock Model (ask PANCAN) Calculation .
- . VDT Calculation
- Lung RADS Calculation .
- . MeVis CAD Integration
- Validation LAA Analysis ●
- Validation LAA Size Analysis ●
- . Size analysis algorithm of LAA clusters
- Lung Registration .
- Reliability Test for Airway wall Measurement .
- . Fissure Completeness
- CAC Performance Evaluation of Denoised CT ●
- Validation on DVF Size Optimization with Sub-sampling .
- Airway Segmentatation ●
- · Auto Lung & Lobe Segmentation
- Kernel Conversion .
- A The LAA result on kernel converted sharp image should have higher reliability with the soft kernel than LAA results on sharp kernel image that is not Kernel Conversion applied. Of the 96 total, 53 are U.S. population and 43 are Korean.
- . Fissure Completeness
- A Fissure completeness was validated using a total of 129 subjects from TCIA (the Cancer Imaging Archive) LIDC database. The performance was evaluated using Bland Altman plots to assess the fissure completeness performance compared to radiologists. Kappa and ICC were also reported.
13
CONCLUSIONS 9
The new device and predicate device are substantially equivalent in the areas of technical characteristics, general functions, application, and intended use. The new device does not introduce a fundamentally new scientific technology, and the nonclinical tests demonstrate that the device is safe and effective. Therefore, it is our opinion that the AVIEW described in this submission is substantially equivalent to the predicate device.