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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), estimated effective diameter from the volume of 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 Maior: 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 AVIEW Lung Nodule CAD (Computer aided detection) (K221592). 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.

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, 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 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.

The provided text is a 510(k) premarket notification letter and summary for a medical device called "AVIEW." This document primarily asserts substantial equivalence to a predicate device and notes general software changes rather than providing detailed acceptance criteria and study results for specific performance metrics that would typically be found in performance study reports.

Specifically, the document states: "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 substantial equivalence of the device is supported by the non-clinical testing." This means that the submission does not include information about a standalone or MRMC study designed to prove the device meets specific performance acceptance criteria for its analytical functions.

Therefore, I cannot provide the requested information from the given text as the detailed performance study data is not present. The document focuses on regulatory equivalence based on technological characteristics and intended use being similar to a predicate device, rather than providing new performance study data.

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AVIEW CAC provides quantitative analysis of calcified plaques in the coronary arteries using non-contrast/non-gated Chest CT scans. It enables the calculation of the Agatston score for coronary artery calcification, segmenting and evaluating the right coronary artery and left coronary artery. Also provide risk stratification based on calcium score, gender, and age, offering percentile-based risk categories by established guidelines. Designed for healthcare professionals, including radiologists and cardiologists, AVIEW CAC supports storing, transferring, inquiring, and displaying CT data sets on-premises, facilitating access through mobile devices and Chrome browsers. AVIEW CAC analyzes existing non-contrast/non-gated Chest CT studies that include the heart of adult patients above the age of 40. Also, the device's use should be limited to CT scans acquired on General Electric (GE) or its subsidiaries (e.g., GE Healthcare) equipment. Use of the device with CT scans from other manufacturers has not been validated or recommended.

The AVIEW CAC 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 also provides a calcium score by automatically analyzing coronary arteries from the segmented arteries.

The provided text indicates that the device, AVIEW CAC, calculates the Agatston score for coronary artery calcification from non-contrast/non-gated Chest CT scans. It segments and evaluates the right and left coronary arteries and provides risk stratification based on calcium score, gender, and age, using percentile-based risk categories by established guidelines. The device is for healthcare professionals (radiologists and cardiologists) and analyzes existing CT studies from adult patients over 40 years old, acquired on GE equipment.

The document states that a clinical study was not considered necessary and that non-clinical testing supports the substantial equivalence of the device to its predicate. However, it does not provide specific acceptance criteria or an explicit study description with performance metrics for the AVIEW CAC device. It states that the device is substantially equivalent to a predicate device (K233211, also named AVIEW CAC) and that the substantial equivalence is supported by non-clinical testing.

Therefore, many of the requested details about acceptance criteria, specific performance metrics, sample sizes, expert involvement, and ground truth establishment are not present in the provided text.

Based on the available information:

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

The document does not explicitly state acceptance criteria or report specific device performance metrics in a tabular format. It generally states that "the results of the software verification and validation tests concluded that the proposed device is substantially equivalent" and "the nonclinical tests demonstrate that the device is safe and effective."

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):

This information is not provided in the document.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience):

This information is not provided in the document.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

This information is not provided in the document.

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

A MRMC comparative effectiveness study is not mentioned in the document. The document explicitly states: "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 indications for use is equivalent to the predicate device. The substantial equivalence of the device is supported by the non-clinical testing."

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

The document implies that the "nonclinical tests" evaluated the device's performance, which would typically involve standalone algorithm performance. However, specific details about such a study or its results are not provided. The device's function is centered on automatic analysis (calculation of Agatston score, segmenting and evaluating arteries).

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

This information is not provided in the document.

8. The sample size for the training set:

This information is not provided in the document.

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

This information is not provided in the document.

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AVIEW CAC provides quantitative analysis of calcified plaques in the coronary arteries using non-contrast non-gated Chest CT scans. It enables the calculation of the Agatston score for coronary artery calcification, segmenting and evaluating the right coronary artery and left coronary artery. Also provide risk stratification based on calcium score, gender, and age, offering percentile-based risk categories by established guidelines. Designed for healthcare professionals, including radiologists and cardiologists, AVIEW CAC supports storing, inquiring, and displaying CT data sets on-premises, facilitating access through mobile devices and Chrome browsers. AVIEW CAC analyzes existing noncontrast/non-gated Chest CT studies that include the heart of adult patients above the age of 40. Also, the device's use should be limited to CT scans acquired on General Electric (GE) or its subsidiaries (e.g., GE Healthcare) equipment. Use of the device with CT scans from other manufacturers has not been validated or recommended.

The AVIEW CAC 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 also provides a calcium score by automatically analyzing coronary arteries from the segmented arteries.

The provided text describes the acceptance criteria and the study conducted for the AVIEW CAC device.

Here's the breakdown of the information requested:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the quantitative analysis of calcified plaques is primarily based on the Intraclass Correlation Coefficient (ICC) of the Agatston score against a ground truth and a predicate device.

Note: The acceptance criterion for ICC is implied by the p-value requirement ">0.8", which usually signifies a strong correlation. The reported ICC values are all above this implied threshold.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size:
  • 150 CSCT (gated) cases
  • 150 Chest CT (non-gated) cases
  • Additionally, 280 datasets collected from multiple institutions were used for a separate "MI functionality test report" which also evaluated correlation.
• Data Provenance: The document does not explicitly state the country of origin. The test cases were derived from "multiple institutions". It is implied to be retrospective as the device analyzes "existing" non-contrast/non-gated chest CT studies.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The document does not specify the number of experts used or their detailed qualifications (e.g., "radiologist with 10 years of experience") for establishing the ground truth.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (such as 2+1, 3+1) for establishing the ground truth. It simply refers to "ground truth" without detailing its consensus process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, If So, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study focuses on the standalone performance of the algorithm against a defined ground truth and comparison against a predicate device, not on human reader performance with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone performance study was done. The performance data section explicitly states, "we evaluated the agreement in A coronary calcium scoring between the subject device and the ground truth" and "the correlation coefficient A between the AVIEW CAC automatic analysis results of the chest CT based on the heart CT and the Agatston scores was over 90%". This indicates the algorithm's performance without human intervention.

7. The Type of Ground Truth Used

The ground truth used was Agatston scores for coronary artery calcification. The document does not specify if this ground truth was established by expert consensus of human readers, pathology, or outcomes data. However, the comparison is made to "Ground Truth" for Agatston Score measurements, which implies a highly reliable, perhaps manually derived or reference Agatston score.

8. The Sample Size for the Training Set

The document does not provide the sample size for the training set. It only mentions the test set sizes.

9. How the Ground Truth for the Training Set Was Established

The document does not describe how the ground truth for the training set was established. It only refers to deep learning for automatic segmentation but does not detail the process for creating the ground truth data used to train the deep learning model.

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AVIEW LCS is intended for the review and analysis and reporting of thoracic CT images for the purpose of 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), estimated effective diameter from the volume of 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 measured in 3D for a 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 nodule), VDT (Volume doubling time), Lung-RADS (classification proposed to aid with findings) and CAC score and LAA analysis. The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, also integrate with FDA certified Mevis CAD (Computer-aided detection) (K043617).

AVIEW LCS is intended for use as diagnostic patient imaging which is intended for the review and analysis of thoracic CT images. Provides following features as semi-automatic nodule measurement (segmentation), maximal plane measure, 3D measure and volumetric measures, automatic nodules detection by integration with 3th party CAD. Also provides cancer risk based on PANCAN risk 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 is based on type, size, size change and other findings that is reported.

The provided text does not contain detailed acceptance criteria for specific performance metrics of the AVIEW LCS device, nor does it describe a study proving the device meets particular acceptance criteria with quantitative results.

The document is a 510(k) premarket notification summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed performance study like a clinical trial.

However, based on the information provided, here's what can be extracted and inferred regarding performance and testing:

1. Table of Acceptance Criteria and Reported Device Performance

As specific quantitative acceptance criteria and detailed performance metrics are not explicitly stated in the provided text for AVIEW LCS, I cannot create a table of acceptance criteria and reported device performance. The document generally states that "the modified device passed all of the tests based on pre-determined Pass/Fail criteria" for software validation.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify the sample size used for any test set or the data provenance (e.g., country of origin, retrospective/prospective). The described "Unit Test" and "System Test" are internal software validation tests rather than clinical performance studies involving patient data.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not mention using experts to establish ground truth for a test set. This type of information would typically be found in a clinical performance study.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method for a test set. This is relevant for clinical studies where multiple readers assess cases.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was performed. Therefore, no effect size of human readers improving with AI vs. without AI assistance is mentioned.

6. Standalone (Algorithm Only) Performance Study

The document does not explicitly state that a standalone (algorithm only without human-in-the-loop performance) study was conducted. The "Performance Test" section refers to DICOM, integration, and thin client server compatibility reports, which are software performance tests, not clinical efficacy or diagnostic accuracy studies for the algorithm itself. The device description mentions "automatic nodules detection by integration with 3rd party CAD (Mevis Visia FDA 510k Cleared)", suggesting it leverages an already cleared CAD system for detection rather than having a new, independently evaluated detection algorithm as part of this submission.

7. Type of Ground Truth Used

The document does not specify the type of ground truth used for any performance evaluation. Again, this would be characteristic of a clinical performance study.

8. Sample Size for the Training Set

The document does not provide the sample size for any training set. This is typically relevant for AI/ML-based algorithms. The mention of "deep-learning algorithms" for lung and lobe segmentation suggests a training set was used, but its size is not disclosed.

9. How the Ground Truth for the Training Set Was Established

The document does not explain how ground truth for any potential training set was established.

Summary of available information regarding testing:

The "Performance Data" section (8) of the 510(k) summary focuses on nonclinical performance testing and software verification and validation activities.

• Nonclinical Performance Testing: The document states, "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 nonclinical testing." This indicates the submission relies on the substantial equivalence argument and internal software testing, not new clinical performance data for efficacy.
• Software Verification and Validation:
  • Unit Test: Conducted using Google C++ Unit Test Framework on major software components for functional, performance, and algorithm analysis.
  • System Test: Conducted based on "integration Test Cases" and "Exploratory Test" to identify defects.
    • Acceptance Criteria for System Test: "Success standard of System Test is not finding 'Major', 'Moderate' defect."
    • Defect Classification:
      • Major: Impacting intended use, no workaround.
      • Moderate: UI/general quality, workaround available.
      • Minor: Not impacting intended use, not significant.
  • Performance Test Reports: DICOM Test Report, Performance Test Report, Integration Test Report, Thin Client Server Compatibility Test Report.

In conclusion, the provided 510(k) summary primarily addresses software validation and verification to demonstrate substantial equivalence, rather than a clinical performance study with specific acceptance criteria related to diagnostic accuracy, reader performance, or a detailed description of ground truth establishment.

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A VIEW provides CT values for pulmonary tissue from CT thoracic datasets. This software can be used to support the physician quantitatively in the diagnosis, followup evaluation of CT lung tissue images by providing image segmentation of sub-structures in the left and right lung (e.g., the five lobes and airway), volumetric and structural analysis, density evaluations and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data sets. A VIEW is not meant for primary image Interpretation in mammography.

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.

The provided text describes the AVIEW software, a medical device for processing CT thoracic datasets, and its substantial equivalence to predicate devices. However, the document does not contain the specific details required to fully address your request regarding acceptance criteria and the study that proves the device meets them.

Here's a breakdown of what information is available and what is missing:

Information Available:

• Indications for Use: AVIEW "provides CT values for pulmonary tissue from CT thoracic datasets. This software can be used to support the physician quantitatively in the diagnosis, followup evaluation of CT lung tissue images by providing image segmentation of sub-structures in the left and right lung (e.g., the five lobes and airway), volumetric and structural analysis, density evaluations and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data sets. AVIEW is not meant for primary image Interpretation in mammography."
• Performance Data: "Verification, validation and testing activities were conducted to establish the performance, functionality and reliability characteristics of the modified devices. The device passed all of the tests based on pre-determined Pass/Fail criteria."
• Tests Conducted:
  • Unit test
  • System test
  • DICOM test
  • LAA analysis test
  • LAA size analysis test
  • Airway wall measurement test
  • Reliability test
  • CT image compatibility test
• Conclusion: The device is deemed "substantially equivalent to the predicate device" based on "technical characteristics, general functions, application, and intended use," and "nonclinical tests demonstrate that the device is safe and effective."

Information Missing (and why based on the document):

1. A table of acceptance criteria and the reported device performance: While various tests are listed (e.g., LAA analysis test, Airway wall measurement test), the document explicitly states these are "nonclinical tests." It does not provide specific quantitative acceptance criteria or corresponding reported device performance values for these tests. The nature of these tests appears to be functional and reliability-focused rather than clinical performance metrics. For example, it doesn't state "AVIEW achieved X% accuracy for LAA analysis against ground truth Y" or "Airway wall measurement deviation was within Z mm."

2. Sample size used for the test set and the data provenance: The document mentions "CT thoracic datasets" but does not specify the sample size for any test set or the provenance (e.g., country of origin, retrospective/prospective nature) of the data used for testing.

3. Number of experts used to establish the ground truth for the test set and their qualifications: The document states, "Results produced by the software tools are dependent on the interpretation of trained and licensed radiologists, clinicians and referring physicians as an adjunctive to standard radiology practices for diagnosis." However, it does not specify how many, if any, experts were used to establish ground truth for the test set, nor their specific qualifications, for the performance testing cited.

4. Adjudication method for the test set: No information is provided regarding any adjudication methods (e.g., 2+1, 3+1) used for establishing ground truth for the test set.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance: The document explicitly states that AVIEW is "not meant for primary image Interpretation in mammography" and that its results "are dependent on the interpretation of trained and licensed radiologists, clinicians and referring physicians as an adjunctive to standard radiology practices for diagnosis." This suggests it's an assistive tool, but no MRMC study comparing human readers with and without AI assistance, or any effect size, is mentioned. The "Performance Data" section focuses on "nonclinical tests" for software functionality.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The provided detail about "LAA analysis test," "LAA size analysis test," and "Airway wall measurement test" implies standalone algorithmic performance was assessed in these nonclinical tests. However, specific performance metrics (e.g., accuracy, precision, recall) from a standalone evaluation are not provided.

7. The type of ground truth used: For the mentioned performance tests (e.g., LAA, airway wall measurement), the type of ground truth used is not explicitly specified. It's implied that these are technical validations against known values or established methods, but whether this involved expert consensus on clinical cases, pathology, or outcomes data is not detailed.

8. The sample size for the training set: No information is provided about a training set or its size, as the document refers to "Verification, validation and testing activities" as "nonclinical tests" demonstrating substantial equivalence, not a machine learning model's development.

9. How the ground truth for the training set was established: Since no training set information is provided, this cannot be answered.

In summary, the document serves as an FDA 510(k) clearance letter and summary, which primarily focuses on demonstrating "substantial equivalence" to predicate devices through technical characteristics and "nonclinical tests" for functionality and reliability. It does not provide the detailed clinical performance study data that would include specific acceptance criteria, sample sizes (for test or training sets), expert qualifications, or ground truth establishment methods typical for AI-based diagnostic/assistive tools evaluated for quantitative clinical outcomes.

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