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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® is intended for use in conjunction with TEE investigation of the upper mediastinum in anesthetized patients. They allow visibility of the distal ascending aorta by TEE and permit the condition of the ascending aorta to be evaluated before surgery. A-View® is limited for use in adult patients.

The A-View® is a catheter with an inflatable balloon that is inserted through an endotracheal tube of an anesthetized patient. Once inserted the balloon is inflated with saline. This inflated balloon is placed near the aortic arch so that when the clinician is performing a TEE (transesophageal echocardiography) procedure the saline filled balloon provides better contrast for an image.

The provided text describes the 510(k) premarket notification for the A-View® device, which is a diagnostic ultrasonic transducer. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study to prove the device meets acceptance criteria through clinical performance testing. Therefore, much of the requested information regarding clinical studies, ground truth, expert adjudication, and comparative effectiveness studies is not included in this type of submission.

However, based on the non-clinical performance testing conducted, I can extract information related to acceptance criteria and performance for certain device characteristics.

Here's the breakdown of the available information:

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

The document details bench tests conducted and states that the A-View® met all performance requirements and specifications, supporting substantial equivalence to the predicate device. Specific acceptance criteria are not explicitly quantifiable values in the provided text (e.g., "leakage less than X ml/hr"), but rather a general statement of meeting requirements.

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

The document does not specify a "test set" in the context of clinical data. It discusses bench tests for device performance. Sample sizes for these non-clinical tests (e.g., number of devices tested for leakage) are not provided. There is no mention of country of origin for any clinical data because none was presented. The testing described is non-clinical/bench testing, not retrospective or prospective clinical studies.

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

This information is not applicable. No clinical "test set" with ground truth established by experts is mentioned or described in this 510(k) summary. The device performance was assessed via bench testing, not expert interpretation of clinical data.

4. Adjudication method for the test set

This information is not applicable as there was no clinical "test set" requiring expert adjudication.

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

This information is not applicable. The A-View® device is a physical diagnostic ultrasonic transducer, not an AI-powered diagnostic tool. No MRMC study was conducted or reported.

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

This information is not applicable as the A-View® device is not an algorithm.

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

For the non-clinical performance testing (bench tests), the "ground truth" would be established by the engineering and quality standards set for each test (e.g., a specific pressure maintained for a leak test, a measured force for a tensile test). These are objective physical measurements rather than clinical ground truth types like pathology or expert consensus.

8. The sample size for the training set

This information is not applicable. The A-View® is a physical medical device, not an AI or machine learning algorithm that requires a training set.

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

This information is not applicable for the same reason as above (not an AI/ML algorithm).

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