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AmCAD-UV is a software device designed for classifying the ultrasonic color intensity data and allowing users to view classified color-coded signals, namely, primary pulsatile, and unidentified signals of flow Doppler ultrasound images. It is intended as a general-purpose medical image processing tool for vascular pulsatility analysis but must not be used alone for primary diagnostic interpretation.

AmCAD-UV (model number 1.0) is a software device designed for classifying the ultrasonic color intensity data and allowing users to view classified color-coded signals, namely, primary pulsatile, secondary pulsatile, and unidentified signals of flow Doppler ultrasound images. The ultrasonic color intensity data here means the flow Doppler ultrasound images (i.e. color and power Doppler ultrasound images) acquired from FDA-cleared ultrasound systems. The device quantifies those color pixels within the quadrilateral scanning area on a sequence of flow Doppler ultrasound images based on their color intensities and groups the color pixels with similar periodic pulsatile waveforms. The image with classified color-coded pulsatile signals will then be generated by the proposed device for users to evaluate the vascular pulsatility, such as pulsatile flow velocity and flow energy, dependent on the type of flow Doppler ultrasound image analyzed. As a PACS (Picture Archiving and Communication System) software device, AmCAD-UV does not generate new quantities but provide pulsatile information of existing information from flow Doppler ultrasound images. The device is intended as a general-purpose medical image processing tool for vascular pulsatility analysis but must not be used alone for primary diagnostic interpretation. AmCAD-UV provides dual images for viewing original flow Doppler ultrasound images and the image with classified color-coded pulsatile signals. The user can delineate a specific region of interest (ROI) on the image for analysis. The device also provides a trend chart for displaying pulsatile waveforms with summarized statistics. The device can export the quantified values of the classified pulsatile signals in text format and export the sequence of color-coded pulsatile images and the waveform trend chart in Bitmap (.bmp) and JPEG (.jpg or *.jpeg) formats.

The provided text describes a 510(k) premarket notification for a device named AmCAD-UV, a software device for classifying ultrasonic color intensity data. Based on the document, here's a breakdown of the acceptance criteria and the study that proves the device meets them:

Disclaimer: The provided document is a 510(k) summary, which often provides high-level information. While it mentions "performance validation testing (i.e., human validation study)," it does not explicitly detail the quantitative acceptance criteria, the specific metrics used (e.g., accuracy, sensitivity, specificity), or the detailed results of that study in a format that allows for a direct population of a "reported device performance" column with numerical values for specific criteria. It primarily focuses on demonstrating substantial equivalence to a predicate device for its intended use as a general-purpose image processing tool, rather than presenting a comparative effectiveness study or a standalone performance study with specific numerical thresholds for diagnostic accuracy.

Therefore, the table below will reflect the stated purpose of the validation study and the general conclusion that the device "meets all functional and specifications for its indications for use" and "can be used to visualize and quantify" the signals, implying successful performance without explicit numerical targets.

Acceptance Criteria and Device Performance

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

Study Details:

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

The document mentions "performance validation testing (i.e. human validation study)" but does not specify the sample size for the test set (number of patients, images, or cases). It also does not specify the data provenance (e.g., country of origin, retrospective or prospective collection).

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

The document does not specify the number of experts or their qualifications used to establish ground truth for the test set.

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

The document does not specify any adjudication method for the test set.

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

The document describes a "human validation study," but it's presented as a validation of the device's ability to "visualize and quantify" the signals, rather than a comparative effectiveness study evaluating human reader performance with and without AI assistance. Therefore, no MRMC comparative effectiveness study or effect size for human reader improvement is reported. The device is positioned as a general-purpose medical image processing tool that "must not be used alone for primary diagnostic interpretation," implying its role is to assist visualization and quantification rather than replace or directly augment diagnostic decision-making in a comparative effectiveness setting.

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

The document mentions "software verification testing, including software unit test, software integration test, and software system test," which are internal tests to ensure the software meets its functional specifications. However, no dedicated standalone performance study with metrics like accuracy, sensitivity, or specificity for diagnostic classification by the algorithm alone is described or reported. The device's stated function is to classify and visualize signals for user interpretation, not to provide an automated diagnosis.

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

The document does not explicitly state the type of ground truth used for the human validation study. Given the device's function (classifying color intensity and visualizing pulsatile signals), the ground truth would likely relate to the accurate representation/quantification of these signals based on the raw Doppler ultrasound data, potentially evaluated by experts. However, this is inferential.

8. The sample size for the training set

The document does not provide information regarding a "training set" or its sample size. The focus is on the validation study rather than the development or training of an AI model, which might suggest this device does not utilize a machine learning model that requires a distinct "training set" in the conventional sense (or such details are not required for this type of 510(k)). It primarily describes itself as a "software device designed for classifying the ultrasonic color intensity data," implying a rule-based or algorithmic classification rather than a learned one.

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

As no training set is described, there is no information on how its ground truth was established.

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