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UW LV Analysis Software assists in measuring left ventricular volume at end diastole and end systole, ejection fraction, and regional wall motion around the ventricular contour. The intended applications of the software are: 1) to assist in evaluating a patient with suspected heart disease, 2) to measure the effectiveness of therapy, 3) to assess risk or prognosis, and 4) in clinical trials evaluating new therapies.

The method comprises five main parts: a) reviewing the digital images, b) selecting the image frames to analyze, c) identifying and tracing the border of the left ventricle, d) measuring left ventricular volume from the traced border and calculating ejection fraction, and e) measuring regional wall motion. The UN LV Analysis Software allows the user to review a series of images in digital format, and manually select frames for analysis. Identifying and tracing the ventricular border is performed manually using the Of the techniques developed to measure left LV Analysis Software. UW ventricular volume from a traced border, the area length method is generally the most accurate, and is the method used by the UW LV Analysis accepted as Software. Of the techniques for measuring left ventricular wall motion, the centerline method has been proven useful for clinical trials, and is the method used by the UW LV Analysis Software. Calibration to correct for image magnification is required for volume measurement, but not for calculation of the ejection fraction or for analysis of regional wall motion.

Here's an analysis of the provided text, focusing on the acceptance criteria and the study details, presented in the requested format:

Acceptance Criteria and Device Performance Study

The provided document describes the UW LV Analysis Software, which assists in measuring left ventricular volume, ejection fraction, and regional wall motion. This software is largely a manual tool with automated calculations based on user input.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly define a "test set" in the context of a formal validation study with a distinct sample size for evaluating acceptance criteria. Instead, it describes:

• During software upgrades: "the data of thousands of patients were analyzed by both the new and previous versions and compared". This suggests a large, retrospective set of patient data, likely from the University of Washington's clinical archives.
• During licensing/installation: The standard of accuracy was applied "when licensed to other companies and installed on their systems". This implies a comparison against the University of Washington's programs, possibly using a smaller, but representative, dataset for each installation.

Data Provenance: The data primarily originated from the University of Washington. The nature of the data (retrospective/prospective) isn't explicitly stated for all uses, but the "data of thousands of patients" used for upgrades strongly suggests retrospective analysis of existing clinical data.

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

The concept of "experts establishing ground truth for the test set" as a distinct activity is not described. The software relies on manual tracing by users/clinicians. The "standard of accuracy" during licensing/upgrades was comparison against the "programs at the University of Washington." This implies that the University of Washington's version, and the measurements derived from it by experienced users, served as the de facto reference.

While not explicitly stated, it's reasonable to infer that the "programs at the University of Washington" were used and validated by experienced cardiologists or technicians over 13 years, given their use in NIH-sponsored trials. However, the specific number or qualifications of these "experts" for creating a ground truth dataset are not provided.

4. Adjudication Method for the Test Set

No formal adjudication method is described for generating a ground truth test set. The validation seems to be based on:

• Comparison between software versions: The new version's results were compared to the preceding version's results.
• Comparison against the "University of Washington" standard: Installed programs were compared against the UW programs.

This implies a direct comparison rather than a consensus-based adjudication 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

No MRMC comparative effectiveness study is described. The software is presented as an assistance tool for human readers (users manually trace borders). There is no mention of a study comparing human performance with and without the software, nor any effect size regarding human improvement. The software's primary role is to provide a standardized tool and calculations for manual input, rather than an AI-driven interpretation tool.

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

No standalone algorithm performance study was done. The UW LV Analysis Software description clearly states: "Identifying and tracing the ventricular border is performed manually using the UW LV Analysis Software." This explicitly indicates a human-in-the-loop process for the critical step of border identification. The software then performs calculations based on these manual tracings.

7. The Type of Ground Truth Used

The ground truth used for validation (during upgrades and licensing) was essentially based on measurements derived from previous, trusted versions of the same software, operated by experienced users at the University of Washington. In essence, it's a form of expert consensus/established clinical practice as embodied by the long-standing use of the UW programs. It is not pathology, or direct outcomes data.

8. The Sample Size for the Training Set

The document does not describe a distinct "training set" in the context of machine learning. The software is described as a rule-based system ("area length method," "centerline method") that performs calculations based on manual input. It is not a supervised learning model that requires a labeled training set in the typical AI sense.

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

Since there is no training set mentioned in the context of machine learning, the question of how its ground truth was established is not applicable. The software's methods (area length, centerline) are established clinical methodologies, not learned from data.

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