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EPIQ: The intended use of EPIQ Ultrasound Diagnostic System is diagnostic ultrasound imaging and fluid flow analysis of the human body, with the following indications for use: Abdominal, Cardiac Adult, Cardiac other (Fetal), Cardiac Pediatric, Cerebral Vascular, Cephalic (Adult), Cephalic (Neonatal), Fetal/Obstetric, Gynecological, Intraoperative (Vascular), Intraoperative (Cardiac), intra-luminal, intra-cardiac echo, Musculoskeletal (Conventional), Musculoskeletal (Superficial), Ophthalmic, Other: Urology, Pediatric, Peripheral Vessel, Small Organ (Breast, Thyroid, Testicle), Transesophageal (Cardiac), Transrectal, Transvaginal, Lung.
Affiniti: The intended use of Affiniti Series Diagnostic Ultrasound Systems is diagnostic ultrasound imaging and fluid flow analysis of the human body, with the following indications for use: Abdominal, Cardiac Adult, Cardiac Other (Fetal), Cardiac Pediatric, Cerebral Vascular, Cephalic (Adult), Cephalic (Neonatal), Fetal/Obstetric, Gynecological, Intraoperative (Vascular), Intraoperative (Cardiac), Musculoskeletal (Conventional), Musculoskeletal (Superficial), Other: Urology, Pediatric, Peripheral Vessel, Small Organ (Breast, Thyroid, Testicle), Transesophageal (Cardiac), Transrectal, Transvaginal, Lung.

The R-Trigger algorithm software feature on Philips EPIQ and Affiniti Ultrasound System is intended to support detection of R-wave peak (R-trigger) as an input to certain TTE clinical applications, initially including AutoStrain LV, AutoEF, 2D Auto LV (collectively referred to as "AutoStrain"), and AutoMeasure applications. The R-trigger algorithm is planned to be implemented as workflow enhancement for transthoracic clinical applications on EPIQ and Affiniti Ultrasound Systems in the VM13 software release. The Auto-Measure and AutoStrain features support users during B-mode (2D), CW-, PW- and TDI-Doppler measurements by automating some of the measurements needed to complete a routine transthoracic echo (TTE) exam for adult patients. The R-trigger feature (non-ECG-based) has been developed to enable clinical users to use AutoMeasure and AutoStrain application without the R-trigger (ECG based) input, which is currently required. There are no hardware changes to the EPIQ and Affiniti systems due to change to the introduction of the R-Trigger software application. The software application is supported by all EPIQ and Affiniti models running software version 13.0 or higher.

The provided FDA 510(k) clearance letter describes the R-Trigger software application on Philips EPIQ and Affiniti Ultrasound Systems, which aims to provide an alternative method for detecting R-wave peaks (R-triggers) for cardiac clinical applications like AutoStrain and AutoMeasure, especially when the ECG signal is unavailable or unusable.

Here's an analysis of the acceptance criteria and the study proving the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance:

The primary acceptance criteria for the R-Trigger algorithm are related to the agreement of its R-wave time stamp detection with the ground truth (ECG-based R-trigger) and the subsequent impact on the clinical outputs of AutoMeasure and AutoStrain. These are evaluated using Bland-Altman analysis (for agreement, specifically the Upper and Lower Limits of Agreement, LoA) and Pearson's correlation (for correlation, specifically the Lower Confidence Bound, LCB).

Conclusion on Acceptance Criteria: The study results demonstrate that the R-Trigger AI-based algorithm meets all the pre-defined acceptance criteria across all three primary endpoints (R-trigger time stamp detection, AutoStrain outputs, and AutoMeasure outputs), as indicated by the reported confidence intervals for the limits of agreement and correlation.

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

• Test Set Sample Size: A total of 7309 cardiac clips from 3964 subjects were used for the analysis of the R-trigger AI-based algorithm.
• Data Provenance: The data consisted of retrospective cardiac TTE clips acquired with Philips Ultrasound systems. The subject demographics indicate data from various regions, including North America (USA1, USA2, Canada, Mexico) and "Rest of world". This suggests a diverse, multi-national data set.

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

The document states that the R-Trigger algorithm's performance was compared against "ECG-based R-trigger" as the ground truth. It does not explicitly mention the number or qualifications of human experts used to establish this ground truth or to review the R-trigger AI's output. The ECG signal from the on-cart physio board is presented as the existing and preferred method for R-trigger detection, implying it is the established clinical standard.

4. Adjudication Method for the Test Set:

The document does not describe a formal adjudication method (e.g., 2+1, 3+1) for the test set. Instead, the study compares the R-trigger AI's output directly against the "ECG-based R-trigger" as the established ground truth. It also mentions that "users are generally expected to review and concur with the initialization and generated results" and can "edit the application(s) generated measurements and outputs based on their clinical expertise," implying a human-in-the-loop for clinical use, but not necessarily for the ground truth establishment in this particular retrospective study.

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, a MRMC comparative effectiveness study was not explicitly stated or performed in this context. The study focuses on the standalone performance of the R-Trigger AI algorithm compared to the existing ECG-based R-trigger, and its impact on automated measurements, rather than a direct comparison of human readers' performance with and without AI assistance on R-trigger detection itself. The AI is positioned as a "back-up" or "workflow enhancement" rather than a tool to directly improve human reader accuracy in R-trigger detection, as the ECG is the preferred method when available.

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

Yes, a standalone performance study was conducted. The primary endpoint #1 specifically evaluated "the performance of the R-Trigger (non-ECG) algorithm in isolation" by comparing its detected R-wave time stamp against the ECG-based R-trigger (ground truth). Endpoints #2 and #3 then evaluated the impact of this standalone algorithm's output when used as input for AutoMeasure and AutoStrain. The study reports the performance of the AI algorithm directly, without human intervention in the R-trigger detection process for the test clips.

7. The Type of Ground Truth Used:

The primary ground truth used for evaluating the R-Trigger AI algorithm was the ECG-based R-trigger signal obtained from the ultrasound system's on-cart physio board. This is considered the established clinical standard for R-wave detection in cardiac cycles.

8. The Sample Size for the Training Set:

The document does not provide the sample size used for the training set for the R-Trigger AI algorithm. It only details the test set's sample size and demographics.

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

The document does not specify how the ground truth for the training set was established. Given that the ground truth for the test set was the "ECG-based R-trigger," it is highly probable that similar ECG signals were used to establish the ground truth for the training data, allowing the AI to learn to identify R-wave peaks from ultrasound clips in correlation with known ECG R-waves.

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