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EchoPAC Software Only / EchoPAC Plug-in is intended for diagnostic review and analysis of ultrasound images, patient record management and reporting, for use by, or on the order of a licensed physician. EchoPAC Software Only / EchoPAC Plug-in allows post-processing of raw data images from GE ultrasound scanners and DICOM ultrasound images.

Ultrasound images are acquired via B (2D), M, Color M modes, Color, Power, Pulsed and CW Doppler modes, Coded Pulse, Harmonic, 3D, and Real time (RT) 3D Mode (4D).

Clinical applications include: Fetal/Obstetrics; Abdominal (including renal and GYN); Urology (including prostate); Pediatric; Small organs (breast, testes, thyroid); Neonatal and Adult Cephalic; Cardiac (adult and pediatric); Peripheral Vascular; Transesophageal (TEE); Musculo-skeletal Conventional; Musculo-skeletal Superficial; Transrectal (TR); Transvaginal (TV); Intraoperative (vascular); Intra-Cardiac; Thoracic/Pleural and Intra-Luminal.

EchoPAC Software Only / EchoPAC Plug-in provides image processing, annotation, analysis, measurement, report generation, communication, storage and retrieval functionality to ultrasound images that are acquired via the GE Healthcare Vivid family of ultrasound systems, as well as DICOM images from other ultrasound systems. EchoPAC Software Only will be offered as SW only to be installed directly on customer PC hardware and EchoPAC Plug-in is intended to be hosted by a generalized PACS host workstation. EchoPAC Software Only / EchoPAC Plug-in is DICOM compliant, transferring images and data via LAN between systems, hard copy devices, file servers and other workstations.

The provided 510(k) clearance letter and summary discuss the EchoPAC Software Only / EchoPAC Plug-in, including a new "AI Cardiac Auto Doppler" feature. The acceptance criteria and the study proving the device meets these criteria are primarily detailed for this AI-driven feature.

Here's an organized breakdown of the information:

1. Acceptance Criteria and Reported Device Performance (AI Cardiac Auto Doppler)

2. Sample Size and Data Provenance for the Test Set

• Sample Size:

  • Tissue Doppler: 4106 recordings from 805 individuals.
  • Doppler Trace: 3390 recordings from 1369 individuals.
  • BMI Sub-analysis: 41 patients, 433 Doppler measurements (subset of Vivid Pioneer dataset).

• Data Provenance: Retrospective, collected from standard clinical practices.

  • Countries of Origin: USA (several locations), Australia, France, Spain, Norway, Italy, Germany, Thailand, Philippines.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts:

  • Annotators: Two cardiologists.
  • Review Panel: Five clinical experts.

• Qualifications of Experts:

  • Annotators: Cardiologists, implying medical expertise in cardiac imaging and diagnosis. They followed US ASE (American Society of Echocardiography) based annotation guidelines.
  • Review Panel: Clinical experts, implying medical professionals with experience in the relevant clinical domain.

4. Adjudication Method for the Test Set

The ground truth establishment process involved:

• Two cardiologists performed initial annotations.
• A review panel of five clinical experts provided feedback on these annotations.
• Annotations were corrected (as needed) until a consensus agreement was achieved between the annotators and reviewers. This suggests an iterative consensus-based adjudication method.

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

• No MRMC comparative effectiveness study was explicitly mentioned. The provided document focuses on the standalone performance of the AI algorithm against expert-derived ground truth, not human-in-the-loop performance.
• Therefore, an effect size of how much human readers improve with AI vs. without AI assistance is not provided.

6. Standalone (Algorithm Only) Performance

• Yes, a standalone performance evaluation was done. The "AI Auto Doppler Summary of Testing" section describes the performance of the AI Cardiac Auto Doppler algorithm itself, without human intervention for the critical performance metrics (e.g., "All clinical parameters, as performed by AI Cardiac Auto Doppler without user edits passed this check").

7. Type of Ground Truth Used

• The ground truth was established by expert consensus (two cardiologists performing annotations, reviewed and corrected by a panel of five clinical experts until consensus).
• It was based on manual measurements and assessments of Doppler signal quality and ECG signal quality on curated images, following US ASE based annotation guidelines.

8. Sample Size for the Training Set

• Tissue Doppler: 1482 recordings from 4 unique clinical sites.
• Doppler Trace: 2070 recordings from 4 unique clinical sites.

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

• The ground truth for both development (training) and verification (testing) datasets was established using the same "truthing" process:
  • Annotators (two cardiologists) performed manual measurements after assessing Doppler signal quality and ECG signal quality of curated images.
  • These annotations followed US ASE based annotation guidelines.
  • A review panel of five clinical experts provided feedback, and corrections were made until a consensus agreement was achieved between the annotators and reviewers.
• It is explicitly stated that the development dataset was selected from clinical sites not used for the testing dataset, ensuring independence between training and test data.

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