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AI Platform 2.2 is intended for noninvasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function of patients with suspected disease. In addition, it can provide Quality Score feedback to assist healthcare professionals, trained and qualified to conduct echocardiography, abdominal, and lung ultrasound scans in the current standard of care while acquiring ultrasound images. The device is intended to be used on images of adult patients.

Exo AI Platform 2.2 (AIP 2.2) is a software as a medical device (SaMD) that helps qualified users with image-based assessment of ultrasound examinations in adult patients. It is designed to simplify workflow by helping trained healthcare providers evaluate, quantify, and generate reports for ultrasound images. AIP 2.2 takes as an input in the Digital Imaging and Communications in Medicine (DICOM) format from ultrasound scanners of a specific range and allows users to detect, measure, and calculate relevant medical parameters of structures and function of patients with suspected disease. In addition, it provides frame and clip quality score in real-time for the Left Ventricle from the four-chamber apical and parasternal long axis views of the heart, Abdominal Upper Quadrant and Pelvic views, and lung scans.

The AI modules are provided as software components to be integrated by another computer programmer into their legally marketed ultrasound imaging device. Essentially, the Algorithm and API, which are modules, are medical device accessories.

Key features of the software are:

• Lung AI: An AI-assisted tool for suggesting the presence of lung structures and artifacts on ultrasound images, namely A-lines and B-lines.
• Cardiac AI: An AI-assisted tool for the quantification of Left Ventricular Ejection Fraction (LVEF), Myocardium wall thickness (Interventricular Septum (IVSd), Posterior wall (PWd)), and IVC diameter on cardiac ultrasound images.
• Quality AI: An AI tool designed to assess ultrasound per frame and per clip quality across Cardiac (A4C and PLAX), Lung, and Abdominal (Upper Quadrants and Pelvic) views.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter excerpt for AI Platform 2.2 (AIP002):

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Quality AI functionality, specifically for abdominal views (upper quadrants and pelvic), focused on the agreement between the AI's quality rating and expert sonographers' ratings, as well as the ability of the AI to identify diagnostic quality scans.

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

The provided text describes two distinct validation activities:

• Retrospective Data Analysis:

  • Sample Size: 200 clips (comprising 29,371 frames) from 184 patients.
  • Data Provenance: The data was "previously acquired from various ultrasound devices and various abdominal pathologies." It encompassed "diverse demographic variables, including gender, age, and ethnicity from multiple clinical sites in metropolitan cities with diverse racial patient populations." The specific countries of origin are not explicitly stated, but "metropolitan cities with diverse racial patient populations" suggests a varied, likely multi-national, origin or at least from diverse populations within a country (e.g., USA). The data was retrospective as it was "previously acquired."

• Prospective Clinical Use Case (Real-time Scanning):

  • Sample Size: 186 abdomen scans.
  • Data Provenance: Data was "acquired using the image and clip Quality AI in real time while scanning the pelvic and upper quadrant views of the abdomen." The details suggest this was prospective data collection specifically for validating the real-time feedback. Similar to the retrospective data, it encompassed diverse demographics and was acquired from different sites.

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

• Retrospective Data Analysis: "experienced sonographers" were used for quality rating on each frame and the entire clip. The exact number of sonographers is not specified. Their qualifications are described as "experienced sonographers."
• Prospective Clinical Use Case: "expert readers" were used to rate the ACEP quality of the clips and deem scans as diagnostic. The exact number of expert readers is not specified, but they rated clips based on ACEP quality (American College of Emergency Physicians), suggesting expertise in emergency ultrasound or similar fields.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method (e.g., 2+1, 3+1) used for establishing ground truth. It mentions "quality rating by experienced sonographers" and "experts" deeming scans diagnostic, implying expert consensus or individual expert judgments, but the process for resolving disagreements (if any) is not detailed.

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 was explicitly mentioned in the provided text, comparing human readers with and without AI assistance for improved performance. The studies described focus on the standalone performance of the AI in assessing image quality and agreement with human experts, and how the AI's real-time feedback correlates with expert-rated diagnostic quality, but not on improvement in human reader performance.

6. If a Standalone (i.e. Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, a standalone performance assessment was conducted.

• The retrospective data analysis directly evaluates the "overall agreement between the Quality AI and quality rated by the experienced sonographers" (ICC of 0.94 for frames and 0.95 for clips). This is a measurement of the AI algorithm's performance in isolation against established ground truth.
• The AI Platform 2.2 provides "Quality Score feedback to assist healthcare professionals... while acquiring ultrasound images." The validation results showing the correlation between the Quality AI's assessment and expert-rated diagnostic quality (96.6% and 96.1%) reflect the standalone algorithm's ability to assess quality, which then serves as feedback.

7. The Type of Ground Truth Used

The ground truth for the validation studies was primarily established through expert consensus/judgment.

• For the retrospective analysis, it was "quality rating by experienced sonographers."
• For the prospective clinical use case, it involved "expert readers" who rated the ACEP quality and deemed scans diagnostic.

8. The Sample Size for the Training Set

The sample size for the training set is not provided in the given FDA 510(k) summary. The document explicitly states that the "test data was entirely separated from the training/tuning datasets and was not used for any part of the training/tuning," but it does not disclose the size of the training or tuning datasets themselves.

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

The document states that the AI algorithms are "trained with clinical data." It is implied that this clinical data, used for training, would have had its ground truth established similarly to the test sets, likely through expert annotation or review by qualified medical professionals. However, the exact methodology for establishing ground truth for the training set is not detailed in this summary.

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