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The Gastric Alimetry System is intended to record, store, view and process gastric myoelectrical activity as an aid in the diagnosis of various gastric disorders.

The modified Gastric Alimetry System is indicated for patients 12 years and older.

The Gastric Alimetry System is an electrogastrography (EGG) device, used for non-invasively measuring the myoelectrical activity of the stomach at the surface of the abdomen. The Gastric Alimetry System is intended to record, store, view and process gastric myoelectrical activity as an aid in the diagnosis of various gastric disorders.

The device is used to acquire and digitize gastric myoelectrical data and movement artifacts using the Alimetry Reader connected to a single use Gastric Alimetry Array which includes electrodes on an adhesive patch used for recording the myoelectrical data from the skin surface. The Gastric Alimetry App runs on an iPad mini and is used to set up the device and capture patient-reported symptom data. The Gastric Alimetry Report is provided to the clinicians at the end of the test and includes myoelectrical signal data for manual analysis, together with computed data summaries and plots. A Supplementary Report is also routinely available to clinicians that includes signal data from all 64 channels on the array. The Alimetry Cloud acts as a secure website portal for physicians to access Gastric Alimetry Reports. The Gastric Alimetry Dock (Accessory) is used to guide alignment of the Array and Reader during the setup procedure and charge the Reader.

The Gastric Alimetry System is non-invasive and used in healthcare facilities.

The device is mostly unchanged compared to the previously cleared Gastric Alimetry System, apart for the following modifications:

Modifications to the Gastric Alimetry Algorithm:

• Replacement of the existing signal noise filter with a convolutional neural network (CNN) and additional updates to the processing pipeline of the algorithm to improve performance.

Modifications to the Gastric Alimetry Report:

• Addition of grouping symptoms into subgroups, including an overall symptom score per subgroup.
• Updating presentation of spectral and symptom data.
• Addition of symptom tags to describe common symptom patterns.
• Addition of visual indicators for spectral metrics which are outside of the reference intervals and a summary of the spectral metric values.
• Addition of subgroup scores in the 'Gut-Brain Wellbeing' page.
• Addition of caution statements that are shown on the report when test quality metrics are outside of target ranges and/or non-standard test protocols are used.
• Addition of a plot of average impedance over time and modification of movement index plot.
• Addition of Gut-Brain Wellbeing questions for adolescents between the ages of 12-17 years.
• Removal of guidelines from the report and the release of a separate guidelines document.
• Rearranging information in the reports.

Additional minor updates were made to the Reader, Cloud, Algorithm, App and labeling.

The provided FDA 510(k) clearance letter and summary for the Gastric Alimetry System do not contain detailed acceptance criteria or a study proving the device meets specific performance criteria. The document primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a comprehensive performance study with set acceptance criteria.

The submission describes modifications to an existing Gastric Alimetry System, specifically:

• Replacement of the existing signal noise filter with a convolutional neural network (CNN) and additional updates to the processing pipeline of the algorithm to improve performance.
• Modifications to the Gastric Alimetry Report, including grouping symptoms, updating presentation of spectral and symptom data, adding visual indicators and caution statements, and adding Gut-Brain Wellbeing questions for adolescents.

The "Performance Data" section states: "The modifications to the device since the prior clearances, namely updates to the processing pipeline of the algorithm, and modifications to the Report... did not significantly impact the safety or performance of the device as reflected in the performed bench testing, in addition to bench testing submitted in prior 510(k) notices."

This statement implies that the "bench testing" was sufficient to confirm that the changes did not negatively affect performance, but it does not specify what those performance metrics or acceptance criteria were. It also does not describe a clinical study for proving new acceptance criteria.

Therefore, for the information requested:

1. Table of Acceptance Criteria and Reported Device Performance

• Acceptance Criteria: Not explicitly stated in the provided document. The document implies that the acceptance criterion was that the changes to the algorithm and report "did not significantly impact the safety or performance of the device" compared to the predicate, as demonstrated by "bench testing." Specific numerical targets for performance metrics (e.g., sensitivity, specificity, accuracy, signal-to-noise ratio) are not provided.
• Reported Device Performance: Not explicitly enumerated with specific values. The document states that the changes were assessed and "shown to be substantially equivalent to the predicate" and "raise no new questions of safety or efficacy."

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

• The document refers to "bench testing." It does not specify a sample size for a test set of patient data, nor does it mention the data provenance (e.g., country of origin, retrospective/prospective). This is typically because 510(k) submissions focusing on software/algorithm changes for a substantially equivalent device often rely on internal validation datasets or existing clinical data rather than a new full-scale prospective clinical study.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Not applicable / Not provided. The document does not describe a study involving expert readers or ground truth establishment by experts for a test set where the device's diagnostic accuracy is being evaluated against human interpretation. The changes are primarily in signal processing and reporting format.

4. Adjudication Method for the Test Set

• Not applicable / Not provided. No specific adjudication method is mentioned as there's no description of a study involving human readers or interpretation of diagnostic findings.

5. MRMC Comparative Effectiveness Study

• No. The document does not mention any Multi-Reader Multi-Case (MRMC) comparative effectiveness study to assess how human readers improve with AI assistance.

6. Standalone Performance Study

• The document implies that "bench testing" was performed on the algorithm's updated processing pipeline. However, specific details of a standalone (algorithm only) performance study, including metrics and quantitative results, are not provided. The focus is on demonstrating that the changes to the algorithm do not negatively impact performance, rather than establishing entirely new standalone performance metrics.

7. Type of Ground Truth Used

• Not specified. Given that the document refers to "bench testing" for algorithm changes, the ground truth would likely be synthetically generated data, known noise profiles, or pre-existing "golden standard" EGG recordings where the true gastric myoelectrical activity is characterized. However, this is not detailed in the provided text.

8. Sample Size for the Training Set

• Not specified. The document mentions the replacement of a noise filter with a convolutional neural network (CNN), which implies machine learning. However, the sample size used to train this CNN is not provided.

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

• Not specified. For a CNN, the ground truth for training would involve pairs of noisy and "clean" EGG signals, or signals with known artifacts. The method of establishing this "ground truth" (e.g., manually cleaned signals, simulations, expert annotation) is not detailed in the provided document.

In summary, the provided FDA clearance letter focuses on demonstrating substantial equivalence and the absence of significant impact from device modifications, rather than detailing a new performance study with explicit acceptance criteria and corresponding results.

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