(170 days)
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 Gastric Alimetry 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 the myoelectrical data and movement artifacts through an array with recording electrodes on an adhesive patch which is used for recording the myoelectrical data from the skin surface. An App used to set up the device and capture patient-reported symptom data.
A report is provided to the clinicians at the end of the test which displays myoelectrical data.
The provided text describes the Gastric Alimetry System, an electrogastrography (EGG) device. However, it does not explicitly state specific acceptance criteria (e.g., a specific sensitivity or specificity threshold) for the device's performance. Instead, it concludes that the device's performance is "equivalent" or "comparable" to a predicate device and manual marking of artifacts.
Therefore, the table below will reflect the comparison to the predicate device where performance is discussed, rather than predefined acceptance criteria.
1. Table of Acceptance Criteria and Reported Device Performance
| Feature/Metric | Acceptance Criteria (Derived from comparison to predicate/manual) | Reported Device Performance |
|---|---|---|
| Gastric Myoelectrical Frequency Detection (vs. Predicate) | Detection and measurement of gastric myoelectrical frequency across pre-prandial and postprandial periods in an equivalent manner to the predicate device. | The Gastric Alimetry System detects and measures gastric myoelectrical frequency across pre-prandial and postprandial periods in an equivalent manner to the predicate device within a cohort of patients with various gastric disorders. |
| Automated Artifact Detection (vs. Manual Marking) | Automated artifact detection algorithm to be comparable to manual marking by clinicians. | The automated artifact detection algorithm is comparable to manual marking. |
2. Sample size used for the test set and the data provenance:
- Sample Size (for Gastric Myoelectrical Frequency Detection Study): 25 patients.
- Data Provenance: Prospective clinical study. The country of origin of the data is not specified.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- For Gastric Myoelectrical Frequency Detection: The study was a "simultaneous head-to-head comparison to the predicate," meaning the predicate device itself served as a pseudo-ground truth for comparison, rather than an independent expert panel.
- For Automated Artifact Detection: Ground truth was established by "manual marking of artifacts by clinicians." The number of clinicians and their specific qualifications are not specified in the provided text.
4. Adjudication method for the test set:
- The text does not specify an adjudication method like 2+1 or 3+1. For the gastric myoelectrical frequency detection, it was a head-to-head comparison to the predicate. For artifact detection, it was compared against "manual marking by clinicians," implying those clinicians' markings were the reference, without detailing an adjudication process.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:
- No, a multi-reader multi-case (MRMC) comparative effectiveness study involving human readers and AI assistance is not described in the provided text. The studies mentioned focus on the standalone performance of the device or its algorithms against a predicate or manual marking.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, the performance studies described are essentially standalone evaluations:
- The head-to-head comparison with the predicate device evaluates the device's ability to measure gastric myoelectrical frequency.
- The evaluation of the artifact detection algorithm compares its automated output against manual markings.
7. The type of ground truth used:
- For Gastric Myoelectrical Frequency Detection: The performance of the predicate device (Polygraf ID with POLYGRAM NET ElectroGastroGraphy Application Software) was used as the reference point for comparison.
- For Automated Artifact Detection: "Manual marking of artifacts by clinicians" was used as the ground truth.
8. The sample size for the training set:
- The document does not provide details about a training set or its sample size. The clinical studies described are presented as evaluations of the device's performance, implying they might be test or validation sets.
9. How the ground truth for the training set was established:
- As no information on a specific training set or its ground truth establishment is provided, this cannot be answered from the given text.
§ 876.1735 Electrogastrography system.
(a)
Identification. An electrogastrography system (EGG) is a device used to measure gastric myoelectrical activity as an aid in the diagnosis of gastric motility disorders. The device system includes the external recorder, amplifier, skin electrodes, strip chart, cables, analytical software, and other accessories.(b)
Classification. Class II (Special Controls). The special controls are as follows:(1) The sale, distribution and use of this device are restricted to prescription use in accordance with § 801.109 of this chapter.
(2) The labeling must include specific instructions:
(i) To describe proper patient set-up prior to the start of the test, including the proper placement of electrodes;
(ii) To describe how background data should be gathered and used to eliminate artifact in the data signal;
(iii) To describe the test protocol (including the measurement of baseline data) that may be followed to obtain the EGG signal; and
(iv) To explain how data results may be interpreted.
(3) The device design should ensure that the EGG signal is distinguishable from background noise that may interfere with the true gastric myoelectric signal.
(4) Data should be collected to demonstrate that the device has adequate precision and the EGG signal is reproducible and is interpretable.