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510(k) Data Aggregation
(189 days)
The modified Sonamet Body Composition Analyzer, also known as the PEA POD® Infant Body Composition System (the PEA POD), is indicated for measuring body mass and estimating the body composition (i.e., the body fat and lean body mass) of infants between 1 and 8 kilograms. It is not intended for use with infants requiring life support.
The modified Sonamet Body Composition System ("The PEA POD") is designed to measure the mass and estimate the body composition of infants with body weights ranging between 1 and 8 kilograms, who do not require life support. The PEA POD estimates body composition using a densiometric approach (i.e. by determining the density of the entire body). A weighing apparatus is used to-measure the subject's mass. Air displacement plethysmography is used to measure the subject's volume. Using this data, the subject's density is calculated. The - subject's body composition is then estimated using several algorithms derived from scientific research. The device components are housed in a movable cart, which contains the reference chamber, calibration volume, air circulation system, Air Temperature Control System, electronic components, printer and CPU.
The provided text describes a 510(k) submission for the Modified Sonamet Body Composition Analyzer (PEA POD for infants). However, it does not contain detailed information about specific acceptance criteria or a dedicated study proving device performance against those criteria. The document states:
"The results of verification testing demonstrate that the PEA POD Body Composition Analyzer is substantially equivalent to the predicate devices. Test results indicate that the device satisfies functional performance requirements safely and accurately when used as indicated."
This is a general statement of compliance rather than a detailed report of a study with specific acceptance criteria and outcome metrics.
Therefore, many of the requested details cannot be extracted from the given text.
Here's an attempt to answer the questions based only on the provided information, noting where information is absent:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria (Stated or Implied) | Reported Device Performance |
|---|---|
| Substantial equivalence to predicate devices | "The results of verification testing demonstrate that the PEA POD Body Composition Analyzer is substantially equivalent to the predicate devices." |
| Satisfies functional performance requirements safely and accurately when used as indicated | "Test results indicate that the device satisfies functional performance requirements safely and accurately when used as indicated." |
| Indicated for measuring body mass and estimating body composition (body fat and lean body mass) of infants between 1 and 8 kg. | The device is described as designed for this purpose, but specific performance metrics (e.g., accuracy, precision) against a defined gold standard are not provided. |
| Not intended for use with infants requiring life support. | This is an intended use exclusion, not a performance criterion. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample size for test set: Not specified in the provided text.
- Data provenance: Not specified in the provided text (e.g., country of origin, retrospective or prospective).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not applicable/Not specified. The document does not describe a study involving expert-established ground truth for the device's performance.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable/Not specified. The document does not describe a study involving expert adjudication.
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
Not applicable. The PEA POD is a medical device for body composition analysis, not an AI-assisted diagnostic imaging tool that would typically involve human readers.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
The device is a standalone system for measuring body composition. The text implies a standalone performance (system-only) by stating it "satisfies functional performance requirements safely and accurately." However, no specific details of such a study are provided, nor is an algorithm explicitly distinguished from the overall device operation for "standalone" testing in this context.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Not explicitly specified. The document refers to "verification testing" and "functional performance requirements" but does not detail the nature of the ground truth used (e.g., comparison against a gold standard method, phantom studies, etc.). Given it's a body composition analyzer, potential ground truths could involve known-composition phantoms or comparison against other accepted body composition measurement methods (though this is not detailed).
8. The sample size for the training set
Not applicable. The document does not describe a machine learning or AI algorithm that would typically have a separate training set. The "several algorithms derived from scientific research" mentioned in the device description are likely established mathematical models, not deep learning models requiring a specific training dataset in the same vein.
9. How the ground truth for the training set was established
Not applicable, as there's no mention of a training set for an AI algorithm.
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