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The BOD POD® is indicated for measuring the body mass and estimating the body composition (i.e. percent and absolute amounts of fat and lean body mass) of generally healthy individuals. The BOD POD is also indicated for estimating Resting Metabolic Rate (RMR) and Total Energy Expenditure (TEE) in generally healthy individuals aged 18 years or older.

The Sonamet Body Composition System is designed to measure the mass and estimate the body composition of individuals. Once an individual's body composition has been determined, the BOD POD is able to accurately estimate Resting Metabolic Rate (RMR) and total Energy Expenditure (TEE).

The BOD 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.

An individual's RMR and TEE can also be estimated accurately using values for Fat Mass and Fat Free Mass. Scientifically derived algorithms utilize the Fat Mass and Fat Free mass values determined by the BOD POD to calculate RMR and TEE.

Here's an analysis of the provided 510(k) summary regarding the acceptance criteria and the study conducted for the Sonamet Body Composition Analyzer (BOD POD):

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state specific numerical acceptance criteria for the device's performance. Instead, it makes a general statement about accuracy.

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

The document does not provide information on the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective nature). It only mentions "verification testing."

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

The document does not provide information on the number of experts used or their qualifications for establishing ground truth in any test set.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method used for a test set.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. The device is a "Body Composition Analyzer," which typically involves direct measurement rather than interpretation by multiple human readers comparing performance with and without AI assistance.

6. Standalone (Algorithm Only) Performance Study

The entire document describes the performance of the standalone device (algorithm only). The BOD POD itself is the algorithm that measures mass and volume and then calculates body composition, RMR, and TEE. The "verification testing" mentioned would refer to the performance of this algorithm.

7. Type of Ground Truth Used

The ground truth for the device's accuracy appears to be established by:

• Published scientific research: The document states, "RMR and TEE results generated by the BOD POD Body Composition Analyzer are accurate when compared with expected results based on published scientific research." This implies that established scientific models or data serve as the benchmark.
• Substantial equivalence to predicate devices: The comparison to legally marketed predicate devices (Sonamet Body Composition Analyzer K924972 and TANITA Segmental Body Composition Analyzer Model BC-418) implies that their established performance serves as a form of ground truth or benchmark for comparison.

8. Sample Size for the Training Set

The document does not provide information on the sample size used for a "training set." This type of device (Body Composition Analyzer) likely relies on established physical principles and scientific equations rather than a machine learning model that would typically have a distinct "training set." The algorithms are "derived from scientific research."

9. How Ground Truth for the Training Set Was Established

As noted above, the concept of a "training set" in a machine learning sense is not applicable here. The algorithms for calculating body composition, RMR, and TEE are described as being "derived from scientific research." This means the ground truth for establishing these algorithms comes from:

• Scientific literature and models: Previous research on human physiology, energy expenditure, and densitometry would have informed the algorithms.
• Empirical data from prior studies: The original development of these equations would have involved studies where body composition, RMR, and TEE were measured using gold standard methods (e.g., DEXA, indirect calorimetry for RMR) to derive and validate the predictive equations used in the device.

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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

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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