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The EasyBody 202 and EasyBody 203 are intended to estimate PBF(Percentage of Body Fat), MBF(Mass of Body Fat), LBM(Lean Body Mass), BMR(Basic Metabolic Rate), recommended daily calorie intake, daily exercise time, and sort and term of exercise using the BIA(Bio-electrical Impedance Analysis) method.

The EasyBody 205 is intended to estimate PBF(Percentage of Body Fat), MBF(Mass of Body Fat), LBM(Lean Body Mass), BMR(Basic Metabolic Rate), Segmental LBM, recommended daily calorie intake, daily exercise time, and sort and term of exercise using the BIA(Bio-electrical Impedance Analysis) method.

The EasyBody 202, EasyBody 203 and EasyBody 205 measure the impedance and weight of the user, and are intended for use only in healthy subjects between the age of 5-89.

The EasyBody 202, EasyBody 203 and EasyBody 205 are non-invasive body composition analyzers intended for use only in healthy subjects between the age of 5-89. The devices employ BIA(Bio-electrical Impedance Analysis) method, 4 electrodes for EasyBody 202 and EasyBody 203, 8 electrodes for EasyBody 205, and then measure body composition using an experimentally derived algorithm. The devices are powered by four AA batteries (DC 6V) or AC adapter.

Here's a summary of the acceptance criteria and the study details for the Jawon Medical Body Composition Analyzer Models EasyBody 202, EasyBody 203, and EasyBody 205, based on the provided text:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the demonstration of "no significant difference" and substantial equivalence to the predicate device, ZEUS 9.9 (K053556). The specific performance metrics for acceptance are not explicitly listed as numerical targets (e.g., "accuracy > 90%"). Instead, the performance is judged against the established performance of the predicate.

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "clinical comparison tests," but does not provide details on the number of subjects or cases used in these tests.
• Data Provenance: Not explicitly stated, but the submitter and contact person are from the Republic of Korea, suggesting the clinical tests were likely conducted there. It is described as "clinical comparison tests," implying prospective data collection for the purpose of the study.

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

This information is not provided in the document. The study relies on comparison to a predicate device, rather than a separate expert-established ground truth.

4. Adjudication Method for the Test Set

This information is not provided in the document.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not conducted in the manner typically associated with AI products where human readers improve with or without AI assistance. This device is a standalone body composition analyzer, not a diagnostic aid for human readers. The clinical tests were for comparing the device's performance to another device.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone performance study was conducted. The "clinical comparison tests" evaluate the device's outputs (PBF, MBF, LBM, BMR, etc.) against the predicate device's outputs directly. The device operates independently to measure and estimate these parameters.

7. Type of Ground Truth Used

The "ground truth" for the test set was essentially the measurements and estimations provided by the predicate device (ZEUS 9.9). The study aimed to show that the new devices produced comparable results to an already legally marketed, substantially equivalent device. There is no mention of an independent, gold-standard reference ground truth (e.g., DEXA scan, hydrostatic weighing, pathology).

8. Sample Size for the Training Set

The document does not describe a "training set" in the context of an AI algorithm learning from data. The devices "measure body composition using an experimentally derived algorithm," which likely refers to a pre-defined algorithm based on physiological models and potentially some internal calibration data, rather than a machine learning model trained on a large dataset. Therefore, a sample size for a training set in the AI sense is not applicable/not provided.

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

As noted above, the concept of a "training set" and its "ground truth" (in the AI/machine learning sense) is not applicable to the description of this device's algorithm development. The algorithm is described as "experimentally derived," which typically implies a scientific or engineering approach to formula derivation and calibration, rather than a data-driven machine learning training process with ground truth labels.

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