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510(k) Data Aggregation
(62 days)
BODY COMPOSITION ANALYZER, MODEL: IOI 353
This device is intended to estimate PBF(Percentage of Body Fat), MBF(Mass of Body Fat), LBM(Lean Body Mass), TBW(Total Body Water), BMI(Body Mass Index), BMR(Basic Metabolic Rate), Segmental LBM, ICW(Intra-Cellular Water), ECW(Extra-Cellular Water), and ratio of ECW/TBW using the BIA(Bio-electrical Impedance Analysis) method. The device measures or calculates the impedance, BMI(Body Mass Index), weight, and WHR(Waist to Hip Ratio) of the user.
This device is intended for use only in healthy subjects between the age of 7-89.
The ioi 353 is body composition analyzer is intended for use only in healthy subjects between the age of 7-89. The device employs BIA(Bio-electrical Impedance Analysis) method and 8 electrodes placed on hands and feet, and then measure body composition using an experimentally derived algorithm. The device is powered by AC adapter.
The Jawon Medical Body Composition Analyzer Model ioi 353 did not undergo clinical testing to establish its performance against specific acceptance criteria. Instead, substantial equivalence was claimed based on compliance with electrical safety and electromagnetic compatibility standards and the device's technological similarity to predicate devices.
1. Table of Acceptance Criteria and Reported Device Performance
| Parameter | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Electrical Safety | Compliance with IEC 60601-1 | Meets IEC 60601-1 requirements |
| Electromagnetic Compatibility (EMC) | Compliance with EN 60601-1-2 | Meets EN 60601-1-2 requirements |
| Device Functionality | Measures/outputs/determines the same quantities as predicate device GAIA 359 PLUS (PBF, MBF, LBM, TBW, BMI, BMR, Segmental LBM, ICW, ECW, ECW/TBW, impedance, weight, WHR). | Same functionality as GAIA 359 PLUS. |
| Measurement Frequencies | Uses the same frequencies as predicate device GAIA 359 PLUS. | Uses the same frequencies as GAIA 359 PLUS. |
| Electrode Pattern and Number | Uses the same electrode pattern and number of electrodes as predicate device GAIA 359 PLUS. | Uses the same electrode pattern and number of electrodes as GAIA 359 PLUS. |
2. Sample Size Used for the Test Set and Data Provenance
No specific test set or clinical data is provided for the ioi 353 device, as it was not clinically tested. The evaluation relies on non-clinical tests (electrical safety and EMC) and comparison to a predicate device.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
Not applicable, as no clinical test set was used for the ioi 353.
4. Adjudication Method for the Test Set
Not applicable, as no clinical test set was used for the ioi 353.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC study was conducted or referenced. The submission explicitly states the device was "not clinically tested."
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
No standalone performance study for the algorithm was conducted or referenced in this document. The device's performance is inferred by its similarity to predicate devices.
7. Type of Ground Truth Used
The ground truth for the device's safety and effectiveness relies on compliance with international standards (IEC 60601-1, EN 60601-1-2) and the established performance of its predicate device (GAIA 359 PLUS and XBIA 500), rather than direct comparison to a clinical ground truth.
8. Sample Size for the Training Set
Not applicable. This device is not described as having an AI algorithm requiring a training set in the context of this 510(k) summary. Its algorithm is "experimentally derived," implying a fixed, pre-determined calculation based on BIA, not a machine learning model.
9. How the Ground Truth for the Training Set Was Established
Not applicable, as no AI training set is mentioned for this device. The "experimentally derived algorithm" for the Body Composition Analyzer (using BIA) would typically be based on established physiological models and correlations between impedance measurements and body composition, often developed through various research studies, but not specifically described as a machine learning training set in this submission.
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