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BC is a software accessory for RJL Systems Quantum-X, Quantum Desktop, and Quantum-III. It requires a separate personal computer running the Windows Operating System. To automatically retrieve data from the Quantum Desktop and Quantum-III analyzers, the computer must have an available serial port.

Indications for Use:

Calculation and Historical Tracking of:

• Actual Impedance .
• Actual Phase Angle (PA) .
• Estimated Body Fat (FAT) .
• Estimated Fat Free Mass (FFM) .
• . Estimated Total Body Water (TBW)
• Estimated Intra-Cellular Water (ICW) ●
• Estimated Extra-Cellular Water (ECW) ●
• Estimated Basal Metabolic Rate (BMR) .
• Estimated Daily Energy Expenditure (DEE) .
• Actual Body Mass Index (BMI) .

BC is intended only for use on normally healthy adults and adolescents age 9-94.

The BC application is intended to be used as a software accessory to RJL Systems' existing line of Bio-Impedance Analyzers ( K830292 and K862383 ). The user obtains values for the Resistance and Reactance of an individual, and enters these numbers, along with the individual's name, age, height, weight, gender, activity level, frame size, and optionally, subject ID and desired target weight. These values are stored in a database to enable historical tracking, and are then used in a series of prediction equations to estimate the parameters listed above.

For estimating body composition parameters, the BC application relies on prediction equations developed as the result of clinical studies. The user is provided the opportunity to select from several different sets of equations, each assembled from one or more studies. References are provided for every set of equations, except for those contained in the original RJL BIA-103 device software.

The BC application is non-diagnostic in nature and does not express any opinions with regard to any specific disease or medical condition.

The provided 510(k) summary for the "BC body composition software" does not contain a study that proves the device meets specific acceptance criteria in the way a clinical performance study would for a diagnostic device.

Instead, the submission focuses on substantial equivalence to predicate devices based on its indications for use and general functional equivalence. There are no detailed acceptance criteria or a dedicated study with performance metrics in the provided text.

Here's a breakdown of the requested information based on the available text:

Acceptance Criteria and Device Performance

The document does not explicitly state acceptance criteria in the form of numerical thresholds for accuracy, sensitivity, specificity, or other performance metrics. The acceptance is based on the device's functional equivalence to predicate devices and its ability to calculate and track the listed body composition parameters.

Study Details (Based on available information)

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):
The document does not describe a specific "test set" or a separate clinical study with a defined sample size for the BC software itself. It mentions that the "prediction equations developed as the result of clinical studies" are used. However, it does not provide details on these underlying studies' sample sizes or data provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable. There is no specific test set described for the BC software itself where experts established ground truth. The "prediction equations" are referred to as being from "clinical studies," but no details about these studies or expert involvement are provided.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
Not applicable. No test set or adjudication method is described.

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. This device is a software accessory for body composition analysis; it does not involve human readers interpreting medical images or data that would typically be part of an MRMC study.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
The device itself is a "software accessory" that takes input from Bio-Impedance Analyzers and user-entered data (name, age, height, weight, gender, activity level, frame size). It then applies prediction equations. While the calculations are algorithm-only, the setup inherently has a "human-in-the-loop" for data entry and selection of equations. There is no standalone performance study described for the software's accuracy against a ground truth. Its performance is tied to the validity of the selected prediction equations, which are from other "clinical studies" not detailed here.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
The document states the BC application "relies on prediction equations developed as the result of clinical studies." For these underlying studies that generated the prediction equations, the "ground truth" for body composition parameters would typically be established using methods like:

• DEXA (Dual-energy X-ray absorptiometry): A gold standard for body composition.
• Hydrostatic weighing: Another common validation method.
• Isotope dilution (for TBW): For accurate total body water measurements.
• Anthropometric measurements combined with other techniques: Depending on the specific parameter.
  However, the document for the BC software does not specify the ground truth used in the original clinical studies that developed the prediction equations.

8. The sample size for the training set:
Not applicable for the BC software itself. The BC software utilizes pre-existing prediction equations. The sample sizes for the training sets of those underlying prediction equations are not provided in this 510(k) summary.

9. How the ground truth for the training set was established:
Not applicable for the BC software itself. As above, the ground truth establishment for the training sets of the underlying prediction equations is not detailed in this document.

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