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The TANITA family of InnerScan Body Composition Monitors measure body weight and impedance and estimate percentage of body fat and body water, visceral fat rating, bone mass, muscle mass, physique rating, daily calorie intake (DCI) and metabolic age using BIA (bioelectrical impedance analysis). They are intended for use by healthy children 7-17 years old and healthy adults with active, moderately active, to inactive lifestyles for body composition assessment in the home environment.

The TANITA InnerScan Body Composition Monitor is a computer-operated body composition analyzer that utilizes BIA (bioelectrical impedance analysis) to determine body fat percent, body water percent, muscle mass, bone mass, visceral fat rating, metabolic age, physique rating and daily calorie intake.

The provided text is a 510(k) summary for the TANITA InnerScan Body Composition Monitor. It establishes substantial equivalence by comparing it to predicate devices. However, it does not include detailed information about specific acceptance criteria for performance metrics, nor does it describe a study specifically designed to prove the device meets those criteria with statistical rigor.

Instead, the submission relies on the established "Foot-to-Foot" BIA methodology, which was used in the predicate devices, and states that the InnerScan monitor "performs as well as the predicate devices and therefore have proven its safety and efficacy." This is a common approach in 510(k) submissions, where substantial equivalence often hinges on similar technology and intended use, rather than presenting new, detailed performance studies with explicit acceptance criteria.

Therefore, many of the requested details cannot be extracted directly from the provided text.

Here's a breakdown of what can be inferred or stated as missing:

1. A table of acceptance criteria and the reported device performance

This information is not provided in the document. The 510(k) summary asserts that the device "performs as well as the predicate devices" based on the established BIA methodology, but it does not specify quantitative acceptance criteria (e.g., "accuracy of X% +/- Y kg") or report specific performance values for the InnerScan device against such criteria.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the document. The text mentions "in-house BIA," "DEXA reference methods," "Deuterium Dilution and MRI reference methods" were used as analytical/measurement techniques, but it doesn't describe specific test sets, their sizes, or provenance.

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)

This information is not provided in the document. The 510(k) relies on reference methods like DEXA, Deuterium Dilution, and MRI, which are considered objective measurements for body composition. It does not mention human experts establishing ground truth for the test set.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not provided in the document. No 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

This is not applicable to this device. The TANITA InnerScan Body Composition Monitor is a standalone device that provides direct measurements and estimations of body composition. It is not an AI-assisted diagnostic tool that aids human readers (e.g., radiologists interpreting images).

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

The device itself is a standalone algorithm (BIA methodology) that provides body composition measurements. The submission focuses on the performance of this system without human intervention in the measurement process, but it doesn't detail a specific "standalone performance study" in the modern sense of algorithm evaluation against a ground truth dataset with reported metrics. Its performance is implicitly compared to predicate devices using the same fundamental methodology.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The document mentions several reference methods that serve as a form of "ground truth" or highly accurate measurement techniques:

• DEXA (Dual-energy X-ray absorptiometry): A widely accepted clinical standard for body composition.
• Deuterium Dilution: A method for measuring total body water, which can be used to estimate body composition.
• MRI (Magnetic Resonance Imaging): Can be used to assess body fat distribution and muscle mass.

These are considered objective, quantitative body composition assessment methods.

8. The sample size for the training set

This information is not provided in the document. The BIA methodology is described as "patented in-house BIA," implying it's based on internal development, but details about training data are absent.

9. How the ground truth for the training set was established

This information is not provided in the document. While reference methods like DEXA, Deuterium Dilution, and MRI are mentioned, the document does not elaborate on how these were used to establish ground truth for any training data that might have been used to develop the "in-house BIA."

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A body composition analyzer that measures body weight and impedance and estimates percentage of body fat and body water using BIA (bioelectrical impedance analysis) in healthy children (7-17 years old) and healthy adults.

The TANITA Scale plus Body Fat Monitor with Body Water Percentage is a computeroperated body composition analyzer that utilizes BIA (bioelectrical impedance analysis) to determine body fat and body water percentage.

The provided text is a 510(k) summary for the TANITA Scale plus Body Fat Monitor with Body Water Percentage. It focuses on establishing substantial equivalence to predicate devices rather than providing detailed acceptance criteria and a standalone study for the applicant device. Therefore, much of the requested information regarding specific acceptance criteria, detailed study design, and ground truth establishment is not present in the given document.

Here's a breakdown of the available information:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative acceptance criteria in terms of specific performance metrics (e.g., accuracy, precision, bias) for body fat or body water percentage. Instead, it relies on a qualitative statement of substantial equivalence to predicate devices.

2. Sample size used for the test set and the data provenance

The document does not specify the sample size used for any test set or the data provenance (country of origin, retrospective/prospective). It mentions "in-house BIA and DEXA/Deuterium Dilution reference methods" which implies some form of testing was conducted, but details are absent.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This information is not provided in the document.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

This information is not provided in the document.

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

This type of study is not applicable to this device. The device is a "Scale plus Body Fat Monitor" and directly measures/estimates body composition, it is not an AI-powered diagnostic tool requiring human reader interpretation in a medical imaging context.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

A direct "standalone" performance study with quantifiable results is not explicitly described. The closest indication is the "validation of the new total body water algorithm," but no details on this validation are provided. The general statement of performance is in comparison to predicate devices, implying performance of the device itself.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The document mentions "DEXA/Deuterium Dilution reference methods" as ground truth comparators.

• DEXA (Dual-energy X-ray absorptiometry) is a widely accepted, gold-standard method for measuring body composition (bone mineral density, lean soft tissue, and fat mass).
• Deuterium Dilution (D2O dilution method) is a reference method for determining total body water.

8. The sample size for the training set

This information is not provided in the document. The submission focuses on substantial equivalence based on a previously approved methodology and validation of a new algorithm, not on a machine learning model's training set.

9. How the ground truth for the training set was established

This information is not provided in the document, as it does not describe a machine learning model's training process.

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A body composition analyzer that measures body weight and impedance and estimates total and segmental body fat percent, body mass index (BMI), fat mass, fat-free mass, regional muscle mass, basal metabolic rate, and total body water using BIA (bioelectrical impedance analysis) in healthy children (7-17 years old) and healthy adults.

The TANITA BC-418, Segmental Body Composition Analyzer is a computer-operated body composition analyzer that utilizes BIA (bioelectrical impedance analysis) to determine body fat percent and regional muscle mass.

Here's an analysis of the provided text, focusing on the acceptance criteria and the study that proves the device meets those criteria:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state quantitative acceptance criteria in a dedicated section. Instead, it relies on a comparison to a predicate device to establish substantial equivalence.

Explanation of "Acceptance Criteria" in this context: For a 510(k), the primary "acceptance criterion" is often that the new device is "substantially equivalent" to a legally marketed predicate device. This means it has the same intended use, and the same technological characteristics, or if it has different technological characteristics, it does not raise different questions of safety and effectiveness, and the information submitted demonstrates that the device is as safe and effective as the predicate device. The document implies that the performance of the predicate (Model TBF-410) serves as the benchmark for acceptable performance.

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

• Sample Size: The document does not specify a sample size for the test set. It mentions "Side by side comparisons" and "results of using the previously approved BIA methodology," but no numerical data for the number of subjects or measurements.
• Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective. The study appears to be an internal comparison ("in-house BIA and DEXA reference methods") conducted by Tanita.

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

The document mentions "in-house BIA and DEXA reference methods" for establishing ground truth, but it does not specify the number of experts or their qualifications who were involved in operating the DEXA or BIA reference methods, or in interpreting the results to establish ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for the test set. The comparison seems to be direct performance against the predicate and reference methods rather than an expert consensus process on specific cases.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted. The study described is a comparison of the new device to a predicate device and reference methods, not an assessment of human reader performance with or without AI assistance. The device itself is a measurement tool, not an AI for interpretation.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done

Yes, the study described is essentially a standalone performance assessment of the device (algorithm only). The device is a diagnostic measurement tool. The evaluation focuses on its analytical performance (body composition measurements) compared to reference methods and a predicate, without considering a human-in-the-loop aspect for interpretation or decision-making.

7. The Type of Ground Truth Used

The ground truth was established using:

• "In-house BIA and DEXA reference methods."
  • DEXA (Dual-energy X-ray absorptiometry) is widely considered a gold standard for body composition measurement.
  • "In-house BIA" (of the predicate device) is used as a reference point for substantial equivalence, implying its output is accepted as valid.

8. The Sample Size for the Training Set

The document does not provide any information about a separate "training set" or its sample size. This type of device relies on established BIA principles and internal calibration, rather than a machine learning model that requires a distinct training set in the typical sense. The "training" here would likely refer to the development and calibration data used to create the BIA algorithms for body composition estimation embedded in the device, which is not detailed.

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

As no separate training set is explicitly mentioned or detailed, there is no information on how its ground truth was established. For BIA devices, the algorithms are typically derived from extensive research correlating impedance measurements with direct body composition methods (like DEXA or hydrostatic weighing) in general populations. The "in-house BIA and DEXA reference methods" mentioned likely refer to the validation of the device's algorithms, not a distinct training process for a machine learning model.

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MODELS TBF-300A, TBF-310, TBF-410, TBF-215, BF-350: A body composition analyzer that measures body weight and impedance and estimates body fat percent. BMI, fat mass, fat free mass, basal metabolic rate, and total body water using BIA (bioelectrical impedance analysis). MODELS TBF-611, TBF-612, TBF-621, TBF-622, BF-623, BF-625, BF-626, BF-541, BF-555, BF-542, TBF-551, BF-556, BF-558, BF-559, BF-572, TBF-521, Ultimate Scale 2001: A body composition analyzer that measures body weight and impedance and estimates body fat percent using BIA (bioelectrical impedance analysis).

The TANITA Body Fat Monitor/Scales (multiple models) are designed to determine body weight and body fat composition. Each model will offer a range of features. The system consists of two subdivided stainless steel footpad electrodes mounted on a platform scale. The four-foot electrodes are designed where each foot makes contact with a heel and toe electrode. Impedance of the lower extremities and body weight is measured simultaneously while the subject stands on the scale.

The provided text describes a 510(k) submission for TANITA Body Fat Analyzer TBF/BF/Ultimate Scale devices, asserting their substantial equivalence to a predicate device (TANITA Body Fat Analyzer Model TBF-105 K930599). However, it does not contain the detailed information necessary to fully answer all components of your request regarding acceptance criteria and the specific study proving the device meets those criteria.

The document largely focuses on establishing substantial equivalence based on the intended use, product description, and analytical method used, rather than presenting a performance study with detailed acceptance criteria and reported device performance.

Therefore, many sections below will indicate that the information is "Not provided in the document."

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

   • Sample Size: Not provided in the document. The document refers to "results of using the previously approved 'Foot-to-Foot' BIA methodology with our patented in-house BIA" but does not specify a sample size for any comparative study.
   • Data Provenance: Not provided in the document.

2. 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 provided. This device is a body composition analyzer. The "ground truth" would likely refer to a reference method (like DEXA or hydrostatic weighing) not expert consensus. The use of "DEXA reference methods" is mentioned, but without details on how it was applied or the expertise involved in its application for the study.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set

   • Not applicable/Not provided. Adjudication methods like 2+1 or 3+1 are typically used in studies involving human readers reviewing medical images or data. For a device like a body fat analyzer, the "ground truth" establishment would typically involve a reference measurement method rather than expert adjudication.

4. 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 standalone body composition analyzer, not an AI-assisted diagnostic tool for human readers. Therefore, an MRMC study and analysis of human reader improvement with AI assistance are not relevant to this submission.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

   • Yes, implicitly. The comparison describes the performance of the device itself (the "algorithm only," in a sense, as it performs the BIA measurement and calculation) against its predicate. The conclusion states, "the TANITA Body Fat Analyzer(s) TBF/BF/Ultimate Scales perform as well as the predicate device," which suggests an evaluation of the device's inherent measurement capabilities. However, specific standalone performance metrics are not given, only the assertion of equivalence.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

   • The document mentions "DEXA reference methods" as part of the analytical method/measurement for both the predicate and applicant device. This implies that DEXA (Dual-energy X-ray Absorptiometry), a widely accepted reference method for body composition, would be the primary type of "ground truth" or reference standard if a direct comparison study was performed. However, no specific study results using DEXA as ground truth are presented.

7. The sample size for the training set

   • Not applicable/Not provided. This document describes a medical device, not a machine learning model that would typically have a specific training set. The BIA methodology is physics-based, not learned from data in the same way an AI algorithm is a "training set" in the context of deep learning. While calibration data might exist, it's not referred to as a "training set" here.

8. How the ground truth for the training set was established

   • Not applicable/Not provided, as there is no mention of a "training set" in the context of an AI/ML model. If "training set" refers to calibration data or development data, the document does not elaborate on how ground truth was established for that data, other than mentioning "patented in-house BIA and DEXA reference methods."

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