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MoistureMeterD Compact and LymphScanner is a device utilizing inter-am ratios of tissue dielectric constant (TDC) that supports local assessment of tissue water differences between affected and contralateral non-affected ann tissues to aid in forming a clinical judgment of unilateral lymphedema in adult women. The device is not intended to make a dagnosis or predict arm lymphedema.

The MoistureMeterD Compact (MMDC) and the LymphScanner are identical differently labelled bioelectrical analyzers to measure localized tissue fluid content. High-frequency electromagnetic waves produced in the hand-held main unit are guided into integrated open-ended coaxial probe. When the probe is placed onto the skin, skin and upper subcutis is exposed to electromagnetic field. In skin and upper subcutis electromagnetic fields are interacting with tissue water molecules by rotation causing absorption of electromagnetic energy in tissue. The devices measure the amplitude and phase shift of the reflected electromagnetic wave and calculate tissue dielectric constant (TDC). Tissue dielectric constant (TDC) is an index of localized tissue water content. The measured TDC value can be converted into Percentage Water Content (PWC) value.

The provided text describes the Delfin Technologies Ltd. MoistureMeterD Compact and LymphScanner devices and their substantial equivalence to a predicate device (MoistureMeterD). It discusses the indications for use, technology, and clinical studies used to demonstrate substantial equivalence, but it does not explicitly provide a table of acceptance criteria and reported device performance with specific values.

However, based on the information provided, I can infer the type of study conducted and the general nature of the acceptance criteria.

Inferred Acceptance Criteria and Reported Device Performance (General):

Study Details based on the provided text:

1. Sample Size used for the test set and the data provenance:

   • Sample Size: The text states clinical studies were conducted in "healthy female volunteers, women waiting for surgery of breast cancer, women operated for breast cancer, women at risk for lymphedema or a diagnosis of lymphedema." It does not specify the exact number of participants in these studies for the test set.
   • Data Provenance: Not explicitly stated (e.g., country of origin, specific institutions). The studies are referred to as "clinical studies," implying prospective data collection relevant to the device's application.
   • Retrospective or Prospective: Implied to be prospective ("clinical studies in healthy female volunteers, women waiting for surgery..."), where data was likely collected specifically for the purpose of demonstrating equivalence.

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

   • The document does not provide information on "experts" establishing ground truth in the traditional sense (e.g., radiologists interpreting images). The device itself measures tissue dielectric constant (TDC)/Percentage Water Content (PWC), which is a direct physiological measurement. The "ground truth" implicitly relates to the physiological state of lymphedema, which is then assessed by comparing the device's readings.
   • The text mentions the device "supports local assessment of tissue water differences... to aid in forming a clinical judgment," but it doesn't detail how that clinical judgment (ground truth for lymphedema diagnosis) was established in the study, nor the qualifications of clinicians making these judgments within the context of the study.

3. Adjudication method for the test set:

   • The document does not describe any adjudication method. This is likely because the study focuses on the instrumental equivalence of measurements (TDC/PWC ratios) rather than a diagnostic performance evaluation where multiple human readers might disagree.

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:

   • No, an MRMC comparative effectiveness study was not done. The device (MoistureMeterD Compact/LymphScanner) is a measurement tool, not an AI-assisted diagnostic algorithm intended to improve human reader performance in interpreting complex data (like medical images). The study focused on the equivalence of the device's measurements to a predicate device.

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

   • This is not applicable in the typical sense of AI algorithms. The device itself is a standalone measurement instrument. The study essentially compared the standalone performance of the new devices (MoistureMeterD Compact/LymphScanner) to the standalone performance of the predicate device (MoistureMeterD) in generating TDC/PWC ratios. The "algorithm" here is the embedded software that calculates TDC/PWC from raw electromagnetic wave data. The core finding was that these algorithmic outputs were not statistically different between the new and predicate devices.

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

   • The "ground truth" in this context is implicitly the actual physiological tissue water content and its differences between arms, as measured by the predicate device. The study aims to show that the new devices produce measurements that are equivalent to this established "truth" (i.e., the predicate device's measurements).
   • The clinical judgment of unilateral lymphedema, which the device aids in forming, would be an outcome, but the study focuses on the equivalence of the measurement values themselves (TDC/PWC ratios) to the predicate device.

7. The sample size for the training set:

   • The document does not specify a separate "training set" sample size. The study described is for validation/equivalence of the measurement itself, not for training a machine learning model in the conventional sense. The device's underlying physics-based calculations are already established.

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

   • As there's no explicitly mentioned "training set" for an AI model, this question is not applicable in the context of the provided text. The device's functionality is based on established physical principles of electromagnetic wave interaction with tissue water, rather than being "trained" on a dataset to learn patterns.

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Delfin MoistureMeterD is a device utilizing inter-arm ratios of tissue dielectric constant (TDC) that supports local assessment of tissue water differences between affected and contralateral non-affected arm tissues to aid in forming a clinical judgment of unilateral lymphedema in women. The device is not intended to make diagnosis or predict arm lymphedema.

The Delfin MoistureMeterD is a high-frequency bioelectrical analyzer used to detect tissue fluid. The device accurately measures voltage, current and the phase shift of the reflected current of the attenuated electric field and calculate capacitance and. moreover the tissue dielectric constant (TDC). The tissue dielectric constant (TDC) supports the measurement of local tissue fluid.

I apologize, but the provided text does not contain the specific details about acceptance criteria, device performance, sample sizes for test and training sets, ground truth establishment, expert qualifications, or adjudication methods for the Delfin MoistureMeterD. The document is an FDA 510(k) clearance letter and summary, which confirms the device's substantial equivalence to a predicate device but does not include the detailed study results that would typically contain such information.

To answer your request thoroughly, I would need a different document, such as a clinical study report, a scientific publication, or a more detailed section of the 510(k) submission that discusses the performance testing.

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