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The DYMEDIX Incorporated Reusable Airflow/Snore Scnsor is used with existing recording devices in support of diagnostic recording of nasal, oral airflow and breath sounds (snore). The sensors are used with patients who require a sleep study.

During evaluation of sleep disorders and other clinical applications for airflow recording, an airflow sensor is applied to the patient, typically under the nasal passages, to support recording of airflow signals. During the evaluation of sleep disorders and other clinical applications for breath sounds (snore) recording, a snore sensor is applied to the patient to support recording of snore signals. These recorded signals are analyzed by a qualified physician, clinician or licensed medical practitioner in support of diagnosis of sleep and airflow disorders.

The Reusable Airflow/Snore Sensor is a pyroelectricity (pyro) and piezoelectric (piezo) sensor. The temperature difference between patient exhaled air and ambient air generates a small voltage signal, which can be recorded as airflow by diagnostic recording equipment. The vibration from breath sounds (snoring) produce a small voltage signal, which can be recorded as snoring by diagnostic recording equipment. The user's recording device displays the resulting signal for interpretation by a trained physician or clinician.

Polarized Polyvinylidene Fluoride Film (PVDF) is the temperature sensing matcrial. The polarized PVDF material has pyro (temperature) signal generation capability. Polarized PVDF material self generates a small electrical charge in the presence of temperature. The predicate Disposable Airflow/Snore Sensor (K990321) applied the same PVDF Film airflow and breath sound (snore) sensing technology.

The DYMEDIX Airflow/Snore Sensor consists of a temperature sensitive material (PVDF) molded into a reusable housing. The molding material is PVC. This same PVC material is used in the lead wires from the sensor. This same PVC material is used with the predicate DYMEDIX Disposable Airflow/Snore Sensor lead wires, reference K990321.

The DYMEDIX Reusable Airflow/Snore Sensor is intended for diagnostic recording of nasal, oral airflow, and breathing sounds (snore) in patients requiring a sleep study, used in conjunction with existing recording devices.

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

   The provided document does not explicitly define acceptance criteria as quantitative thresholds for performance metrics. Instead, it relies on demonstrating "equivalent performance" to a predicate device through laboratory data.

   Performance Metric/Characteristic	Acceptance Criteria (Implicit)	Reported Device Performance
   Airflow Sensing Capability	Equivalent to predicate (DYMEDIX Gemini Sensor K990321)	Demonstrated through laboratory data using polarized PVDF film
   Snore Sensing Capability	Equivalent to predicate (DYMEDIX Gemini Sensor K990321)	Demonstrated through laboratory data using polarized PVDF film
   Material (temperature sensing)	Polarized Polyvinylidene Fluoride Film (PVDF)	Uses Polarized Polyvinylidene Fluoride Film (PVDF)
   Material (housing/lead wires)	PVC material used in predicate (K990321)	Uses PVC material, same as predicate DYMEDIX Disposable Airflow/Snore Sensor lead wires
   Biosafety/Biocompatibility	Not explicitly stated, but implied by "standards certifications"	"standards certifications" support safety

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 states that "Representative laboratory data are presented to demonstrate equivalent performance." However, it does not specify the sample size used for this testing or the provenance of the data (e.g., number of subjects, country of origin, retrospective or prospective nature).

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 study relies on laboratory data for "equivalent performance" rather than expert clinical assessment as ground truth for a test set.

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

   This information is not provided in the document. The study focuses on laboratory data demonstrating equivalence, not human-in-the-loop diagnostic accuracy with adjudication.

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:

   No, a multi-reader multi-case (MRMC) comparative effectiveness study was not performed. This device is a sensor, not an AI-powered diagnostic tool requiring human reader interpretation in the context of an MRMC study.

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

   The document describes the device as a "sensor" that generates voltage signals for interpretation by a "trained physician or clinician." It does not mention an algorithm or standalone performance without human interpretation. The study focused on demonstrating the sensor's ability to produce signals equivalent to a predicate device in a laboratory setting.

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

   The "ground truth" implicitly used for this device is the performance of the predicate device, the DYMEDIX Gemini Sensor (Disposable) (K990321), and the Pro-Tech Services Inc. Cannula-Style Thermocouple Airflow Sensor (K913396). The study aimed to show equivalent performance to these legally marketed devices based on "representative laboratory data." There is no mention of expert consensus, pathology, or outcomes data as ground truth for this particular submission.

8. The sample size for the training set:

   This information is not applicable and is not provided. The DYMEDIX Reusable Airflow/Snore Sensor is a physical sensor, not a machine learning model that requires a training set.

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

   This information is not applicable and is not provided, as the device is not a machine learning model.

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