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The neoBLUE cozy phototherapy light is intended for the treatment for neonatal hyperbilirubinemia. It is designed to provide phototherapy treatment from underneath the baby. The neoBLUE cozy system must be used within a patient enclosure, such as a bassinet, an open crib, a warming table or an incubator. The neoBLUE cozy system can be used in a clinical setting or in the home.

The neoBLUE cozy system is a portable phototherapy light that delivers a narrow band of high-intensity blue light via blue light emitting diodes (LEDs) to provide treatment for neonatal hyperbilirubinemia. The neoBLUE cozy system is designed to vrovide phototherapy treatment from underneath the baby. The neoBLUE cozy system must be used within a patient enclosure, such as a bassinett, an open crib, a warming tat hlast or incubator.

Blue LEDs emit light in the range of 400 -- 550 nm (peak wavelength 450-470 nm). This range corresponds to the spectral absorption of light by bilirubin, and is thus considered to be the most effective for the degradation of bilirubin. Blue LEDs do not emit significant energy in the ultraviolet (UV) region of the spectrum, so there is no concern about UV exposure to the infant. As with all phototherapy lights, protective eyeshades must be used to protect the infant's eyes from excessive light exposure.

LEDs have minimal light output degradation over their lifetime with proper use. Nevertheless, the biomedical engineer can adjust the output of the LEDs using the potentiometer located behind the filter cover at the flat end of the light enclosure. The system is expected to operate at intensities above 30 uW/cm2/nm for approximately 3.000 hours.

The neoBLUE cozy system consists of five components: the neoBLUE cozy light source (light), the mattress, the disposable mattress cover, the bumper accessory, and the power supply.

This is a 510(k) premarket notification for a medical device called the "neoBLUE cozy LED Phototherapy System." This type of submission focuses on demonstrating substantial equivalence to a predicate device already on the market, rather than extensive clinical efficacy studies with predefined acceptance criteria in the same way a PMA (Premarket Approval) submission would.

Therefore, the document does not contain explicit acceptance criteria and a study proving the device meets those criteria in the traditional sense of an AI/software efficacy study. Instead, it demonstrates that the new device is as safe and effective as already legally marketed predicate devices.

However, I can extract information related to the device's performance based on the comparison provided:

1. Table of Acceptance Criteria (Implied) and Reported Device Performance

Since this is a 510(k) and not a clinical trial, "acceptance criteria" are not explicitly stated as performance metrics to be met by a study. Instead, the substantial equivalence is based on comparing the new device's features and specifications to those of predicate devices. The "reported device performance" are the specifications of the new device.

Regarding "study that proves the device meets the acceptance criteria" and other related points:

The document explicitly states: "This submission includes the results of testing of prototype devices to specifications. The results were as expected and no new issues of safety and effectiveness were raised as a result of the nonclinical testing."

This indicates that a study was performed internally to verify the device's specifications (e.g., light intensity, safety features, dimensions, weight, electrical standards compliance) matched the design and were comparable to the predicate devices. However, the details for most of your specific questions are not provided in this 510(k) summary.

Here's a breakdown based on the provided text, and what is not mentioned:

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

• Sample Size: Not specified. The testing was on "prototype devices," suggesting a small number of devices rather than a large clinical "test set" of patients.
• Data Provenance: Not specified, but generally, testing for 510(k) submissions of this nature would be conducted in a laboratory or engineering setting by the manufacturer (Natus Medical, Inc., USA). This would be retrospective in the sense that the device was fully designed before testing.

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

• Not Applicable. This device is a hardware phototherapy unit, not an AI/software device requiring expert interpretation of results to establish "ground truth." The ground truth for device performance is based on direct physical measurements and engineering standards (e.g., light intensity, temperature, electrical safety).

4. Adjudication method for the test set:

• Not Applicable. As above, no "test set" in the context of expert review or consensus 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:

• No. This is a hardware medical device, not an AI or imaging diagnostic tool. MRMC studies are not relevant to this submission.

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

• Not Applicable. This is a hardware medical device, not an algorithm.

7. The type of ground truth used:

• For the physical and functional aspects of the device, the "ground truth" would be established through:
  • Engineering specifications and measurements: E.g., light intensity measured by a radiometer, temperature measured by a thermometer, dimensions measured by calipers.
  • Compliance with recognized standards: Electrical safety (EN 60606-1, UL 2601-1, etc.), ingress protection (IPX4).
  • Biological/Physiological understanding: The efficacy relies on the known spectral absorption of light by bilirubin.

8. The sample size for the training set:

• Not Applicable. This is not an AI/machine learning device that requires a training set.

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

• Not Applicable. No training set as it's not an AI/ML device.

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