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The Philips Biosensor BX100 is a physiological measurement device for use by healthcare professionals to aid in the treatment and management of patient conditions in general care areas of a hospital.

The Philips Biosensor BX100 is intended for use by healthcare professionals on patients 18 years of age and older. This chest-worn biosensor collects, stores, and transmits physiological data and contextual parameters to a qualified backend system. Physiological data includes respiration rate and heart rate; contextual parameters include ambulation, activity level, and posture.

The Philips Biosensor BX100 is a single patient use, small, lightweight, chest-worn sensor. which collects, stores and transmits physiological data and contextual parameters to a qualified backend system. The Philips Biosensor BX100 includes Bluetooth communication capabilities. The Philips Biosensor BX100 has an LED indicator and a power button, and requires integration into a qualified system. The Philips Biosensor BX100 sends the collected patient data wirelessly to a qualified system directly or indirectly through IT equipment. Physiological data provided by the Philips Biosensor BX100 includes respiration rate and heart rate; contextual parameters include ambulation, activity level, and posture. The Biosensor has a 115hour wear life.

This document describes the Philips Biosensor BX100, a physiological measurement device. The provided text outlines the device's characteristics, intended use, and substantial equivalence to predicate devices, supported by various performance data including clinical trials.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" for each parameter in a formal table; however, it implicitly defines performance targets by comparing the device's capabilities to its predicate devices and referencing relevant standards. The key physiological measurements are Heart Rate and Respiration Rate.

Other performance metrics for wear duration and environmental ranges are also mentioned in the comparison to predicate devices, suggesting these are also part of the acceptance criteria:

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

• Clinical Performance Testing:
  • Philips G10 Biosensor Wear Study: 25 subjects.
  • G10 Algorithm Validation Study: 53 subjects.
• Biocompatibility Testing:
  • Cytotoxicity: Clinical study (25 subjects), Osmolarity evaluation, ISO 10993-10 irritation testing (3 animals), ISO 10993-10 sensitization testing (15 animals).
• Data Provenance: The document does not specify the country of origin for the clinical study data or if the studies were retrospective or prospective. Given the nature of a 510(k) submission for a new device, these would almost certainly be prospective clinical studies conducted specifically for regulatory submission.

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

The document does not provide details on the number of experts or their qualifications for establishing ground truth specifically for the algorithm validation study. It references "capnography" as the reference for respiration rate accuracy, implying a recognized medical measurement standard as ground truth, but doesn't detail human expert involvement in its establishment.

For biocompatibility, the tests are standard ISO procedures (animal studies, clinical subject reactions).

4. Adjudication Method for the Test Set

The document does not describe any adjudication method (e.g., 2+1, 3+1) for resolving discrepancies in ground truth establishment. It implies that the reference measurements (e.g., capnography) serve as the singular ground truth for algorithm validation.

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

No, an MRMC comparative effectiveness study was not mentioned. The studies described are focused on validating the device's direct physiological measurement accuracy and wearability, not on how human readers/clinicians improve with AI assistance from this specific biosensor data. The device collects and transmits data to a backend system, suggesting it's an input to broader clinical decision-making, not an "AI assistance" tool in itself as might be seen for image analysis.

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

Yes, the "G10 Algorithm Validation Study" appears to be a standalone performance study. The study was conducted to "acquire physiological and contextual data using the Philips Biosensor BX100 and reference measurements to validate design specifications related to Biosensor software algorithms." This implies evaluating the algorithm's performance directly against established reference measurements, without human interpretation as part of the primary evaluation endpoint.

7. The Type of Ground Truth Used

• Physiological Data (Respiration Rate, Heart Rate): Reference measurements from established methods, specifically capnography for respiration rate for the 53-subject algorithm validation study. The heart rate algorithm accuracy is stated to be compliant with IEC 60601-2-47 and IEC 60601-2-27.
• Adhesive Performance/Wear Study: Demonstrated by "average adhesive performance of 6.7 days (excluding electrical performance) and 4.8 days (including electrical performance) with no significant skin reactions." This suggests direct observation and measurement of wear duration and skin reaction.
• Biocompatibility: Established through standard in-vitro (cytotoxicity, though with noted method incompatibility due to device material) and in-vivo (animal irritation and sensitization studies, and a clinical study on human subjects) tests against ISO standards.
• Bench Testing: Verifying system-level device specifications, mechanical and electrical specifications, and packaging integrity.

8. The Sample Size for the Training Set

The document does not provide any information about a training set or its sample size. This submission is for a medical device that measures physiological parameters. While it contains "algorithms" for processing these signals, it is treated more like a sensor/monitor than a complex AI/ML diagnostic tool that would typically have a distinct training phase documented for regulatory submission. The algorithms are likely signal processing and filtering algorithms, rather than machine learning models that require large labeled training datasets in the conventional sense.

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

Since no training set is mentioned in the provided text, no information is available on how its ground truth might have been established.

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