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510(k) Data Aggregation
(24 days)
Non-invasive monitoring of patient oxygen saturation (SpO₂), pulse rate, and plethysmographic pulse wave.
The SensAid sensors can be used for Adult, Pediatric and/or Neonate/Infant applications with the appropriate Disposable Positioning Tape (DPT) accessory.
The SensAid SpO2 Sensors measure oxygen saturation (SpO2), pulse rate, and plethysmographic pulse wave. The self-adhesive, latex free Disposable Positioning Tape (DPT) accessories quickly, comfortably, and securely attach to a patient. When a DPT is properly positioned on the patient and the SpO2 Multi-Site Reusable sensor is connected to the DPT, the sensor light emitting diodes and detector are aligned to ensure proper sensor performance.
The provided text is a 510(k) summary for a pulse oximeter accessory (sensors), not a standalone AI/device or clinical study report for software/AI. Therefore, much of the requested information (acceptance criteria, study details, sample sizes, ground truth establishment, expert involvement, MRMC study, standalone performance) is not applicable or not present in the document.
The document describes the SensAid SpO2 Sensors and their similarity to a predicate device. The primary "study" mentioned is a "desaturation study" to demonstrate substantial equivalence to the predicate device, not to prove specific performance against acceptance criteria in the way a novel AI device would.
Here's an attempt to answer the questions based only on the provided text, noting where information is absent or not relevant for this type of medical device submission:
1. Table of acceptance criteria and reported device performance
No explicit acceptance criteria (e.g., specific accuracy thresholds with upper/lower bounds) nor direct reported performance metrics (e.g., SpO2 accuracy, bias, precision) are provided for the SensAid sensors within this 510(k) summary. The study's conclusion is qualitative: "no significant deviation exists between the SensAid SpO2 sensors and the respective predicate devices."
2. Sample size used for the test set and the data provenance
- Sample Size: Not specified.
- Data Provenance: The study was conducted by an "independent research laboratory." No information on country of origin or whether it was retrospective or prospective is given, though clinical desaturation studies are typically prospective.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. For a pulse oximeter, ground truth for oxygen saturation is typically established by direct arterial blood gas analysis, not expert consensus interpreting images or data. No information on experts is provided.
4. Adjudication method for the test set
Not applicable. Adjudication methods like 2+1 or 3+1 are typically for reconciling discrepancies in expert interpretations (e.g., in imaging studies). This study compares device measurements to a reference standard (likely co-oximetry).
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
Not applicable. This is not an AI device or a study involving human readers or interpretation. It's a physiological monitoring device.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
This refers to a standalone medical device (the SensAid sensors) operating to measure physiological parameters. It does not contain an "algorithm only without human-in-the-loop" in the AI sense, as it performs its measurement function directly when applied to a patient. The "standalone performance" is implicitly what the desaturation study assesses by comparing it to the predicate device.
7. The type of ground truth used
While not explicitly stated, for a pulse oximeter desaturation study, the ground truth for oxygen saturation (SpO2) is typically established using arterial blood gas analysis (co-oximetry).
8. The sample size for the training set
Not applicable. These are physical sensors, not a machine learning model that requires a training set.
9. How the ground truth for the training set was established
Not applicable.
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