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510(k) Data Aggregation
(2 days)
SA-3000P
The device provides noninvasive measurement of pulse waveform and heart rate by photoelectric plethysmography. The anatomical site for taking the measurement is the left index finger. The device is intended for use with patients age 18 years and older and with a weight of 100 lbs or greater. The device is indicated for use in hospitals, health care clinics and physicians' offices
The device is a photoelectric plethysmograph with is used to estimate blood flow in a region of the body using photoelectric measurement techniques
The provided text is a 510(k) summary for the SA3000P System, a photoelectric plethysmograph. It does not contain the detailed information necessary to complete all sections of your request regarding acceptance criteria and the comprehensive study that proves the device meets them.
Here's what can be extracted and what information is missing:
1. A table of acceptance criteria and the reported device performance
The document broadly states:
Acceptance Criteria | Reported Device Performance |
---|---|
Functions substantially equivalent to the predicate | "The functions are substantially equivalent to the predicate" |
Meets "the same safety and performance Standards as the predicate" | "the device meets the same safety and performance Standards as the predicate." |
Missing Information: The specific quantitative acceptance criteria for parameters like accuracy of pulse waveform, heart rate measurement, safety, or specific performance standards are not detailed in this summary.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Missing Information: This information is not provided in the 510(k) summary. The summary mentions "performance testing showing that The functions are substantially equivalent to the predicate," but no details about the test set, sample size, or data provenance are included.
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)
Missing Information: This information is not provided. The summary does not describe how ground truth was established or if any experts were involved in a blinded assessment.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Missing Information: No adjudication method is mentioned in the provided text.
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
Missing Information: The device described is a photoelectric plethysmograph, which measures physiological parameters like pulse waveform and heart rate. It is not an AI-assisted diagnostic imaging device that would typically involve human readers or MRMC studies for comparative effectiveness of AI vs. human performance. Therefore, an MRMC study is highly unlikely to have been conducted for this type of device. The document does not mention any AI component.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Missing Information: As this device is a plethysmograph (a measurement device), its performance would inherently be "standalone" in terms of its ability to measure pulse waveform and heart rate. However, the details of how this "standalone performance" was assessed (e.g., against a gold standard reference device) are not provided. The document focuses on equivalence to a predicate, not necessarily absolute accuracy.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Missing Information: The type of ground truth used for performance validation is not specified. For a plethysmograph, ground truth would typically be established by a reference method or a gold standard device for pulse waveform and heart rate measurement.
8. The sample size for the training set
Missing Information: This device does not appear to involve machine learning, therefore, there would not be a "training set" in the context of AI/ML models. The summary focuses on hardware performance and equivalence.
9. How the ground truth for the training set was established
Missing Information: Not applicable, as there is no indication of a machine learning component requiring a training set.
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