Search Results
Found 2 results
510(k) Data Aggregation
(241 days)
The Finger Pulse Oximeter is a small portable device that measures % SpO2, pulse rate, and pulse indication on the finger. It may be used as a spot-check device in the home or clinical environment. The pulse oximeter will provide reliable measurements on pediatric patients weighing 35 lbs or more, and on adult patients. This device is not intended for continuous patient monitoring. There are no audible or visible patient alarms.
The AccuPulse Oximeter is a portable, lightweight, and battery-operated device that measures %SpO2 and pulse rate on the finger of a patient.
The provided text describes a 510(k) summary for the AccuPulse Oximeter, which relies on demonstrating substantial equivalence to a predicate device (Fox Oximeter). The document contains information about the device's performance through a clinical study.
Here's an analysis based on the provided input:
Acceptance Criteria and Device Performance
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
Correlation between blood gas and oximetry readings ≤ 2.0% | 1.87% correlation between blood gas and oximetry readings |
2. Sample Size for Test Set and Data Provenance
- Sample Size: Not explicitly stated. The document mentions "a clinical study" was performed but does not quantify the number of patients or samples.
- Data Provenance: The clinical data was collected in April 2005 at the Milwaukee VA Medical Center. Anesthesia Research Lab. This indicates prospective data collection in the USA.
3. Number of Experts and their Qualifications for Ground Truth
- Not explicitly stated. The clinical study was performed by the "Milwaukee VA Medical Center. Anesthesia Research Lab." This suggests medical professionals were involved in the data collection and analysis, but their specific roles, number, or qualifications for establishing ground truth are not detailed.
4. Adjudication Method for the Test Set
- Not explicitly stated. The study involved a "correlation between blood gas and oximetry readings," which implies a direct comparison rather than an adjudication process between multiple readers. Since we don't have details on how "blood gas" readings were obtained or interpreted, we cannot infer an adjudication method.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No, a MRMC comparative effectiveness study was not done. The documented clinical study focused on the correlation between device readings and blood gas measurements, not on comparing human readers with and without AI assistance.
6. Standalone (Algorithm Only) Performance Study
- Yes, a standalone performance study was implicitly done. The clinical study evaluated the "correlation between blood gas and oximetry readings" for the prototype devices that led to the Fox Oximeter. This assesses the device's (and its underlying algorithm's) ability to accurately measure oxygen saturation and pulse rate against a gold standard (blood gas analysis). The AccuPulse Oximeter uses the same microprocessor and signal processing software (AccuPulse V1.03) as the Fox Oximeter (FOX V1.03), implying the performance of the core algorithm is unchanged from the Fox Oximeter clinical test.
7. Type of Ground Truth Used
- Blood gas analysis (e.g., arterial blood gas - ABG). The clinical study measured the "correlation between blood gas and oximetry readings," indicating that blood gas analysis served as the reference standard for oxygen saturation.
8. Sample Size for the Training Set
- Not applicable / Not stated. This device is a pulse oximeter, which typically uses established physiological principles and algorithms rather than machine learning models that require distinct "training sets" in the modern AI sense. The reference to "FOX V1.03" and "AccuPulse V1.03" software suggests a pre-programmed algorithm.
9. How the Ground Truth for the Training Set was Established
- Not applicable / Not stated. As mentioned above, a traditional "training set" with ground truth in the context of AI/machine learning is not relevant here. The device's underlying algorithm is likely developed based on extensive physiological and engineering data, rather than a specific labeled training set from a clinical study.
Ask a specific question about this device
(99 days)
Ask a specific question about this device
Page 1 of 1