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510(k) Data Aggregation
(43 days)
LNOP BLUE OXIMETRY SENSOR
The LNOP Blue oximetry sensors are intended for the continuous nomitoring of functional oxygen saturation of arterial hemoglobin (SpO2) and pulse rate (measured by an SpO2 sensor) for pediatric, infant, and neonatal patients in hospitals, hospital-type facilities, mobile, and home environments.
The LNOP Blue Sensors are indicated for the continuous nonitoring of fifictional oxygen saturation of arterial hemoglobin (SpO2) and pulse rate (measured by an SpO2 sensor) for use with pediatric, infant, and neonatal patients with congenital cyanotic cardiac lesions in hospital-type facilities, mobile, and home environments.
The LNOP Blue Oximetry Sensors are fully compatible disposable sensors for use with Masimo SET compatible pulse oximeter monitors. The LNOP Blue sensor is substantially equivalent to Masimo's LNOP Inf-L sensor except it is designed for use on patients with congenital cyanotic cardiac lesions.
Here's a summary of the acceptance criteria and study details for the LNOP Blue Oximetry Sensors, based on the provided 510(k) summary:
Acceptance Criteria and Device Performance
Acceptance Criteria (Target Accuracy) | Reported Device Performance (Achieved Accuracy) |
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N/A (implied by predicate, but not explicitly stated as a target) | Less than 4% SpO2 ARMS in the 60%-80% SaO2 range |
N/A (implied by predicate, but not explicitly stated as a target) | Less than 3% SpO2 ARMS in the 80%-100% SaO2 range |
Note: The 510(k) summary doesn't explicitly state the acceptance criteria as a pre-defined numerical target for accuracy. Instead, it reports the achieved accuracy, implying that these results were deemed acceptable for substantial equivalence, likely benchmarked against predicate devices and clinical expectations for this patient population.
Study Details
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Sample Size used for the test set and the data provenance:
- Sample Size: Not explicitly stated as a numerical count of patients. It refers to "pediatric, infant, and neonatal patients with congenital cyanotic cardiac lesions."
- Data Provenance: The study was "Clinical studies... performed using the LNOP Blue Disposable oximetry sensors with Masimo SET Radical Pulse Oximeters." This indicates a prospective clinical study, typically conducted in a hospital setting. The country of origin is not specified, but given the FDA submission, it's generally assumed to be carried out under U.S. clinical trial standards, or internationally recognized standards acceptable to the FDA.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable. The ground truth was established by a device, not human experts.
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Adjudication method for the test set:
- Not applicable. The ground truth was established by objective device measurements, not expert consensus requiring adjudication.
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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 device is an oximetry sensor, not an AI-assisted diagnostic tool requiring human reader studies.
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If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance study was conducted. The "Clinical testing of the LNOP Disposable sensors resulted in an accuracy of less than 4% SpO2 ARMs in the range of 60%-80% SaO2 and less than 3% SpO2 ARMS in the range of 80%-100%." This reflects the device's performance in measuring SpO2 values directly against a reference standard.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Ground Truth Type: Objective physiological measurement. The ground truth was "the arterial hemoglobin oxygen determined from arterial blood samples with a CO-Oximeter." This is considered a gold standard for blood oxygen saturation measurement.
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The sample size for the training set:
- Not applicable/Not specified. This device is a physiological sensor, not a machine learning algorithm that requires a training set in the typical sense. Its design and performance are based on established physiological principles and engineering.
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How the ground truth for the training set was established:
- Not applicable. As noted above, training sets and associated ground truths in the context of machine learning are not relevant to this type of device.
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