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The Positional Sleep Assessment System (PoSAS) Software is a software-only device intended for use by healthcare professionals to access previously recorded data for review and interpretation of sleep length, sleep disruptions, snoring, and positional sleep disordered breathing events in adults. The PoSAS Software is used in healthcare facilities to generate sleep study reports from data obtained with the Night Shift and pulse oximeter devices; reports include sleep/wake, position, snoring, SpO2, pulse rate, pulse event (6 bpm change), and/or desaturation event information. The report requires clinician interpretation of the results; it does not suggest a course of treatment or generate a diagnosis.

The Positional Sleep Assessment System (PoSAS) Software is a standalone desktop software application that provides the capability to generate reports from the data acquired with the Advanced Brain Monitoring (ABM) Night Shift (K140190) and/or a pulse oximeter. The PoSAS Software is currently compatible with the Nonin WristOx> (model 3150) cleared in K102350. The Night Shift is a small neck-worn device with software indicated for use in reporting position, movement, and sound so that positional changes in sleep quality and snoring can be assessed for adult patients only. The Nonin WrisOx₂ is a small wrist-worn device indicated for use in measuring, displaying, and storing functional oxygen saturation of arterial hemoglobin (SpO₂) and pulse rate of adults and pediatric patients. The PoSAS Software is intended to be used in healthcare facilities (e.g. Clinician's office) to transfer and analyze data obtained with the Night Shift and WristOx2 by adult patient's in the home or sleep lab. The Night Shift uses a USB 2.0 data cable to transfer data via Standard USB 2.0 flash drive and the WristOx₂ uses a proprietary USB 2.0 data cable to transfer data via USB 2.0 Virtual COM Port. Once the devices are recognized, the PoSAS Software graphical user interface (GUI) enables the user to synchronize the devices by writing the same computer date/time (down to the second) to each of the devices. The PoSAS Software also allows for the data to be erased from both the Night Shift and WristOx2 devices, and allows for the Night Shift device settings (I.e. Vibration feedback on, Vibration feedback off, or Trial mode) to be updated prior to recording. Once the clock times from the two devices are synchronized, and the user has set the desired settings of the Night Shift device (i.e., identical to features available using Night Shift software), the Night Shift and WristOx2 are worn by a patient to record sleep study data. The PoSAS Software reads the study files and recognizes the respective clock times within each file, and aligns the data for generation of a PoSAS report. The PoSAS Software report presents the Night Shift data, analyzed pulse oximeter data, and combines the positional data from the Night Shift with the analyzed results from the pulse oximetry data. A PoSAS Software report can be generated either from the data saved on the connected Night Shift and pulse oximeter devices or from data saved to the hard disk of the personal computer. Data from the Night Shift is displayed on the PoSAS Software report without modification. The PoSAS Software calculates oximetry data based on the once per second SpO₂ and pulse data acquired by the pulse oximeter. 3% or 4% desaturations and pulse events (6 bpm change) are also calculated. The PoSAS software combines Night Shift position information with calculated oximetry data to present metrics based on the patient's position (supine or non-supine).

Here's a breakdown of the acceptance criteria and the study details for the Positional Sleep Assessment System (PoSAS) Software, based on the provided 510(k) summary:

Acceptance Criteria and Device Performance

The provided document primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a formal table of specific acceptance criteria with corresponding performance metrics for all features. However, it does highlight key performance aspects and claims of equivalence.

From the text, the core acceptance criteria are implied to be:

• Accuracy and Sensitivity of Pulse Oximeter Event Recognition: The software must accurately recognize pulse events and 3%/4% desaturation events.
• Equivalence of SpO2 and Pulse Metrics: The PoSAS software's calculation and reporting of SpO2 and pulse rate metrics must be equivalent to the reference Sleep Profiler software.
• Equivalence of Position-Related Metrics: The display of position-related metrics, particularly when combining Night Shift data with oximetry, must be equivalent to what the predicate device or comparable reference software provides.

Reported Device Performance:

Study Used to Prove Device Meets Acceptance Criteria:

The document describes a non-clinical study that involved several components:

• Software Verification and Validation Testing: Conducted in compliance with FDA guidance for software in medical devices, ISO 14971:2007 (risk management), "General Principles of Software Validation," and "Guidance for Content of Premarket Submissions for Management of Cybersecurity in Medical Devices."
• Performance Testing with Pulse Oximeter Tester: This test specifically assessed the software's ability to recognize pulse and desaturation events.
• Retrospective Comparative Analysis: Data from sleep studies acquired with a different device (Advanced Brain Monitoring X8/PSG2) were analyzed independently by both the PoSAS software and the reference Sleep Profiler software (K153412). The goal was to demonstrate agreement and equivalence of SpO2 and pulse metrics between the two software applications.

Detailed Study Information:

1. Sample Size used for the Test Set and the Data Provenance:

   • Sample Size: Not explicitly stated. The document mentions "Retrospective data from sleep studies acquired with the Advanced Brain Monitoring X8/PSG2 device." It does not provide the number of patients or studies included in this retrospective test set.
   • Data Provenance: The data was retrospective and acquired using the Advanced Brain Monitoring X8/PSG2 device (K152040). The country of origin for the data is not specified.

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

   • The document does not explicitly state that human experts established the ground truth for the test set in the comparative analysis between PoSAS and Sleep Profiler. Instead, the Sleep Profiler software itself (a cleared reference device) served as the "ground truth" or reference for comparison against PoSAS for SpO2 and pulse metrics.
   • For the performance testing using the Pulse Oximeter Tester, the "ground truth" would be the known, programmed patterns of SpO2 and pulse changes generated by the tester.

3. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set:

   • No adjudication method involving human experts is described for the test set used in the comparative analysis between PoSAS and Sleep Profiler. The comparison was directly between the outputs of the two software programs.

4. 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:

   • No, an MRMC comparative effectiveness study was not done. The PoSAS software is a standalone desktop application for report generation and analysis of recorded data. It does not describe a human-in-the-loop scenario or evaluate the improvement of human readers with AI assistance.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

   • Yes, a standalone performance assessment was conducted. The entire performance testing and retrospective comparative analysis described (comparing PoSAS to a Pulse Oximeter Tester and to Sleep Profiler software) evaluates the algorithms and software without explicit human intervention in the interpretation process of those specific tests. The intended use states the report "requires clinician interpretation of the results; it does not suggest a course of treatment or generate a diagnosis," indicating that while the software is standalone in its analysis, clinical interpretation remains a human task.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • For SpO2 and pulse metrics: The "ground truth" for demonstrating equivalence was the algorithms and output of the legally marketed reference device software, Sleep Profiler (K153412).
   • For recognition of pulse events and 3%/4% desaturation events: The "ground truth" was the known, generated patterns from a Pulse Oximeter Tester.
   • For Night Shift data (e.g., position, snoring metrics): The "ground truth" for demonstrating equivalence was the Night Shift software (K140190), which provides "identical" processing and reporting for these metrics.

7. The sample size for the training set:

   • The document does not provide information on a training set or its sample size. This is common for software systems that replicate or combine existing, cleared algorithms rather than developing new predictive models requiring extensive supervised learning. The PoSAS software primarily harmonizes and displays data, and uses algorithms "identical" or "similar" to previously cleared devices.

8. How the ground truth for the training set was established:

   • As no training set is mentioned, no information on establishing its ground truth is provided.

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The Nonin Model 3100 WristOx TM Pulse Oximeter is a small, wrist-worn device indicated for use is measuring, displaying and storing functional oxygen saturation of arterial hemoglobin (Sp02) and pulse rate. It may be used for spot-checking and/or data collection and recording of adult and pediatric patients during both no motion and motion conditions, and for patients who are well or poorly perfused. The intended use environments are hospitals, medical facilities, ambulatory, subacute, sleep study environments, and mobile units.

Model 3150 is a wrist-worn device with memory for patient data collection and Bluetooth wireless communication capability. The device is a simple-touse pulse oximeter for both long and short term measurements. The device can be integrated into a telemedicine system, interfaced to other health data collection systems through the wireless connection, or used with Nonin's nVISION® Data Management Software K033307. The basic functions of the Model 3150 with an external sensor will be: ■ Measurement of SpO2 and pulse rate, . Motion artifact detection, . Collection and storage of data, and Communication via a wired connection and / or Bluetooth wireless connection.

The provided 510(k) summary focuses primarily on establishing substantial equivalence for the Nonin Model 3150 WristOx2 Pulse Oximeter to its predicate device, the Nonin Model 3100 WristOx. It highlights functional and safety testing but does not provide detailed acceptance criteria and performance data in the structured format requested for device accuracy.

Therefore, many of the requested points about detailed study design, sample sizes, ground truth establishment, expert qualifications, and MRMC studies are not present in this document. Pulse oximeter submissions typically rely on human desaturation studies to assess accuracy, but the specifics of such a study are not detailed here.

Here's a breakdown of what can be extracted and what is missing:

Acceptance Criteria and Device Performance

The document states that the device successfully underwent testing to demonstrate it meets the requirements of ISO 9919:2005 Clause 50 Accuracy of Operating Data. While it states the device meets these requirements, the specific numerical acceptance criteria and the precise reported device performance (e.g., RMS error for SpO2) are not provided in this summary.

Study Information

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not specified in the provided text.
   • Data Provenance: Not specified regarding country of origin or whether the data was retrospective or prospective. Pulse oximeter accuracy studies are typically prospective human desaturation studies.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not specified. For pulse oximetry, the ground truth is typically established by co-oximetry readings from arterial blood samples, which are laboratory measurements rather than expert interpretations.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not applicable as the ground truth for pulse oximetry accuracy studies is typically objective laboratory measurement (co-oximetry) rather than expert consensus requiring adjudication.

4. 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 a pulse oximeter, not an AI-assisted diagnostic imaging device requiring MRMC studies for human reader performance. Its primary function is direct physiological measurement.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

   • Yes, for pulse oximeters, the primary accuracy testing is a standalone assessment of the device's ability to measure SpO2 and pulse rate against a reference standard (co-oximetry). The mention of "clinical testing" and meeting "ISO 9919:2005 Clause 50 Accuracy of Operating Data" refers to this standalone performance.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • The typical ground truth for pulse oximeter accuracy is co-oximetry readings from arterial blood samples. This is strongly implied by the reference to ISO 9919:2005 Clause 50, which details accuracy testing methods using induced hypoxemia and arterial blood gas analysis.

7. The sample size for the training set:

   • Not applicable and not specified. Pulse oximeters are typically developed and calibrated rather than "trained" in the machine learning sense. The device's algorithms are based on physiological principles and empirical spectral data, not a machine learning training set in the way AI algorithms are.

8. How the ground truth for the training set was established:

   • Not applicable as there isn't a "training set" in the machine learning context for this type of device. Calibration and algorithm development would rely on controlled experimental data with known oxygen saturation levels and corresponding optical measurements.

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The Nonin® Model 3100 WristOx® Pulse Oximeter is a small wrist-worn device indicated for use in measuring, displaying, and storing functional oxygen saturation of arterial hemoglobin (SpO2) and pulse rate of adult and pediatric patients. It is intended for spot-checking and / or data collection and recording of patients during both no motion and motion conditions, and for patients who are well or poorly perfused.

The Nonin Model 3100 WristOx® is a small wrist-worn pulse oximeter. It has an electro-optical sensor that determines the light absorption of functional arterial hemoglobin. It is used to spot check patients, or it can be used to provide continuous data collection and recording of patients in situations where alarms are not required. The device turns on automatically when the sensor is placed on the finger and turns off when the finger is removed. It may be used with Nonin's 8000AA-WO articulated finger clip sensor or the 8000J-WO flex sensor. The WristOx is also memory capable in conjunction with nVision® data management software (K033307). It is capable of storing up to 33 hours of SpO2 and pulse rate data.

The provided text describes the Nonin Model 3100 WristOx® Pulse Oximeter, which was found substantially equivalent to a predicate device. However, the document does not contain explicit acceptance criteria or a detailed study report that proves the device meets specific performance criteria.

The 510(k) summary (K052829) states that the device "has successfully undergone both bench and clinical testing in order to demonstrate that it has appropriate functional characteristics and is substantially equivalent to the predicate device." It also claims the device is "identical in everyway to the predicate device not with standing the changes specified in the labeling."

Without a specific study outlining performance metrics, acceptance criteria, and detailed methodologies, the requested information cannot be fully extracted. The submission focuses on demonstrating substantial equivalence, implying that its performance is comparable to the predicate device (Model 3100 WristOx® (K030668)), which would have previously met its own established criteria.

Therefore, for the current document, I can only report what is explicitly stated or implied about the testing, and cannot provide the detailed acceptance criteria and study particulars for the new device's performance.

Here's an attempt to answer based on the provided text, with many fields indicating "Not explicitly stated":

Acceptance Criteria and Device Performance

Detailed Study Information:

1. Sample size used for the test set and the data provenance: Not explicitly stated. The document refers to "clinical testing" but does not provide details on sample size, participant demographics, or data provenance (e.g., country of origin, retrospective/prospective nature).

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not explicitly stated. The document does not describe the establishment of a ground truth for a test set, nor does it mention any expert involvement for this purpose.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not explicitly stated. No adjudication method is mentioned in the provided text.

4. 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 a pulse oximeter, not an AI-assisted diagnostic tool for human readers. As such, an MRMC study comparing human readers with and without AI assistance is not relevant to this device's function.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The device itself is a standalone measurement tool (a pulse oximeter). Clinical and bench testing would have evaluated its performance independently. No human-in-the-loop performance evaluation in the context of an "algorithm" is applicable here. The device measures, displays, and stores SpO2 and pulse rate.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not explicitly stated. For a pulse oximeter, the ground truth for SpO2 measurement would typically involve a co-oximeter or blood gas analysis, but this is not detailed in the provided summary.

7. The sample size for the training set: Not applicable and not explicitly stated. This device is not an AI/machine learning model that typically requires a "training set." Its functionality is based on electro-optical sensing principles.

8. How the ground truth for the training set was established: Not applicable and not explicitly stated. See point 7.

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