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The SOMNOscreen is a non-life-supporting portable physiological signal recording device intended to be used for testing adult patients suspected of having sleep-related breathing disorders.

The SOMNOscreen is indicated for use in the recording, displaying, monitoring, and storage of biophysical parameters for the purpose of assisting in the diagnosis of Neurological and Sleep Disorders.

The SOMNOscreen is a portable physiological signal recording system intended to be used to record, display, monitor, print and store biophysical events to aid in the diagnosis of neurologic and sleep disorders. The device is intended to be prescribed for use by a physician in the office, sleep laboratory or patient's home.

This device is NOT designed to be used in a Life Support situation. This device is not designed for use on patients with cardiac pacemakers.

The system provides up to 28 channels for data acquisition; 10 AC Channels, 8 Referential and 2 Differential, 11 Respiratory and AUX Channels, 7 Internal Channels (SPO2, Pulse Rate, Plethysmogram, Body Position, Light, Patient Marker, Thorax/Abdominal Respiratory Effort)

The SOMNOscreen is available in 6 different configurations. All configurations include a Compact Flash Card and Reader, Li ION Batteries, (2000mAh) with 1 x Battery Charger, a Carry Bag for housing the SOMNOscreen and Sensors, Instruction Manuals and the DOMINO software for Initialization, Data Transfer and Analysis.

Here's a summary of the acceptance criteria and study details for the Somnomedics SOMNOscreen based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state quantitative "acceptance criteria" for the SOMNOscreen's clinical performance. Instead, it states that the device's clinical performance was found to be "equivalent to the predicate Sleepscreen and the manual scored Polysomnography." Therefore, the reported device performance is its equivalence to these established methods.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: 25 patients
• Data Provenance: Not explicitly stated, but the applicant (Somnomedics GmbH & Co.KG) is based in Kist, Germany. It is common for clinical studies associated with German manufacturers to originate from Germany or other European countries; however, this is not confirmed in the document. The study evaluates the device "when used as intended in the targeted patient population," implying prospective data collection for this validation.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

The document states that the comparison was made against "manual scored Polysomnography." This heavily implies that sleep experts (e.g., polysomnography technologists, sleep physicians) were involved in scoring the PSG data, which would serve as the ground truth. However:

• Number of experts: Not specified.
• Qualifications of experts: Not specified, but generally, manual PSG scoring is performed by registered polysomnographic technologists (RPSGTs) and interpreted by board-certified sleep physicians.

4. Adjudication Method for the Test Set

The document does not describe an explicit adjudication method (e.g., 2+1, 3+1). The "manual scored Polysomnography" itself is considered the reference standard, and deviations from this standard for the SOMNOscreen's output would be assessed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No. The study described compares the SOMNOscreen to a predicate device and manual PSG. It does not appear to be an MRMC study designed to evaluate the improvement in human reader performance with or without AI assistance. The SOMNOscreen, as described, is a physiological signal recording system, not an AI-assisted diagnostic tool in the sense of providing automated interpretations that would then be reviewed by human readers.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, in essence. The study evaluates the "clinical performance of the SOMNOscreen" itself against predicate devices and manual PSG. While a human would still interpret the output from the SOMNOscreen, the study aims to validate the device's ability to record, display, monitor, print, and store biophysical events reliably, standalone from a human "AI assistance" workflow. The device does not appear to have an AI component for automated interpretation that would undergo a separate standalone performance evaluation.

7. The Type of Ground Truth Used

The ground truth used was manual scored Polysomnography (PSG), which is typically considered the gold standard for sleep disorder diagnosis.

8. The Sample Size for the Training Set

The document does not mention a training set or any details about an algorithm that would require one. The SOMNOscreen is described as a physiological signal recording system, implying it captures raw physiological data rather than employing a machine learning algorithm that would need training.

9. How the Ground Truth for the Training Set Was Established

Since no training set is mentioned and the device appears to be a data acquisition system rather than an AI/ML-based interpretive algorithm, this information is not applicable and not provided in the document.

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Innocor is indicated for the determination of a number of hemodynamic parameters. Cardiac Output (CO) is the principal measured parameter. Utilizing inert gas rebreathing, Innocor measures the relative levels of two inhaled gases of differing blood solubility over approximately 3-4 respirations and calculates pulmonary blood flow (PBF). In the absence of a significant intrapulmonary shunt (arterial oxygen saturation ≤ 95% as measured by a pulse oximeter incorporated in the Innocor), PBF is equal to CC. As an optional accessory, Innocor includes a noninvasive Blood Pressure (NIBP) monitoring system. This option provides systolic, diastolic and mean arterial pressures. With the NIBP option, Innocor provides values for the following measured and calculated hemodynamic parameters: Cardiac Output, Arterial Oxygen Saturation, Heart Rate, Stroke Volume, Lung Volume, Cardiac Index, troke Index, Blood Pressures (Systolic, Diastolic, Mean Arterial), ystemic Vascular Resistance, Systemic Vascular Resistance Index.

Innocor is a compact point-of-care device intended to be used for measurement of a) cardiac output (CO) utilizing inert gas rebreathing (IGR) technology and b) other hemodynamic parameters. Two Models will be made available initially in the U.S: Innocor, Innocor with NIBP option. With the NIBP module option, the device will provide values for the hemodynamic parameters included in the Indications for Use below.

The provided text for K051907 describes the Innocor device and its substantial equivalence to predicate devices, focusing on its ability to measure cardiac output and other hemodynamic parameters. However, it does not contain the detailed information required to fill out the table and answer the study-related questions about acceptance criteria, device performance, sample sizes, ground truth establishment, or expert involvement in a clinical study for the Innocor device itself.

The document primarily focuses on establishing substantial equivalence for regulatory purposes by comparing the Innocor's intended use and components (inert gas rebreathing for cardiac output, pulse oximeter, NIBP) to already cleared predicate devices. It lists the predicate devices and the parameters Innocor measures but does not include a specific study design with acceptance criteria and performance metrics for the Innocor.

Therefore, I cannot provide the requested table and detailed study information based solely on the provided text. The output below reflects the information that is not present in the provided document.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for Test Set and Data Provenance

• Sample Size: Not specified in the provided text.
• Data Provenance: Not specified in the provided text (e.g., country of origin, retrospective or prospective).

3. Number of Experts and Qualifications for Ground Truth Establishment

• Number of Experts: Not specified in the provided text.
• Qualifications of Experts: Not specified in the provided text.

4. Adjudication Method for Test Set

• Adjudication Method: Not specified in the provided text.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? Not specified in the provided text. The document focuses on device equivalence, not human reader performance with or without AI assistance.
• Effect Size of Human Reader Improvement: Not applicable, as an MRMC study is not mentioned.

6. Standalone (Algorithm Only) Performance Study

• Was a standalone study done? The document describes the device's functionality for measuring various parameters. While it performs these measurements, a formal "standalone study" with specific performance metrics against an established ground truth (as typically reported for AI/algorithm performance) is not detailed. The text implies a comparison to methods like Thermodilution and Direct Fick for cardiac output, but not in the format of a standalone performance study with defined criteria and results.

7. Type of Ground Truth Used

• Type of Ground Truth: For the cardiac output measurement, the document mentions substantial equivalence to "Thermodilution Cardiac Output Computers" and the "Direct Fick Method preamendment calculation method." These predicate methods serve as a comparative basis for the Innocor's measurements, implying they represent the "truth" against which the new device is compared. However, a specific ground truth dataset and its source (e.g., pathology, outcomes data, expert consensus on a test set) for Innocor's own validation is not described.

8. Sample Size for Training Set

• Sample Size: Not applicable. The Innocor is described as a device utilizing inert gas rebreathing technology and other measurement components, not an AI/machine learning algorithm that requires a training set in the conventional sense.

9. How Ground Truth for Training Set Was Established

• How Established: Not applicable, as there is no mention of a training set for an AI/machine learning component.

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The AccuO2 System, which provides oxygen therapy on demand, based on continuous, non-invasive monitoring of oxygen saturation, is indicated for home use by adult Chronic Obstructive Pulmonary Disease (COPD) patients who are prescribed low-flow (0-3 L/min) supplemental oxygen via nasal cannula and USP portable oxygen.

The AccuO2 System is a portable, battery-operated system consisting of a proprietary demand oxygen delivery module combined with a commercially available pulse oximeter module. The system is used with a standard nasal cannula and USP portable oxygen. The proprietary software in the AccuO2 demand oxygen delivery module is designed to deliver oxygen on inhalation only and to maintain the patients at an oxygen saturation (SpO2) level of 90% while conserving oxygen.

Here's a breakdown of the acceptance criteria and study information for the MITI Corp. AccuO2 System, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

2. Sample size for the test set and data provenance:

• Sample Size: Not explicitly stated in the provided abstract. The text only mentions "clinical testing was conducted to evaluate the ability to maintain patients at or above 90% SpO2 as designed."
• Data Provenance: Not specified (e.g., country of origin). The studies appear to be prospective given they are evaluating the device's performance, but this is not explicitly stated as retrospective or prospective.

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

• Not specified. The document refers to the device maintaining SpO2 levels and not increasing hypoxic states, which are physiological measurements, not interpretationsrequiring expert consensus in the same way an imaging study would.

4. Adjudication method for the test set:

• Not applicable/Not specified. The assessment criteria are objective physiological measurements (SpO2 levels, detection of breaths, valve actuation, oxygen amount calculation) rather than subjective interpretations requiring adjudication.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

• No. This is not an AI-assisted diagnostic imaging device that would typically involve human readers. The AccuO2 system is a demand oxygen delivery system with an integrated pulse oximeter. The clinical studies compare its performance against existing devices, not human interpretation with/without AI assistance.

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

• Yes, implicitly. The functional testing and clinical studies described evaluate the device's performance directly, independent of continuous human intervention in its core functioning (e.g., detecting breaths, actuating valves, maintaining SpO2). The system's purpose is to automate oxygen delivery based on physiological inputs.

7. The type of ground truth used:

• Physiological measurements and objective device performance. The ground truth for the pulse oximeter's accuracy would likely be against a co-oximeter or arterial blood gas (ABG) analysis, though this is not explicitly detailed. For breath detection and valve actuation, the ground truth would be based on direct measurement of those events. For SpO2 maintenance, the ground truth is the patient's actual oxygen saturation as measured by the device itself (and likely validated against a reference standard during development/testing).

8. The sample size for the training set:

• Not applicable/Not specified. This device predates the widespread use of deep learning and often doesn't involve a "training set" in the modern machine learning sense, as it relies on proprietary software for control rather than a learned model from a large dataset. The software logic would be developed and verified, not "trained."

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

• Not applicable/Not specified, for the reasons mentioned above. The "ground truth" for the device's operational logic would have been established through engineering design, physiological principles, and functional testing to ensure the software correctly implements the intended control strategy.

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