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The SNAP Diagnostics SAM Model 9-10000 device is intended to record airflow, breathing effort and body position, and is indicated for use as an aid for diagnostic evaluation of patients for apnea and snoring.

The SNAP Diagnostics SAM Model 9-10000 is not intended as a substitute for full polysomnography when additional parameters such as sleep stages or EEG activity are required.

The target population consists of patients who are suspected of apnea and/or complain about snoring. The majority of the test procedures will take place at the patient's home, although some may take place in a sleep laboratory. Both pediatric and adult patients may be tested.

Use of this device must be under the direct supervision of a qualified adult (parent or guardian) or health care practitioner trained in the use of the Snap Diagnostics SAM Model 9-10000 device.

The Snap Diagnostics SAM Model 9-10000 is an upgraded version of the Snap Diagnostics Model 8 (K110064, the predicate device) where the only area of change is the data logger component of the Model 8 sleep apnea home sleep test (HST) and recording system. The SAM Model 9-10000 is meant to be used with previously-cleared components of the predicate device including Snap Model 8 accessories and Snap Model 8 analysis software.

The Snap Diagnostics SAM Model 9-10000 differs from the K110064 predicate in the implementation of built in piezo electric effort and body position sensors rather than using external sensors, incorporation of the device electronics into the belt-worn effort sensor enclosure, and support for external wrist worn oximeters (Wellue CheckMe Oximeter cleared under K191088 and Nonin 3150 Oximeter cleared under K102350).

The Snap Diagnostics SAM Model 9-10000 interfaces with the K110064 predicate analysis software only by way of exporting files using the USB port, and this occurs at Snap Diagnostics or a lab location. The SAM Model 9-10000 cannot transfer files over the internet. These files are identical in format to the files output by the predicate data logger. The K110064 predicate analysis software is unchanged and is not the subject of this 510(k).

Just like the Snap Diagnostics Model 8 predicate device, the Snap Diagnostics SAM Model 9-10000 is designed to be used in the patient's home. A SAM Model 9-10000 with a fully charged battery is delivered to the patient along with accessories. The patient uses the data logger for one or more overnight recordings, then returns the device and accessories to Snap Diagnostics or the lab. A technician retrieves the data from the SAM Model 9-10000 using the USB port. The previously-cleared analysis software (K110064) is then used by a supervising technician to review the data and generate a home sleep test (HST) report. This HST report is sent by Snap Diagnostics to the patient's clinical team, who interpret the HST in the context of other relevant clinical data. Just like for the predicate device, Snap Diagnostics or a lab wipes the SAM Model 9-10000 of all data, cleans, inspects, and recharges it, and then provides the SAM Model 9-10000 to the next patient for use.

The Snap Diagnostics SAM Model 9-10000 is capable of logging the heart rate and blood oxygenation data from external wrist worn oximeters (Wellue CheckMe Oximeter cleared under K191088 and Nonin 3150 Oximeter cleared under K102350), but does not itself measure or otherwise manipulate those data.

Just like the predicate, the Snap Diagnostics SAM Model 9-10000 itself measures sound/airflow, respiratory effort, and acceleration/position.

The Snap Diagnostics SAM Model 9-10000 data logger does not perform sleep scoring or any other diagnostic, analysis, visualization, or data transform function, nor does the device have the capacity for alarms or the triggering of actions or therapies.

The provided text does not contain information about "acceptance criteria and the study that proves the device meets the acceptance criteria". The document primarily focuses on demonstrating substantial equivalence to a predicate device (Snap Model 8, K110064) based on regulatory parameters, indications for use, and technological characteristics.

Specifically, the document states:

• "Comparative performance evaluations between the Snap SAM Model 9-10000 and the predicate device, the Snap Model 8 (K110064), demonstrated that the two devices are substantially equivalent in performance."
• "Bench testing confirmed that the device met design requirements and the requirements of applicable standards."

However, it does not provide:

1. A table of specific acceptance criteria (e.g., accuracy, sensitivity, specificity for diagnostic parameters) or reported device performance against such criteria.
2. Details about sample size, data provenance, number or qualifications of experts, or adjudication methods for any test set that would establish diagnostic performance or ground truth.
3. Information on multi-reader multi-case (MRMC) comparative effectiveness studies or effect sizes.
4. Specific standalone algorithm performance.
5. The type of ground truth used to establish performance.
6. The sample size for a training set or how ground truth was established for it.

The document lists various standards that the device underwent comprehensive performance testing against (e.g., IEC 60601-1, IEC 60601-1-11, IEC 60601-1-2, ISO 10993-1, IEC 62133-2), which typically cover safety, electromagnetic compatibility, and biological compatibility, but not diagnostic performance metrics for apnea and snoring.

Therefore, I cannot fulfill the request to provide the detailed information about acceptance criteria and the study proving the device meets them based on the provided text.

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Aurora is a Software as a Medical Device (SaMD) that establishes sleep quality. Aurora automatically analyzes, displays, and summarizes Photoplethysmogram (PPG) data collected during sleep using compatible devices. Aurora is intended for use by and by order of a healthcare professional to aid in the diagnosis of sleep disorders including sleep apnea in adults.

The Aurora output, including automatically detected respiratory events and parameters, may be displayed and edited by a qualified healthcare professional. The Aurora output is not intended to be interpreted or clinical action taken without consultation of a qualified healthcare professional.

Aurora is not intended for use with polysomnography devices.

Aurora is a Class II Software as a Medical Device (SaMD), intended to aid in the evaluation of sleep disorders, where it may inform or drive clinical management. Aurora is a software application that is indicated for use on a general-purpose computing platform. It is a cloud-based software-as-a-medicaldevice (SaMD) with a user interface that runs in a web browser.

Aurora automatically analyzes and displays photoplethsmography (PPG) signal data including SPO2 and pulse/heart rate only from compatible FDA-cleared medical purpose pulse oximeters that meet Aurora's data acquisition requirements for sampling rate, digital resolution, measurement range, and accuracy range.

Following upload of a compatible PPG study to the cloud software, the algorithm functions by verifying minimum signal quality, study length, and technical adequacy requirements, preprocessing the data including normalization, digital filtration, and artifact detection/rejection procedures, applying machine learning algorithms including multiple deep neural network machine learning models, statistical signal processing analyses including time-domain and time-frequency domain analyses over multiple time and resolution scales, and other analyses output a detected set of events and derived signals for the PPG study that are post-processed and logically filtered according to algorithm rules based on the American Academy of Sleep Medicine (AASM) recommended scoring event, desaturation, and association rules. Aurora algorithm outputs, including scored respiratory events, sleep stages, Aurora Apnea-Hypopnea Index (eAHI), Total Sleep Time (TST), Sleep Efficiency (SE), Sleep Latency (SL), Wake After Sleep Onset (WASO), and Oxygen Desaturation Events Index (ODI) measures, are stored and made available for display, editing, and review in Aurora by qualified healthcare professionals.

Aurora reports results of the automated data analysis based on AASM guidelines, including the Aurora output Apnea-Hypopnea Index (eAHI) and total sleep time (TST). The algorithm outputs are graphical and numerical displays and reports of sleep latency, sleep quality, and sleep pathologies including sleep disordered breathing. The Aurora displays and reports are for the order of physicians, trained technicians, or other healthcare professionals to evaluate sleep disorders where it may inform or drive clinical management taking into consideration other factors that normally are considered for clinical management of sleep disorders for adults.

The clinician can view raw data for interpretation, edit events, write clinical notes, and customize sleep reports for the patient.

Aurora output is not intended to be interpreted or clinical action taken without consultation of a qualified healthcare professional.

The document provides detailed information about the performance evaluation of the Aurora device, a Software as a Medical Device (SaMD) intended to aid in the diagnosis of sleep disorders.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for Aurora are implied by the performance metrics reported, demonstrating its accuracy in detecting Apnea Hypopnea Index (eAHI) and performing sleep staging against polyomnography (PSG) ground truth. While explicit numerical "acceptance criteria" tables are not provided, the reported sensitivity, specificity, and regression/Bland-Altman statistics serve as the evidence of meeting performance expectations for substantial equivalence.

Table of Performance Data (Implied Acceptance Criteria)

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

• Test Set Sample Size:
  • For eAHI performance (sensitivity/specificity): 158 adult patients.
  • For Sleep Staging:
    • Wake: 52,622 epochs
    • Light NREM: 69,438 epochs
    • Deep NREM: 10,195 epochs
    • REM: 14,459 epochs
• Data Provenance: The document does not explicitly state the country of origin but implies clinical settings where PSG (Polysomnography) and HSAT (Home Sleep Apnea Test) recordings are collected. The study involved simultaneous PSG and HSAT recordings, suggesting a prospective collection of data for testing purposes to facilitate direct comparison.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Three registered polysomnographic technologists were used for manual scoring, and one board-certified sleep physician reviewed each PSG.
• Qualifications of Experts:
  • Scorers: Registered polysomnographic technologists.
  • Reviewer/Confirmer: Board-certified sleep physician.

4. Adjudication Method for the Test Set

• Adjudication Method: A 2+1 consensus method. For an event to be officially scored or reported, a consensus of at least two-thirds among the three scorers was required. Additionally, each PSG was reviewed by a board-certified sleep physician to provide clinical confirmation of scoring and technical adequacy, serving as a final adjudication layer.

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

• The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to assess how much human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the Aurora algorithm against expert-scored ground truth. The device output may be displayed and edited by a qualified healthcare professional, suggesting a human-in-the-loop workflow, but the study described does not quantify the effect of AI assistance on human reader performance.

6. Standalone Performance Study

• Yes, a standalone performance study was done. The reported sensitivity, specificity, Deming regression, and Bland-Altman analyses directly evaluate the algorithm's performance (Aurora) against the expert-scored PSG as ground truth, without a human in the loop for the performance metrics themselves.

7. Type of Ground Truth Used

• The type of ground truth used was expert consensus from manual scoring of Polysomnography (PSG) data. Specifically, PSG recordings were manually scored by three registered polysomnographic technologists using guidelines following the 3% desaturation guidance. This was further reviewed and confirmed by a board-certified sleep physician.

8. Sample Size for the Training Set

• The document does not specify the sample size for the training set. The provided details pertain exclusively to the test set used for performance validation.

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

• The document does not specify how the ground truth for the training set was established. Information regarding the training data, its collection, or annotation methods is not included in this summary.

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