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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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Morgan Scientific's ComPAS2 is a software application intended to be used to compatible Morgan Scientific or thirdparty devices to acquire, analyze, view, store, export, and print the device outputs including measurements of flow, volume, pressure, and gas concentrations. The product is designed for use on adults and pediatrics 4 years and older, in a variety of healthcare environments such as, but not limited to, primary care, hospitals, and research health centers under the supervision of a healthcare provider.

ComPAS2 is a software application designed to provide a secure PC based medical device for creating, adding/recalling subjects, and performing cardio-pulmonary function testing on those subjects. ComPAS2 will interface and link to compatible Morgan Scientific and third-party devices to read, analyze, and display their output to allow the information to be retained with the subject. Current compatible approved devices: TransAir (K953990), SpiroAir (K042595), Body Plethysmograph (K022636), WristOx2 (K102350), tremoFlo (K170185), Pneumotrac (K142812), Micro (K160253), Model 9100 PFT/D1CO (K221030). Data can be reported directly to a printer or communicated with hospital information systems/electronic medical records. All data are preserved in an SQL database, with key sub-systems of ComPAS2 interacting with the database through an API (Application Program Interface).

ComPAS2 is designed to operate with compatible cardio-pulmonary function testing hardware by manufacturers offering the capability to measure key pulmonary functions including, but not limited to: static and dynamic spirometry, bronchial challenge, maximum voluntary ventilation, respiratory muscle strength, cough peak flow, lung volume sub-divisions (such as but not limited to helium dilution, nitrogen washout and plethysmography), single breath diffusion, airway resistance, distribution with lung clearance index closing volume. Other features include: a task manager to manage patient data for reporting; manual entry to input additional information; and historical data review to analyze data for trending and reporting.

The ComPAS2 device, a software application for diagnostic spirometry, was found to be substantially equivalent to its predicate device, ComPAS2 v2019.1.0 (K190568). The primary "study" proving this substantial equivalence was non-clinical performance testing of the software.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state acceptance criteria in a typical quantitative pass/fail format for each performance metric, but rather highlights that performance testing demonstrated that the subject device met its acceptance criteria. The "reported device performance" is implied to be equivalent to the predicate device's performance, as the core functionality and technical characteristics remain largely the same, and the software was validated against the predicate's results.

However, based on the comparison table and the general description, we can infer some performance aspects:

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

• Test Set Sample Size: Not explicitly stated as a number of patients or cases. The testing involved "developing test cases and test runs for the performance of end-to-end testing with both biological and mechanical controls." This suggests a series of functional tests and expected outcomes rather than a traditional patient-based clinical study with a specific sample size.
• Data Provenance: The document does not specify the country of origin for any data used in this non-clinical testing. The nature of the testing (bench testing, software validation) suggests it's primarily synthetic or controlled data generated internally, or data from mechanical/biological controls (e.g., spirometer calibration syringes, simulated lung models). The testing was against "existing results from ComPAS2 v2019.1.0," indicating a retrospective comparison to previously established performance.

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

Not applicable. The ground truth for this software validation was established by comparing the results of the ComPAS2 v2022.1.0 software to the "existing results from ComPAS2 v2019.1.0," the predicate device, and ensuring compliance with recognized standards (ATS/ERS guidelines). Expertise would have been in the form of engineers, quality assurance personnel, and potentially pulmonologists for clinical interpretation of the standards and expected outputs, but the document does not specify a panel of experts for "ground truth" establishment in the sense of a diagnostic interpretation study.

4. Adjudication Method for the Test Set

Not applicable. This was a software verification and validation study, not a clinical study requiring adjudication of diagnostic outcomes. Validation involved ensuring consistency and accuracy of the new software's outputs against the predicate and established standards.

5. 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 MRMC comparative effectiveness study was done or described. The device is a software application intended to acquire, analyze, view, store, export, and print device outputs, not to provide AI-assisted diagnoses that impact human reader performance.

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

Yes, the performance testing described is a standalone evaluation of the ComPAS2 software application. The "Bench" section explicitly states "software testing activities" and "system level testing to ensure that the product is capable of meeting the intended use." This indicates the algorithm's performance (i.e., the software's ability to process and display data) was tested independently. The software interfaces with hardware devices that generate the raw data, but its own function of processing and presenting that data was evaluated as described.

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

The ground truth used for the validation of ComPAS2 v2022.1.0 was primarily:

• Existing results from the predicate device (ComPAS2 v2019.1.0): The new software's outputs were compared against the established, cleared performance of the previous version.
• Current standards for Lung Function Testing: Compliance with standards issued by the American Thoracic Society (ATS) and European Respiratory Society (ERS) (e.g., Laszlo, 2006; Macintyre et al., 2005; Miller, Crapo, Hankinson, et al., 2005; Pellegrino, et al., 2005; Wanger et al., 2005; ERS/ATS 2017 & 2019 standards).

8. The Sample Size for the Training Set

Not applicable. This is a software update to an existing device, and the testing described is primarily verification and validation against established standards and the predicate's performance. There is no mention of a machine learning or AI component requiring a "training set" in the context of this submission. The software performs calculations and displays data based on established algorithms in pulmonary function testing.

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

Not applicable, as there is no mention of a training set for machine learning or AI.

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Belun Ring BLR-100C is a non-invasive and stand-alone pulse oximeter, intended to be used for spot-checking and/or data collection and recording of oxygen saturation of arterial hemoglobin (SpO2) and the pulse rate of adult patients through index finger in hospital and home environment. It is not intended for single-use and out-of-hospital transport use.

The proposed device Belun Ring BLR-100C is a non-invasive and stand-alone pulse oximeter, which can detect, display and transfer the measured oxygen saturation of arterial hemoglobin (SpO2) and the pulse rate in hospital and home environment.

The proposed device consists of two parts: A Ring and a Cradle.

The Ring, which is of a smooth and a light design and is easy to be worn and taken off, is intended to be worn on the base of the index finger. It provides comfortable and accurate measurements with the Cradle in the spot-checking mode or without the Cradle in the recording mode. The Cradle collects data from the Ring and translates the data into text and graph which can be easily understood by the user. It also exports the collected data via USB port to a host such as computer or mobile equipment for recording data transfer and review. They usually outside of patient environment, which is remote from the patient. There is no wireless function in this device.

Using spectrophotometric methodology, the proposed device measures oxygen saturation by illuminating the skin and measuring changes in the light absorption of oxygenated (oxyhemoglobin) and deoxygenated blood (reduced hemoglobin) using light of two wavelengths: red and infrared. The ratio of absorbance at these wavelengths is calculated and calibrated against direct measurements of arterial oxygen saturation (SaO2) to establish the pulse oximeter's measurement of functional oxygen saturation of arterial hemoglobin (SpO2). The sensor of the Ring should be placed on the palmar side of the proximal phalanx of the index finger and along the radial artery.

The system is using a customized dual CPU design to realize the functions. It consists of two main platforms: The Ring is responsible for signal pre-conditioning, data post-processing (SPO2/PR algorithm), parameters calculation and sensor interfacing, while the Cradle takes care of the user interface including a display for output and a button for input.

The system includes two embedded software, namely the Ring firmware and the Cradle firmware. It is modularized and provides high stability. The software systems work in conjunction with the Ring and the Cradle. The two platforms (Ring and Cradle) are connected via "Connectivity software module". The communication protocol is proprietary which provides a reliable and fast communication.

The provided document is a 510(k) premarket notification for the Belun Ring BLR-100C, a pulse oximeter. It primarily focuses on demonstrating substantial equivalence to a predicate device (Belun Ring BLR-100) and a reference device (Nonin 3150 WristOx2).

Here's an analysis to extract the requested information regarding acceptance criteria and the study that proves the device meets them:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present a "table of acceptance criteria" in a pass/fail format alongside reported device performance for the BLR-100C. Instead, it relies heavily on comparing the BLR-100C's specifications to its predicate device (BLR-100) and a reference device (Nonin 3150 WristOx2). The implicit acceptance criterion is that the BLR-100C's performance specifications are substantially equivalent to or within acceptable limits of the predicate/reference devices, and it meets relevant international standards.

For SpO2 and PR accuracy, which are key physiological measurements for an oximeter, the document lists specifications that can be interpreted as de facto acceptance criteria based on its comparison with predicate devices.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document primarily relies on non-clinical (bench) testing and the clinical study data from its predicate device (Belun Ring BLR-100, K180174).

• The text states: "BLR-100C is using the same PCB assembly (PCBA), materials for mechanical parts and bill-of-material as predicate device BLR-100 (K180174) except that the firmware, a resistor value on a PCB and an adhesive tape model have been changed such that "data collection and recording" function can be added in BLR-100C. Hence, the proposed device Belun Ring BLR-100C is verified in bench studies to meet the specifications fulfilled by the cleared predicate Belun Ring BLR-100 (K180174)."
• And: "Hence, the clinical study of predicate BLR-100 remains valid for BLR-100C."

Therefore, the sample size and data provenance for the clinical validation of the BLR-100C are those of the predicate device, BLR-100. This specific 510(k) document (K191417) does not provide details about the sample size, country of origin, or whether the predicate's study was retrospective or prospective. It simply states the clinical data for the BLR-100 is considered valid for the BLR-100C.

For the non-clinical bench tests performed on the BLR-100C itself, sample sizes are not explicitly mentioned, but these would typically involve a smaller number of devices to verify specific functionalities and meet technical standards.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the document. As the clinical validation relies on the predicate device's study, details about ground truth establishment, including the number and qualifications of experts, would be found in the 510(k) submission for Belun Ring BLR-100 (K180174), not in K191417.

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

This information is not provided in the document. Similar to point 3, details about the adjudication method for clinical ground truth would be found in the K180174 submission.

5. 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 MRMC study was done or reported as part of this 510(k). This device is a pulse oximeter, not an AI diagnostic imaging tool where MRMC studies are typically applicable. It provides direct physiological measurements (SpO2 and pulse rate) and does not involve "human readers" or "AI assistance" in the sense of image interpretation.

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

This device is a standalone oximeter in terms of its direct measurement capability. The "algorithm" for SpO2 and PR calculation is embedded within the device (Ring firmware). The performance assessment of the BLR-100C (and its predicate) against the reference method (e.g., co-oximetry, which would represent the "ground truth") intrinsically represents its standalone algorithmic performance. The document states it is for "spot-checking and/or data collection and recording."

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

For oximeters, the gold standard for measuring arterial oxygen saturation (SaO2) is typically laboratory co-oximetry performed on arterial blood samples. While not explicitly stated in this document, it is standard practice for pulse oximeter clinical studies to use co-oximetry as the ground truth. The document references ISO 80601-2-61:2011, which specifies requirements for pulse oximeters, and typically mandates such validation.

8. The sample size for the training set

This document describes a medical device, not a machine learning/AI algorithm that typically has a distinct "training set" and "test set." The "development" or "calibration" of such a device's algorithm would be part of its initial design and verification, which might involve a set of data, but it's not referred to as a "training set" in the machine learning sense here because this is a traditional, deterministic device. Details of any data used during the initial development/calibration of the BLR-100 or its core technology are not provided in this 510(k).

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

As per point 8, the concept of a "training set" in the context of an ML/AI model with ground truth establishment is not directly applicable to this traditional medical device. Any calibration or verification data used during the device's original development (for the predicate device BLR-100) would have likely used co-oximetry as a reference, but these details are not in this document.

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