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The MediByte™ is a portable sleep data recorder used to record physiological signals during sleep while the patient is either at home or in a clinical environment. The data is downloaded after the recording is completed and the assist software enables the trained human professional typically a Registered Sleep Technologist or Medical Doctor - to verify the results of the study and generate a report.

Target Population: Children and adult patients who are screened during sleep disorder studies.

Environment of Use: The majority of the screenings occur either in the home at in a clinical setting.

The MediByte™ is intended to be used only by or on the order of a physician.

The MediByte™ is a palm-sized recording device capable of acquiring and storing physiological signals from FDAcleared sensors and transmitting the physiological data to a computer through the Universal Serial Bus (USB) port.

The MediByte™ records up to 8 channels of physiological signals: either electromyogram (EMG), or electrocardiogram (EKG); as well as chest effort; abdominal effort; airflow pressure; snoring; body position; arterial oxygen saturation (SpO2); and pulse rate. The signals cannot be viewed in real time, but can be downloaded after collection for assisted analysis by a human professional trained in the analysis and reporting of sleep disorders medicine.

The MediByte™ is powered by one ½ AA battery and connects to a computer via the MediByte™ USB communication cable. The MediByte™ and sensors are both typically worn by the patient during the recording and all patient contact materials consist of latex-free biocompatible material.

The provided text describes the acceptance criteria and performance of the MediByte™ device, primarily through comparison to a predicate device, the MediPalm®.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: The document mentions "Analysis of overnight studies" and "Simulation tests" without specifying the exact number of patients or samples used for these tests. Therefore, the sample size for the test set is not explicitly stated.
• Data Provenance: The studies appear to be internal tests conducted by BRAEBON Medical Corporation to demonstrate equivalence. The document does not specify country of origin for the data (e.g., patient demographics from a specific country) or whether the data was retrospective or prospective clinical data. It primarily describes internal testing and simulations.

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

• The document implies that "Assisted analysis by a human professional trained in the analysis and reporting of sleep disorders medicine" for verifying results the software generates. However, it does not specify the number of experts used for establishing the ground truth of the test set, nor their specific qualifications (e.g., years of experience) beyond "trained human professional typically a Registered Sleep Technologist or Medical Doctor."

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

• The document does not specify any formal adjudication method (like 2+1 or 3+1) for establishing ground truth within the context of performance testing or a clinical study. It implies that a "trained human professional" verifies the results but doesn't detail a consensus or arbitration process.

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, an MRMC comparative effectiveness study was not done. The device description indicates that "assisted software enables the trained human professional... to verify the results of the study and generate a report," suggesting a human-in-the-loop system. However, the performance testing section focuses on the device's functional integrity and signal equivalence to a predicate device, not on the comparative effectiveness of human readers with vs. without AI assistance. Therefore, no effect size for human reader improvement is reported.

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

• The device's intended use explicitly states: "The data is downloaded after the recording is completed and the assist software enables the trained human professional... to verify the results of the study and generate a report." This clearly indicates a human-in-the-loop system, not a standalone (algorithm only) performance evaluation. The device is a "portable sleep data recorder," where the software assists a human professional, implying the algorithm is not intended for standalone diagnostic decisions.

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

• For the core performance assessment related to signal quality and equivalence, the ground truth appears to be based on comparison to the established performance of the legally marketed predicate device, MediPalm®. The statement "The performance of the BRAEBON Medical Corporation MediByte™ (subject device) was identical to that of MediPalm® (predicate device)" suggests that the MediPalm's output served as the reference for signal accuracy. For the "overnight studies," the ground truth for interpreting the physiological signals would presumably come from standard sleep study analysis by qualified professionals, but this isn't explicitly detailed as an expert consensus process for setting a definitive "ground truth" for the test set itself.

8. The sample size for the training set:

• The document does not mention a "training set" in the context of machine learning or AI development. The device appears to be a signal acquisition and recording device with "auto assist software," rather than a deep learning model that requires explicit training data. The testing described is primarily functional and comparative to a predicate device, not an evaluation of an AI algorithm's learned performance.

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

• Since there's no mention of a training set or an AI algorithm that was "trained," there's no information on how a ground truth for such a set would have been established.

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The BRAEBON Medical Corporation MEDIPalm-20™ is intended for use in collecting, recording and disclaying physiological data from FDA-cleared sensors during studies in both a cinical and home environment and for transmitting this data to an electrically isolated FDA-cleared polysomnographic analysis station for analysis.

The MEDIPalm-20™ is a portable data recorder used to collect physiological signals for transfer to an FDA-cleared polysomnographic analysis station capable of reading European Data Format (.edf) files.

The MEDIPalm-20™ is a hand-held recording device capable of acquiring and storing physiological signals from FDA-cleared sensors and transmitting the physiological data to a computer through the Universal Serial Bus (USB) port.

The MEDIPalm-20™ records up to 16 channels of physiological signals: any combination of eight differential analog channels of electroencephalogram (EEG), electronyogram (EOG), electrocardiogram (EMG), and electrocardiogram (EKG) signals; as well as chest effort; abdominal effort; airflow pressure; snoring; body position; arterial oxygen saturation (SaO2); pulse rate; and SaO2 probe status. The signals can be viewed in real time on a built-in LCD or on a remote computer. The data can be downloaded after collection for analysis on any FDA-deared polysomnographic analysis station capable of reading European Data Formatted files.

The provided text is a 510(k) summary for the BRAEBON Medical Corporation MEDIPalm-20™ device. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving novel efficacy through extensive clinical trials. Therefore, the details requested about acceptance criteria for device performance, ground truth establishment, expert adjudication, and comparative effectiveness studies as typically found in AI/ML device submissions are largely not applicable in this context.

However, I can extract the information that is present concerning performance testing and equivalence.

1. Table of Acceptance Criteria and Reported Device Performance:

The document states that functional testing was performed to confirm the MEDIPalm-20™ meets its stated performance specifications. However, specific quantitative acceptance criteria (e.g., accuracy percentages, sensitivity/specificity thresholds) and their corresponding reported device performance values are not detailed in this 510(k) summary. The document only states:

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

• Sample Size:
  • For the verification that signals were readable and appropriate: "a healthy person" (i.e., n=1).
  • For simulation tests comparing with the predicate device: Not specified, but implied to be a simulation environment rather than patient data.
• Data Provenance: Not explicitly stated, but the tests were performed by the manufacturer, BRAEBON Medical Corporation (located in Canada, as per the address, though the submission is to the US FDA). The nature of the tests (connecting to "a healthy person" and "simulation tests") suggests internal validation rather than a clinical trial with sourced patient data. These appear to be prospective tests performed specifically for this submission.

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

Not applicable. The document does not describe the establishment of "ground truth" in the context of diagnostic accuracy or expert consensus. The testing focused on functional performance, software compliance, electrical safety, and signal readability, not on diagnostic outcomes requiring expert interpretation against a gold standard.

4. Adjudication Method for the Test Set:

Not applicable. Since there was no "ground truth" establishment in the sense of diagnostic labels requiring expert consensus, no adjudication method was used.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, and Effect Size:

No. A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The submission explicitly states: "No clinical studies were required to support a substantial equivalence determination." This device is a data acquisition system, not an AI diagnostic tool, so such studies are generally not relevant for this type of 510(k).

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

Yes, in spirit. The performance tests described (functional, software, electrical safety, simulation tests) are essentially "standalone" evaluations of the device's ability to acquire and output data correctly, independent of a human interpreter's diagnostic capabilities. The comparison showing "identical" performance to the Sandman Digital Recording System in simulation tests is also a standalone evaluation of the device's technical output.

7. The Type of Ground Truth Used:

The "ground truth" for this device's performance was its own stated performance specifications and the output of a predicate device (Sandman Digital Recording System) for comparative testing, rather than a clinical ground truth like pathology or patient outcomes. For signal readability, the ground truth was simply whether the signals produced were "readable, appropriate signals."

8. The Sample Size for the Training Set:

Not applicable. This device is an electroencephalograph/polysomnograph data acquisition system, not a machine learning model that requires a training set.

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

Not applicable. As stated above, this device does not utilize a training set in the context of machine learning.

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The Ultima Snoring Mike provides a qualitative measure of snoring for recording onto an FDA-cleared data acquisition system.

Not Found

The provided document is an FDA 510(k) clearance letter for the "Ultima Snoring Mike, Model 0540." This type of document declares substantial equivalence to a predicate device and permits marketing. It does not contain specific details about acceptance criteria or a dedicated study proving the device meets those criteria, as typically found in clinical study reports or detailed technical documentation.

Therefore, I cannot provide the requested information from the given text.

The document primarily states:

• The device name and model.
• Its classification (Class II, Product Code MNR).
• Its intended use: "provides a qualitative measure of snoring for recording onto an FDA-cleared data acquisition system."
• Target population and environment of use.
• Concurrence of the FDA's Office of Device Evaluation.

This document serves as an authorization to market the device, not a detailed technical report of its performance against specific acceptance criteria.

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The Ultima Airflow Pressure Sensor™ is intended for use during sleep disorder studies as a qualitative measure of respiratory airflow for recording onto an FDA-cleared data acquisition system. Respiratory pressures are converted into voltage signals for input to a physiological recorder. The sensor uses a 3.6-volt lithium battery and plugs directly into the patient headbox or to an FDA-cleared DC amplifier. A disposable nasal cannula with a 0.2-micron hydrophobic filter attaches to the patient and connects to the input of the Ultima Airflow Pressure Sensor.

The Ultima Airflow Pressure Sensors, Models #0580L, are small interface devices which convert levels of air pressure to corresponding levels of voltage which can be recorded on any FDA-cleared physiological recorder intended to record low-level electrical signals. The purpose of the device is to detect respiratory airflow. There is one pneumatic input for air pressure, and one set of electrical outputs representing the electrical equivalent of the pressure. The electrical outputs are the active or positive output and the reference or negative output. This device uses a disposable nasal cannula with a disposable 0.2 um hydrophobic filter. The cannula and filter attach to the patient and connect to the pressure input labeled "INPUT" using a standard luer connector. The electrical output connectors are made with 1.5 mm-safety connectors that connect to either an FDA-cleared AC amplifier or an FDA-cleared DC amplifier with a low frequency filter setting of less than 0.05 Hz and a high frequency filter setting of 15 -30 Hz. The Ultima Airflow Pressure Sensor consists of a red ABS plastic enclosure measuring approximately 1.5"(W) x 2.8"(L) x 0.6"(H). The device is battery powered with one 3.6 volt 1/2AA lithium cell. The estimated battery life is 450 hours (50 - eight hour uses), with an On/Off switch provided to conserve battery life when not in use. The device has a 0.14-Hz blinking status LED: green indicates proper operation, red indicates low battery. The battery can be replaced by removing the battery cover, removing the battery and inserting a new one in the orientation shown in the battery compartment of the unit. The pressure sensor is attached either to a bed frame or to a wall with a Velcro strap.

The acceptance criteria for the Ultima Airflow Pressure Sensor and the study proving it meets these criteria are described below:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• The document states "Laboratory testing has been performed using this device in place of the predicate device for respiratory airflow monitoring."
• However, no specific sample size for the test set or information on data provenance (e.g., country of origin, retrospective/prospective) is provided in the given text.

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

• This information is not provided in the document. The testing mentioned is laboratory testing comparing the new device to the predicate.

4. Adjudication Method for the Test Set:

• No information on adjudication methods is provided in the document.

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 MRMC comparative effectiveness study was done or reported. This device is an airflow pressure sensor, not an AI-assisted diagnostic tool for human readers.

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

• The device is a sensor, not an algorithm. Therefore, a standalone algorithm performance study is not applicable and was not performed. The "standalone" performance here would refer to the sensor's ability to convert pressure to voltage accurately. The document states "These tests conclude that the electrical signals recorded using the Ultima Airflow Pressure Sensor provide equivalent informational content to the signals recorded using the predicate device." This implies a form of standalone performance evaluation against the predicate.

7. The type of ground truth used:

• The ground truth used was comparison to a legally marketed predicate device (Ultima Airflow Sensor, Braebon Medical Corporation, K981445). The specific type of "ground truth" for respiratory airflow is implicitly the output of the predicate device. The claim is "equivalent informational content."

8. The sample size for the training set:

• The document does not mention a training set sample size. This device is a hardware sensor, not a machine learning model, so the concept of a "training set" in the AI sense does not apply here. Development and calibration would have occurred, but the specifics are not detailed.

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

• As this is not an AI/machine learning device, the concept of a "training set" with established ground truth in that context does not apply. If "training" refers to the device's design and calibration, then the ground truth would have been established through engineering specifications and potentially comparison to established reference standards for pressure and voltage measurement. However, this information is not provided in the document.

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A qualitative measure of respiratory airflow for recording onto a data acquisition system.
Target Population: Children and adult patients who are screened during sleep disorder studies
Environment of Use: The majority of the screenings occur at a sleep laboratory although the sensor can also be used in home-studies.

Ultima Airflow Sensor, 1051

This document, a 510(k) clearance letter for the Ultima Airflow Sensor, provides very limited information regarding detailed acceptance criteria or a specific study proving the device meets those criteria. The letter primarily confirms that the device is substantially equivalent to legally marketed predicate devices.

Based on the provided text, the following information can be extracted or inferred:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state specific acceptance criteria in terms of numerical performance metrics (e.g., sensitivity, specificity, accuracy, precision, correlation coefficients) or a table of performance results for the Ultima Airflow Sensor.

However, the "Indications for Use Statement" (page 2) implies its intended performance:

• Acceptance Criterion (Implied): The device must provide "a qualitative measure of respiratory airflow."
• Reported Device Performance (Implied): The device is capable of providing "a qualitative measure of respiratory airflow for recording onto a data acquisition system."

The FDA's determination of "substantial equivalence" (K981445) is the primary "proof" mentioned that the device meets the necessary criteria for its intended use, based on comparison to already approved predicate devices. This typically means the device performs at least as well as, or equivalently to, the predicate device for its intended use, but the specific performance results from comparative testing are not detailed in this letter.

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

• Sample size: Not specified in the provided document.
• Data provenance: Not specified in the provided document. The 510(k) process relies on demonstrating equivalence to predicate devices which were previously cleared, rather than necessarily describing new, independent clinical trials with detailed test sets. Therefore, the "data" would likely refer to engineering evaluations, bench testing, and potentially some limited clinical performance data demonstrating equivalence, but no specifics are given here.

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

• Number of experts: Not specified.
• Qualifications of experts: Not specified.

4. Adjudication method for the test set:

• Adjudication method: Not specified.

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:

• MRMC study: No indication of an MRMC study being performed. This is not an AI device, but a physical sensor. The document does not mention human "readers" or "AI assistance."

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

• This is a physical sensor, not an algorithm. Therefore, the concept of "standalone algorithm performance" is not applicable in the context of this document. The device provides raw data ("qualitative measure of respiratory airflow for recording onto a data acquisition system").

7. The type of ground truth used:

• The document implies that the "ground truth" for demonstrating substantial equivalence would be the performance characteristics of its legally marketed predicate device(s). The specific performance data and the methods to establish their "ground truth" are not detailed for either the subject device or the predicate. For a device measuring respiratory airflow, the ground truth would typically be established against highly accurate reference methods (e.g., spirometry, polysomnography channels) in controlled studies.

8. The sample size for the training set:

• Not applicable/Not specified. As this is a sensor (hardware) and not a machine learning algorithm, there isn't a "training set" in the conventional sense.

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

• Not applicable/Not specified, for the reasons mentioned above.

Summary of Limitations Based on Document:

This 510(k) clearance letter serves as a regulatory approval notice and does not contain the detailed technical report or study data that would lay out acceptance criteria and performance results directly. The "proof" of meeting criteria primarily stems from demonstrating "substantial equivalence" to a predicate device, meaning its safety and effectiveness are similar to a device already on the market for the same intended use. The specifics of the testing (sample sizes, expert involvement, ground truth methods for the predicate or the new device) are not part of this summary document.

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An indicator of respiratory effort for recording onto a data acquisition system.

Ultima Respiratory Effort Sensor, 0520 and 0522

This document is a 510(k) clearance letter from the FDA for the Ultima Respiratory Effort Sensor Models 0520 and 0522. It primarily states that the device is substantially equivalent to legally marketed predicate devices and can therefore be marketed.

While it mentions the device's intended use and target population, it lacks the detailed information required to answer your specific questions regarding acceptance criteria, study methodologies, and performance metrics.

Specifically, the document does not contain:

1. A table of acceptance criteria or reported device performance.
2. Details about sample sizes, data provenance, or ground truth establishment for any test sets.
3. Information on expert qualifications, adjudication methods, or MRMC studies.
4. Data on standalone algorithm performance or training set details.

Therefore, I cannot provide the requested information based on the provided text.

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The BRAEBON MEDICAL CORPORATION Ultima Body Position Sensor™ is intended for use during sleep disorder studies as an indicator of body position. The sensor uses a threevolt lithium battery and plugs directly into either a DC amplifier or multiplexer.

The BRAEBON MEDICAL CORPORATION Ultima Body Position Sensor™ is intended for use during sleep disorder studies as an indicator of body position. The sensor uses a threevolt lithium battery and plugs directly into either a DC amplifier or multiplexer.

This document is a 510(k) clearance letter from the FDA for the Ultima Body Position Sensor. It confirms substantial equivalence to predicate devices but does not contain information about acceptance criteria, study design, or performance data. Therefore, I cannot generate the requested table or answer most of the questions.

The document states:

• Device Name: Ultima Body Position Sensor
• Indications For Use: Intended for use during sleep disorder studies as an indicator of body position in children and adult patients.
• Predicate Devices: Sleep Position Indicator, Model BPI1, K940013; Mini-Tracker, K923033.

Since the provided document is an FDA clearance letter and not a study report, it does not contain the detailed information necessary to complete the requested table and answer the study-related questions.

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