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EvieMED Ring is a wireless, non-invasive, and stand-alone pulse oximeter intended to be used for spot checking oxygen saturation of peripheral arterial hemoglobin (SpO2) and pulse rate of adult users with a condition that might benefit from monitoring. Only the Pulse Oximeter function of the EvieMED device provides medical data. The device is designed for use in a healthcare or home environment, during no motion conditions. It is not intended for critical care and out-of-hospital transport use and does not have alarms. It is available with a prescription and can aid in the diagnosis and treatment of health conditions.

The EvieWED Ring is a rechargeable noninyasive wearable is an open arrow design that allows for a snug fit and comfort during fluctuations in fluid retention and/or weight changes. There is an optical sensor that collects physiological signals and an app that processes the signals and provides spot SpO2 and pulse rate (PR) measurements during a spot check on a user's connected iOS phone. The ring is designed to be worn continuously and is waterproof, allowing the user to wear the device during handwashing, showering, and bathing without damaging the ring.

The EvieMED Ring is designed for accurate pulse oximeter readings inside of a clinical setting. The device is for a single user but can be used for many users, with the rechargeable battery in the ring and the charger expected to last 2 years or more. The device is not life-sustaining or life-supporting and does not include alarms. It is not indicated for use in motion or in conditions of low perfusion.

The device is supplied in a labeled box that includes a wearable, a charger, a USB-C cable, and a quick start guide. The ring is provided in common ring sizes (e.g., US 5 through US 12). Operation of the device depends on connection to the user's device running iOS 16.0 and above where the EvieWED Ring Mobile Application has been installed and registered, and a user-supplied AC adaptor is required for recharging the charger.

The provided text describes the acceptance criteria and the study conducted for the EvieMED Ring, a pulse oximeter. Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Eleven (11) healthy young adults (3 male, 7 female). The text also mentions "4 rings/subject" and approximately "25 samples/subject" for a total of 816 pooled samples.
• Data Provenance: The study was conducted at an "independent lab" with subjects placed into "controlled hypoxia." This suggests a prospective, controlled clinical study. The country of origin is not explicitly stated but is implicitly within the US context given the FDA submission.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The text describes an "IRB-approved SpO2 accuracy study" where "SaO2 and test samples were taken" and "SaO2 data was compared to the test (SpO2) data." This strongly implies that arterial blood gas (SaO2) measurements were used as the gold standard/ground truth. There is no mention of human experts establishing ground truth for this type of accuracy study; it relies on the direct physiological measurement from arterial blood.

4. Adjudication Method for the Test Set

Not applicable. The ground truth was established by direct physiological measurement (arterial blood gas) and statistical comparison, not expert consensus or adjudication.

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

No, an MRMC study was not performed. This device is a standalone pulse oximeter, and the study focused on its accuracy compared to a physiological gold standard, not on human reader performance with or without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, this was a standalone performance study. The device's SpO2 and pulse rate readings were directly compared to a reference measurement (SaO2 from arterial blood) without a human interpretation loop.

7. The Type of Ground Truth Used

The ground truth used was outcomes data / direct physiological measurement specifically, arterial blood gas (SaO2) measurements. Subjects were desaturated under controlled conditions, and their SaO2 was measured directly and compared to the device's SpO2 readings.

8. The Sample Size for the Training Set

The document does not specify a training set size. The described study is a clinical validation study on a test set, performed to demonstrate the accuracy of the device. For a device like a pulse oximeter, the core algorithm for SpO2 calculation is typically based on established biophysics and signal processing, often refined and designed during internal development rather than being "trained" on a large dataset in the machine learning sense, at least not in a way that would be detailed in a 510(k) summary focused on validation. If machine learning was used, the training data would be internal to the company and not usually disclosed in this summary.

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

Not applicable, as a specific "training set" and its ground truth establishment are not described in this regulatory submission summary. The accuracy study described is a clinical validation (test set) study.

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Belun Ring BLR-200 is a wireless, non-invasive and stand-alone pulse oximeter intended to be used for spot-checking and/or continuous data collection and recording of oxygen saturation of arterial hemoglobin (SpO2) and the pulse rate of adult patients through index finger in home environment for up to ten hours, during no motion and motion conditions, and for patients who are well or poorly perfused. It is not intended for single-use and out-of-hospital transport use and does not have alarms.

Belun Ring BLR-200 consists of two parts: a Ring, and a host program. The Ring, which has smooth and light design and is easy to wear and take off, is intended to be worn on the bottom of index finger providing comfortable and accurate measurements. To make the Optical module appropriately contact with the soft part of the finger, the Ring arm is designed to be changeable for fitting different sizes of fingers. The Ring transfers the collected data to host via Bluetooth low power technology. The host program translates the collected data into text and graph which can be easily interpreted by the user.

The system consists of two main platforms, namely Ring and host. It includes one embedded software and one host program, namely Ring firmware embedded in Ring and Belun Ring Management (BRM) executed in host respectively. The Ring is responsible for signal acquisition, data processing, parameters calculation (SpO2/PR algorithm), sensor interfacing and data storage. The host is for data display, data export and user interface. The system is modularized, and the communication protocol is proprietary. The system is secured with cybersecurity measures.

This document is a 510(k) summary for the Belun Ring BLR-200. It doesn't contain a detailed study report that proves the device meets specific acceptance criteria with reported device performance, sample sizes, expert qualifications, or details about standalone or MRMC studies.

However, based on the provided text, I can infer some information about how the device's accuracy was tested and the general approach to demonstrating substantial equivalence.

Here's an attempt to answer your questions based on the available information:

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

The document mentions that "The SpO2 and pulse rate accuracy of proposed device have also been tested on healthy subjects and compared with the predicate device in hypoxia tests." However, it does not explicitly state specific acceptance criteria (e.g., A_rms value for SpO2 accuracy) or provide the reported device performance values for these tests. It only states that the device was tested for accuracy.

Therefore, a table cannot be constructed with the detailed information you requested.

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

• Sample Size: The document states that accuracy was tested "on healthy subjects" for hypoxia tests. It does not specify the number of subjects used for the test set.
• Data Provenance: Not explicitly stated. The applicant is based in Hong Kong, but the location of the hypoxia tests is not mentioned. It is implied to be prospective since it describes testing performed on "healthy subjects."

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. For pulse oximetry accuracy during hypoxia tests, the ground truth is typically established by an arterial blood gas co-oximeter reading, not by human experts.

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

Not applicable in the context of pulse oximetry accuracy testing against a reference standard (e.g., arterial blood gas co-oximeter). Adjudication methods are more commonly used when human interpretation of data is being assessed.

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

There is no indication of an MRMC comparative effectiveness study involving human readers or AI assistance in this document. The device is a standalone pulse oximeter.

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

Yes, a standalone performance assessment was done. The document states:

• "The SpO2 and pulse rate accuracy of proposed device under low perfusion and motion conditions have been tested against functional tester."
• "The SpO2 and pulse rate accuracy of proposed device have also been tested on healthy subjects and compared with the predicate device in hypoxia tests."

These describe assessments of the device's (algorithm's) performance in isolation.

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

For the hypoxia tests, the ground truth for SpO2 and pulse rate accuracy is implicitly established by a reference method, typically a co-oximeter measurement from arterial blood samples. This is the standard for pulse oximeter accuracy testing.

8. The sample size for the training set

This information is not provided. The document primarily focuses on demonstrating substantial equivalence through testing of the final device, not on the developmental or training phases of its algorithms.

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

This information is not provided.

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The Belun Sleep System BLS-100 is a wearable device intended to record, analyze, display, export, and store biophysical parameters to aid in evaluating moderate to severe sleep-related breathing disorders suspected of sleep apnea. The device is intended for use in clinical and home settings under the direction of a Healthcare Professional (HCP).

The Belun Sleep System BLS-100 comprises a sensor that is worn on the proximal phalanx of index finger (Belun Ring) over-night whilst the subject is sleeping and a stand-alone analysis software (Belun Sleep AI). The Belun Ring has a small biocompatible enclosure. The sensor has 2 LEDs, one in the red spectrum and the other in the infrared spectrum, and an accelerometer. The sensor is placed on the proximal phalanx of the index finger, with the sensor window applied against the palmar side of the proximal phalanx of the index finger. The sensor measures the reflected red/infrared signals to record the photoplethysmograph (PPG) signal. The accelerometer is used to detect movement. The data recorded by the Belun Ring is stored in device on-board memory. The data is exported when the Belun Ring is returned to the prescribing HCP via USB or Bluetooth and passed to the Belun Sleep AI Software, which is standalone PC software. The Belun Sleep Al loads and processes the signal from the exported data and generates the apnea-hypopnea index (bAHI) and sleep staging identification (bSTAGES).

Let's break down the information regarding the acceptance criteria and the study that proves the device meets them for the Belun Sleep System BLS-100.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the clinical study results being presented as sufficient evidence for clearance. While explicit "acceptance criteria" are not listed as pass/fail thresholds in a formal table, the provided performance metrics represent the device's demonstrated capabilities.

Here's a table summarizing the reported device performance, which the FDA accepted as evidence of substantial equivalence:

2. Sample Size and Data Provenance

• Sample Size for Test Set: 106 patients suspected of obstructive sleep apnea (OSA).
• Data Provenance: The study compared the device's performance against overnight polysomnography (PSG) studies conducted in a sleep laboratory. The location of the sleep laboratory (country of origin) is not explicitly stated in the provided text. The study design implies this was a prospective collection of data for this evaluation, as it describes patients going through a study with both the Belun device and PSG.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: At least two experts, as the text states "a senior sleep tech scorer and reviewed by a board-certified sleep physician."
• Qualifications of Experts:
  • One "senior sleep tech scorer."
  • One "board-certified sleep physician."

4. Adjudication Method for the Test Set

The adjudication method used to establish the ground truth for the test set was: "All sleep studies were manually scored based on the AASM scoring manual (version 2.4) by a senior sleep tech scorer and reviewed by a board-certified sleep physician." This indicates a two-step process where one scorer performs the primary scoring, and a physician provides a review, implying a form of consensus or verification, though not a multi-reader disagreement resolution specifically.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The study focuses on the standalone performance of the device's AI algorithms (bAHI and bSTAGES) compared to PSG ground truth, not on how human readers' performance improves with or without AI assistance.

6. Standalone Performance

• Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The clinical study section directly reports the accuracy, sensitivity, and specificity of the Belun Sleep System BLS-100's AHI and sleep staging calculations compared to PSG results, indicating the performance of the device's algorithms themselves. The statement "All investigators, sleep lab team, and scorers were blinded to the results until statistical analysis was performed" further supports that the device's output was generated independently.

7. Type of Ground Truth Used

• The type of ground truth used was expert consensus based on Polysomnography (PSG) studies, manually scored according to the American Academy of Sleep Medicine (AASM) guidelines (version 2.4) by a senior sleep tech scorer and reviewed by a board-certified sleep physician.

8. Sample Size for the Training Set

• The document does not explicitly state the sample size for the training set used for the Belun Sleep AI's deep-learning algorithms. It only provides details for the clinical validation (test) set.

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

• The document does not explicitly state how the ground truth for the training set was established. It mentions that the clinical evaluation "confirmed that the Belun Sleep System deep-learning algorithms calculating the Belun Apnea Hypopnea Index (bAHI) and Belun Sleep Stage (bSTAGES) generate comparable output to human manual scoring of an Apnea Hypopnea Index (AHI) from Polysomnography (PSG) studies, using American Academy of Sleep Medicine (AASM) scoring guidelines for adult patients". While this describes the validation against PSG ground truth, it doesn't detail the ground truth establishment process for the data used to train the AI models.

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