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The Remi Custom Night Guard is Indicated for protection of teeth and restorations against grinding and clenching, and as an aid in the reduction of medically diagnosed migraine pain associated with jaw clenching and bruxing.

The Remi Night Guard is a mouth guard used as a barrier between teeth for nighttime teeth grinding by creating physical separation between upper and lower tooth surfaces preventing tooth damage caused by bruxism (e.g., grinding and clenching).
Remi Night Guards are manufactured using impressions and/or scans.

N/A

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The REMI Remote EEG Monitoring System is indicated for use in healthcare settings where near real-time and/or remote EEG is warranted and in ambulatory settings where remote EEG is warranted. REMI System uses single patient, disposable, wearable sensors intended to amplify, capture, and wirelessly transmit a single channel of electrical activity of the brain for a duration up to 30 days.

REMI System uses the REMI Mobile software application that runs on qualified commercial off-the-shelf mobile computing platforms. REMI Mobile displays user setup information to trained medical professionals and provides notifications to medical professionals and ambulatory users. REMI Mobile receives and transmits data from connected REMI Sensors to the secure REMI Cloud where it is stored and prepared for review on qualified EEG viewing software.

REMI System does not make any diagnostic conclusion about the subject's condition and is intended as a physiological signal monitor. REMI System is indicated for use with adult and pediatric patients (1+ years).

The REMI System has three major components:

• 1. REMI Sensor A disposable EEG sensor which is placed on the patient's scalp using a conductive REMI Sticker
• 2. REMI Mobile A mobile medical application that is designed to run on a qualified commercial-off-the-shelf mobile computing platform (an Android tablet for use in healthcare settings, and a portable/wearable Android device (phone or smartwatch) for use in ambulatory settings), acquire EEG data transmitted from REMI Sensors and then transmit the EEG data and associated patient information via wireless encrypted transmission to.
• 3. REMI Cloud A HIPAA-compliant secure cloud storage and data processing platform where data is processed into a qualified EEG reviewing software format for neurological review.

The provided document is a 510(k) Pre-market Notification Summary for the REMI Remote EEG Monitoring System (K243185). This document details the device's characteristics, indications for use, and the studies conducted to demonstrate its substantial equivalence to a predicate device (REMI Remote EEG Monitoring System, K230933).

Based on the provided information, here's a description of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily relies on comparisons to its own predicate device (K230933) and general performance testing against recognized standards. Specific quantitative acceptance criteria are not explicitly detailed in a table format within this summary, but the general assertion is that the device met all predetermined acceptance criteria derived from the listed tests.

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

• Clinical Performance Test Set (Pediatric Extension):
  • Sample Size: 13 younger pediatric patients (ages 1 to <6 years)
  • Data Provenance: Retrospective review of existing REMI EEG records gathered from NIH-funded studies conducted by Epitel in support of REMI development efforts. Data collected under IRB oversight and registered under NCT03583957. The document states a single pediatric-focused site was involved.
  • Geographic Origin: Not explicitly stated, but NIH funding implies a U.S. origin.

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

• Number of Experts: One
• Qualifications: An "experienced pediatric epileptologist independent of wired EEG and generally separated in time from the actual time of collection by over a year - to ensure appropriate experience, consistency, and minimization of bias."

4. Adjudication Method for the Test Set

• Adjudication Method: None explicitly described beyond a single expert's retrospective review. The expert's review was the ground truth.

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

• MRMC Study Done? No. The document describes a retrospective review by a single expert to affirm the clinical value of the EEG records, not a comparative effectiveness study pitting human readers with vs. without AI assistance. The device is a physiological signal monitor, not an AI diagnostic tool in this context.

6. Standalone (Algorithm Only) Performance

• Standalone Performance Done? The "Software Verification Testing" section describes end-to-end testing of the system (acquiring, transmitting, and making data viewable), which is essentially testing the algorithm's ability to process and present the data. It verifies the functionality of the system components and data integrity. However, it's not a standalone diagnostic performance study (e.g., sensitivity/specificity for a given condition), as the device "does not make any diagnostic conclusion about the subject's condition." Its essential performance is to record and transfer EEG data.

7. Type of Ground Truth Used

• For Clinical Performance (Pediatric Extension): Expert consensus/review. The "experienced pediatric epileptologist" reviewed the REMI EEG data to assess its quality and presence of EEG features.
• For Technical Performance (Software, Electrical, etc.): Predetermined engineering specifications, compliance with recognized standards, and functional verification.

8. Sample Size for the Training Set

• The document does not mention a training set or machine learning model being the primary focus of this submission. The device is described as a "physiological signal monitor" that "does not make any diagnostic conclusion." The clinical experience discussed references "318 pediatric patients with a mean REMI Sensor wear of 1.7 days" from NIH-funded studies initially used for "REMI development efforts," which could have implicitly involved some level of data-driven development or refinement, but it's not explicitly framed as a "training set" for a distinct AI algorithm evaluated in this 510(k). The focus of this 510(k) is the extension of the device's indications to a younger pediatric age group and the new hydrocolloid sticker.

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

• As a training set is not explicitly referred to as part of a machine learning model specific to this 510(k)'s purpose, the method for establishing its ground truth is not detailed. The "REMI development efforts" data (n=318) was collected "under Institutional Review Board oversight." However, for the specific aspect of this 510(k) (pediatric extension), the ground truth for the test set (n=13) was established via retrospective review by an experienced pediatric epileptologist.

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The Remi Night Guard is indicated for protection of teeth and restorations against grinding and clenching.

The Remi Custom Night Guard is a mouth guard used as a barrier between teeth for nighttime teeth grinding by creating physical separation between upper and lower tooth surfaces preventing tooth damage caused by bruxism (e.g., grinding and clenching).

This document is a 510(k) Premarket Notification from the FDA regarding a dental device called the "Remi Custom Night Guard." It states that the device is substantially equivalent to a legally marketed predicate device (LIJIA Night Guard), meaning a full clinical study with acceptance criteria and a detailed study report is not required.

Therefore, the document does not contain the information requested in the prompt regarding acceptance criteria and a study proving the device meets those criteria.

The document explicitly states:

• "no clinical studies were deemed necessary to demonstrate the safety and effectiveness of the subject device." (page 6)

Instead, the submission relies on:

• Comparison to a Predicate Device: The Remi Custom Night Guard is shown to have the same indications for use, product code, classification, anatomical sites, sterility, patient removability, and general technological features as the predicate LIJIA Night Guard (K241369).
• Non-Clinical Performance Testing:
  • Durability testing: Completed, but no details on specific acceptance criteria or results are provided.
  • Internal manufacturing validation: Performed to test the dimensional accuracy of the manufacturing process, but no specific acceptance criteria or results are provided.
  • Biocompatibility testing: Performed in accordance with ISO 10993, with cytotoxicity testing completed. No specific acceptance criteria or results are provided beyond the statement that it was completed.

In summary, because this is a 510(k) submission based on substantial equivalence to a predicate device and explicitly states that no clinical studies were deemed necessary, the detailed information about acceptance criteria and a study to prove device performance (as requested in the prompt) is not present in this document.

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To relieve dental hypersensitivity caused by bleaching procedures, cold, heat, acids, or sweets.

ReminGel is a convenient, premixed, ready-to-use, opaque aqueous-based gel. ReminGel consists of suspended hydroxyapatite and calcium and phosphate salts that help restore a tooth's hydroxyapatite structure through remineralization. This remineralization results in tubule occlusion and blocks fluid flow in dentinal tubules, thereby relieving dentinal sensitivity. ReminGel blocks nerve excitability through potassium release. ReminGel is packaged in a 3oz (90mL) tube. ReminGel is for prescription use (Rx) and over-the-counter (OTC) use. ReminGel can be applied to teeth using dental appliances (e.g. fluoride tray, whitening tray, periotray, etc.) or via a toothbrush.

Here's a breakdown of the acceptance criteria and study information for ReminGel, based on the provided FDA 510(k) summary:

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample Size used for the test set and data provenance:

   • Tubule Occlusion Testing: Resected human teeth were used. The exact sample size ("N") is not specified.
   • Biocompatibility Testing (Cytotoxicity, Irritation, Sensitization): Not specified in terms of human subjects or teeth. These are in vitro and in vivo tests, typically performed on cell cultures or animal models for initial screening (as per ISO 10993 standards listed). The document does not provide details on the specific samples used for these tests.
   • Shelf-Life Testing: Not specified.
   • Transit Testing: Not specified.
   • Data Provenance: Not explicitly stated, but based on the in vitro nature of the tubule occlusion study on "resected human teeth," it implies laboratory testing rather than a clinical trial in patients. The biocompatibility tests are also laboratory-based. It is retrospective in the sense that the results were gathered and submitted as part of the 510(k) submission, not a prospective clinical trial.

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

   • Not applicable/Not specified. The studies cited are non-clinical (laboratory/bench testing). For tubule occlusion, the "ground truth" would be the visible occlusion as observed through microscopy, which doesn't directly involve multiple expert adjudicators for "truth" establishment in the way clinical images would.

3. Adjudication method for the test set:

   • Not applicable. As the studies are non-clinical, there is no expert adjudication method like 2+1 or 3+1 typically used for clinical image interpretation.

4. 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 conducted. This device (ReminGel) is a medical product (cavity varnish) and not an AI/software-as-a-medical-device that would typically involve human readers and AI assistance for interpretation.

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

   • Not applicable. This is not an algorithm or AI device.

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

   • Tubule Occlusion: Visual observation of dentinal tubule occlusion (likely microscopic evaluation).
   • Shelf-Life: Measured physical and chemical properties over time.
   • Transit, Cytotoxicity, Irritation, Sensitization: Results from standardized laboratory test methods (e.g., ISO 10993 series).

7. The sample size for the training set:

   • Not applicable. This is a medical product, not a machine learning/AI model that requires a training set.

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

   • Not applicable, as there is no training set for this type of device.

Overall Context: The 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (Super Seal) and a reference device (Senzzzz Away) through non-clinical performance and biocompatibility testing, rather than extensive clinical efficacy trials. Clinical performance was explicitly "not deemed necessary" for this submission, likely due to the established mechanism of action and similarity to legally marketed devices.

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The REMI-AI Rapid Detection Module (REMI-AI RDM) is a seizure detection module which is integrated into the REMI Remote EEG Monitoring System and is only indicated for use within non-ICU (Intensive Care Unit) healthcare settings. REMI-AI RDM has not been validated for and is not indicated for detection of electrographic status epilepticus.

REMI-AI RDM conducts automated analysis of REMI EEG data in near real-time and provides notifications of potential electrographic seizures (events) through the REMI System when seizure prevalence of 10% or greater (indicating seizure activity of at least 30 seconds within a 5-minute rolling window) is detected. When seizure prevalence is displayed, the notification also displays the corresponding event detection confidence. Notifications are intended to be used by qualified clinicians who will exercise professional judgment in their application. Detected events are also annotated in the associated REMI EEG record as an aide to the qualified physician's REMI EEG review.

Delays of up to several minutes may occur between the detection of an event and the generation of an event notification, and are thus not a substitute for real-time monitoring. REMI-AI RDM does not make any diagnostic conclusion about the subject's condition and is intended as a physiological signal monitor. REMI-AI RDM is indicated for use with adult and pediatric patients (6+ years).

REMI-AI RDM conducts automated analysis of EEG data collected by the REMI System in near real-time. REMI-AI RDM provides notifications of the prevalence and confidence of potential electrographic seizures, having a combined prevalence of 10% or greater, which correlates with a duration of at least 30 seconds of activity within a rolling 5 minute window of EEG.

REMI-AI RDM notifications are presented through the REMI Mobile software application, and are intended to be used by qualified clinicians who will exercise professional judgment in their interpretation. Notifications include the prevalence and confidence value for the event and are marked in the associated EEG record in order to assist qualified clinicians in their assessment.

REMI-AI RDM notifications identify when a section of EEG is consistent with seizure characteristics it has been trained to recognize. When a notification is presented, clinical context and facility procedures should inform next steps in patient evaluation and management. REMI-AI RDM does not make any treatment or management recommendations.

Here's a summary of the acceptance criteria and study details for the REMI-AI Rapid Detection Module (REMI-AI RDM), based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size:
  • 22 patient records with 54 consensus-determined electrographic seizures (lasting at least 30 seconds).
  • 22 patient records with no consensus-determined electrographic seizures.
  • Total Validation Sample Size: 44 patient records.
• Data Provenance: The text does not explicitly state the country of origin. It indicates that the data was collected concurrently with standard-of-care 19-channel, full-montage, video-EEG in "Epilepsy Monitoring Units (EMUs) or for up to 3 continuous days during at-home ambulatory EEG monitoring." This suggests the data is retrospective as it was "previously acquired" for validation, but the initial data collection method (prospective/retrospective) for the source may vary.

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

• Number of Experts: 3 independent expert epileptologists for panel review, selected from a panel of 6.
• Qualifications of Experts: Certified by the American Board of Psychiatry and Neurology or certified by the American Board of Clinical Neurophysiology with Special Competency in Epilepsy Monitoring.

4. Adjudication Method for the Test Set

• Adjudication Method: Consensus ground truth was established when at least 2 of the 3 expert epileptologists agreed on the presence or absence of an electrographic seizure event (2+1).

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

• No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not reported in this document. The study focuses on the standalone performance of the AI algorithm.

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

• Yes, a standalone performance study was done. The "Clinical Validation" section explicitly details the algorithm's performance (Sensitivity and False Alarm Rate) against a clinical reference standard, with no mention of human interaction with the AI output during this performance assessment. Notifications are "intended to be used by qualified clinicians," but the validation itself is on the algorithm's detection capabilities.

7. The Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. The ground truth was established by a panel of 3 independent expert epileptologists reviewing "standard 19+channel wired 10-20 montage EEG records."

8. The Sample Size for the Training Set

• Training Set Sample Size: 117 patient records.
  • 82 patient records with seizures ("Train Sz").
  • 35 patient records without seizures ("Train No-Sz").

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

• The document states that EEG data was used to "train the REMI-AI RDM algorithm to identify potential electrographic seizure events." While it explicitly describes how the ground truth for the validation data set was established (panel review by experts), it does not explicitly detail the exact method for establishing ground truth for the entire training set. However, given the context of the validation process, it is highly probable that a similar expert review and consensus process would have been used to annotate the training data as well.

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The REMI-AI Discrete Detection Module (REMI-AI DDM) is indicated for the analysis of REMI Remote EEG Monitoring System electroencephalogram (EEG) recordings. REMI-AI DDM is intended to be used by physicians qualified to analyze and interpret EEG who will exercise professional judgment in using the information.

As an aide to the qualified physician's REMI EEG review, REMI-AI DDM marks previously acquired sections of REMI EEG that may correspond to neurological events of interest indicative of potential electrographic seizures lasting at least 10 seconds in duration. REMI-AI DDM is indicated for use with adult and pediatric patients (6+ years).

REMI-AI DDM does not mark REMI EEG records in real time and does not provide any diagnostic conclusion about the patient's condition to the user.

REMI-Al Discrete Detection Module (REMI-AI DDM) is a software as a medical device (SaMD) that automatically identifies and annotates discrete seizure-like events in previously acquired electroencephalography (EEG) traces to aid a qualified physician in their review of REMI EEG records. REMI-AI DDM analyzes previously acquired EEG data from 4-channel recordings obtained from bilateral, bipolar scalp EEG recordings at both the frontal and temporoparietal regions, collected and stored by the REMI Remote EEG Monitoring System. REMI-AI DDM analyzes EEG recordings and detects regions of the data that may correspond to electrographic seizures lasting at least 10 seconds in duration. These regions are annotated in the REMI EEG file as discrete events and are provided to assist in REMI EEG review.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

1. Test Set Sample Size and Data Provenance:

   • Sample Size: 50 patient records (31 with consensus-determined electrographic seizures, 19 with no consensus-determined electrographic seizures). This included 87 consensus-determined electrographic seizures lasting at least 10 seconds.
   • Data Provenance: Not explicitly stated (e.g., country of origin). However, the data was collected from patients wearing REMI wireless EEG sensors alongside standard-of-care 19-channel, full-montage, video-EEG in Epilepsy Monitoring Units (EMUs) or during at-home ambulatory EEG monitoring. The study design suggests retrospective use of this collected data for validation.

2. Number of Experts and Qualifications for Ground Truth (Test Set):

   • Number of Experts: 3 independent expert epileptologists for panel review, selected from a panel of 6.
   • Qualifications: Holding certification by the American Board of Psychiatry and Neurology or certification by the American Board of Clinical Neurophysiology with Special Competency in Epilepsy Monitoring.

3. Adjudication Method for the Test Set:

   • Method: Consensus ground truth was established using 2 out of 3 (2+1, if referring to a single round of review among three) members identifying the presence or absence of an electrographic seizure event from the wired EEG records.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No, a MRMC comparative effectiveness study was not explicitly done or reported in the provided text to show the effect size of human readers improving with AI vs. without AI assistance. The clinical validation focuses on the standalone performance of the AI algorithm.

5. Standalone Performance (Algorithm Only):

   • Yes, a standalone (algorithm only without human-in-the-loop) performance study was done. The reported Event-Level Sensitivity and False Alarm Rate (FAR) are direct measures of the REMI-AI DDM algorithm's performance in identifying electrographic seizures.

6. Type of Ground Truth Used:

   • Expert Consensus: The ground truth for both training and validation was established through panel review by independent expert epileptologists based on standard 19+channel wired 10-20 montage EEG records acquired concurrently with the REMI 4-channel EEG.

7. Training Set Sample Size:

   • 108 patient records (73 seizure patients, 35 non-seizure patients).
   • This included a total of 562 electrographic seizures used for training.

8. How Ground Truth for Training Set Was Established:

   • Similar to the validation set, the ground truth for the training set was established by panel review of standard 19+channel wired 10-20 montage EEG records concurrently acquired with REMI 4-channel EEG. The same criteria of consensus (at least 2 of 3 experts) identifying the presence or absence of an electrographic seizure event would apply, as described in the "Clinical Reference" section for generating a reference standard for REMI-AI DDM.

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The REMI Remote EEG Monitoring System is indicated for use in healthcare settings where near real-time and/or remote EEG is warranted and in ambulatory settings where remote EEG is warranted. REMI uses single patient, disposable, wearable sensors intended to amplify, capture, and wirelessly transmit a single channel of electrical activity of the brain for a duration up to 30 days.

The REMI System uses the REMI-Mobile software application that runs on qualified portable general purpose computing platforms. REMI-Mobile displays user setup information to trained medical professionals and provides notifications to medical professionals and ambulatory users. REMI-Mobile receives and transmits data from connected REMI Sensors to the secure REMI-Cloud where it is stored and prepared for review on qualified EEG viewing software.

REMI does not make any diagnostic conclusion about the subject's condition and is intended as a physiological signal monitor. The REMI System is indicated for use with adult and pediatric patients (6+ years).

The REMI System has three major components:

1. REMI Sensor A disposable EEG sensor which is placed on the patient's scalp using a conductive REMI Sticker
2. REMI Mobile A mobile medical application that is designed to run on a qualified commercial-off-the-shelf mobile computing platform (an Android tablet for use in healthcare settings, and a portable/wearable Android smartwatch for use in ambulatory settings), acquire EEG data transmitted from REMI Sensors and then transmit the EEG data and associated patient information via wireless encrypted transmission to,
3. REMI Cloud A HIPAA-compliant secure cloud storage and data processing platform where data is processed into a qualified EEG reviewing software format for neurological review.

This 510(k) submission includes the addition of the Android smartwatch for ambulatory use and increases the duration of monitoring to up to 30 days.

The provided text describes the REMI Remote EEG Monitoring System and its substantial equivalence to a predicate device. However, it does not include specific quantitative acceptance criteria or detailed study results that would typically be associated with performance metrics like sensitivity, specificity, accuracy, or effect sizes for AI assistance. The document focuses on demonstrating substantial equivalence through testing of electrical safety, wireless technology, software, and human factors.

Here's an attempt to answer your questions based on the available information, with acknowledgements where information is missing.

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

Based on the provided text, the acceptance criteria are generally framed around meeting regulatory standards and functional requirements rather than quantitative performance metrics for diagnostic accuracy.

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 does not specify sample sizes for any of the described tests. It mentions "testing conducted," "accelerated aging," "ship testing," and "human factors/usability testing," but provides no details on the number of units, subjects, or data points involved. Similarly, data provenance (country of origin, retrospective/prospective) is not mentioned.

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)

The document does not describe any study establishing ground truth with expert review for a diagnostic purpose. The device is explicitly stated to "not make any diagnostic conclusion" and is "intended as a physiological signal monitor." Therefore, this question is not applicable in the context of the provided information.

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

Since no expert-based ground truth establishment or diagnostic performance evaluation is detailed, there is no mention of an adjudication method.

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 is mentioned. The device is a physiological signal monitor and does not involve AI assistance for human readers in a diagnostic context.

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

The device itself is a system for acquiring and transmitting EEG data for review by medical professionals on qualified EEG viewing software. It does not perform standalone diagnostic algorithms. Its "Essential Performance" is to record digitized EEG data and transfer it.

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

Given that the device is a physiological signal monitor and "does not make any diagnostic conclusion," the concept of "ground truth" as typically used for diagnostic or screening devices (e.g., pathology, expert consensus on a disease state) is not applicable here. The ground truth for its performance would be the accuracy of EEG signal acquisition and transmission, which is assessed through bench testing and compliance with electrical standards.

8. The sample size for the training set

The document does not describe any machine learning or AI-based component that would require a "training set." The software updates mentioned are for supporting new hardware (smartwatch) and extending monitoring duration, not for developing new diagnostic algorithms.

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

Not applicable, as no training set for an AI/ML algorithm is described.

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The Remi Robotic Navigation System is intended for use as an aid for precisely locating anatomical structures and for the spatial positioning and orientation of a tool holder or guide tube to be used by surgeons for navigating and/or guiding compatible surgical instruments in open or percutaneous spinal procedures in reference to rigid patient anatomy and fiducials that can be identified on a 3D imaging scan or 2D fluoroscopic images. The Remi Robotic Navigation System is indicated for assisting the surgeon in placing pedicle screws in the posterior lumbar region (L1-S1). The system is designed for lumbar pedicle screw placement with the prone position and is compatible with the Accelus LineSider Spinal System.

The Remi Robotic Navigation System (Remi) is an image guided system primarily comprised of a computer workstation, software, a trajectory system, including a targeting platform, a camera, and various image guided instruments intended for assisting the surgeon in placing screws in the pedicles of the lumbar spine. The system operates in a similar manner to other optical-based image y systems.

The provided text describes a 510(k) premarket notification for the Remi Robotic Navigation System (K223350). The submission focuses on demonstrating substantial equivalence to its predicate devices, particularly an earlier version of the Remi Robotic Navigation System (K223070) and the EXCELSIUS GPS (K171651). The key change in the subject device is the addition of compatibility with 2D fluoroscopic imaging systems for pedicle screw placement in the posterior lumbar region (L1-S1).

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 present a table of acceptance criteria with corresponding performance metrics for the tested device. It broadly states that "The testing shows that the use of the 2D fluoroscopic images with the Remi system is equivalent to the use of the validated 3D imaging systems." and that the device "continues to meet design requirements, is as safe and effective as the predicate device, and performs according to its intended use."

However, the "POSITIONING ACCURACY (BENCH)" for the Primary Predicate Device is listed as: 0.74 ± 0.36 mm (worst case); 95% CI: 1.46mm (worst case). Since the subject device "Same as Primary Predicate" for this characteristic and the testing was done to demonstrate equivalence, it can be inferred that the acceptance criterion for accuracy for the new functionality is to maintain this level of accuracy or be equivalent to it.

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

The document mentions "Performance Testing - Bench" was conducted, including "Navigation Accuracy Verification," "System Accuracy Validation," "Software System Test," "ASTM F2554 Accuracy Test," and "Software Unit and Integration Tests." However, specific sample sizes for these tests (e.g., number of cases, images, or measurements) are not provided.

The data provenance is not explicitly stated in terms of country of origin or whether it was retrospective or prospective. Given that it is bench testing, it is likely that the data was generated in a controlled laboratory or testing environment rather than being derived from patient cases.

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

This information is not provided. The document describes bench testing, which typically uses defined physical standards or simulated scenarios to establish ground truth rather than expert consensus on clinical images/data.

4. Adjudication Method for the Test Set

This information is not provided. Adjudication methods (like 2+1, 3+1) are typically used in studies involving human interpretation of clinical data and subsequent consensus determination. Since the testing described is bench testing, such an adjudication method is unlikely to apply.

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

No MRMC comparative effectiveness study is mentioned. The submission focuses on demonstrating substantial equivalence through bench testing, verifying that the added 2D fluoroscopy functionality maintains the device's accuracy and safety profiles compared to its predicate with 3D imaging. There is no information regarding human readers improving with or without AI assistance.

6. Standalone Performance Study

Yes, a standalone (algorithm only) performance study was done. The performance testing described (Navigation Accuracy Verification, System Accuracy Validation, Software System Test, ASTM F2554 Accuracy Test, Software Unit and Integration Tests) are all characteristics of a standalone performance evaluation, focusing on the device's technical capabilities without human interaction determining performance outcomes within the tests themselves. The stated purpose was "to demonstrate that the updated requirement for this change was met and to ensure the risk profile of Remi was maintained," specifically concerning "the use of the 2D fluoroscopic images with the Remi system is equivalent to the use of the validated 3D imaging systems."

7. Type of Ground Truth Used

The ground truth for the bench testing would likely involve engineered physical phantoms, precisely manufactured test fixtures, and controlled experimental setups with known geometric parameters and validated measurements. For example, for "Navigation Accuracy Verification" and "System Accuracy Validation," physical measurements against a known standard or calibrated instruments would establish the ground truth. For "Software System Test" and "Software Unit and Integration Tests," the ground truth would be defined by the software's specified functional requirements and expected outputs.

8. Sample Size for the Training Set

The document does not provide information about a training set or its sample size. The submission is for a modification to an existing cleared device, specifically adding 2D fluoroscopic image compatibility. While the software was updated to support this, including an algorithm correcting distortion, the text implies that the evaluation focused on the performance of the system with the new capability rather than the development of a completely new AI algorithm requiring extensive training data. If machine learning was used for the distortion correction, details about its training would typically be in a separate section not provided here.

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

As no training set is explicitly mentioned, this information is not provided.

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The REMI™ Robotic Navigation System is intended for use as an aid for precisely locating anatomical structures and for the spatial positioning and orientation of a tool holder or guide tube to be used by surgeons for navigating and/or guiding compatible surgical instruments in open or percutaneous spinal procedures in reference to rigid patient anatomy and fiducials that can be identified on a 3D imaging scan. The REMI™ Robotic Navigation System is indicated for assisting the surgeon in placing pedicle screws in the posterior lumbar region (LI-S1). The system is designed for lumbar pedicle screw placement with the prone position and is compatible with the Accelus LineSider® Spinal System.

The Remi Robotic Navigation System (Remi) is an image guided system primarily comprised of a computer workstation, software, a trajectory system, including a targeting platform, a camera, and various image guided instruments intended for assisting the surgeon in placing screws in the pedicles of the lumbar spine. The system operates in a similar manner to other optical-based image y systems.

The provided text outlines the FDA 510(k) clearance for the REMI Robotic Navigation System, focusing on its substantial equivalence to a predicate device. However, it does not contain a detailed study report or explicit acceptance criteria with reported device performance metrics in the format requested.

The document primarily focuses on demonstrating that the updated REMI system, with additional compatible 3D imaging systems, is substantially equivalent to its predicate. The "Performance Testing - Bench" section mentions tests conducted but does not provide specific numerical acceptance criteria or performance results.

Therefore, much of the requested information cannot be extracted directly from the provided text. I will indicate where information is missing or inferred.

Acceptance Criteria and Device Performance

Study Details from the provided text:

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

   • The document mentions "Performance Testing - Bench" and "Verification and validation testing" but does not specify the sample size for any test set or the data provenance (e.g., country of origin, retrospective/prospective). It suggests bench testing was primarily used for equivalence.

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

   • No information provided. The "performance testing" described appears to be technical validation against specified equivalence factors rather than expert review of clinical outcomes or images.

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

   • No information provided.

4. 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 study was not described. This device is a robotic navigation system for spinal surgery, not an AI-assisted diagnostic imaging interpretation tool that would typically involve human readers. Its purpose is to aid surgeons in pedicle screw placement.

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

   • The document does not explicitly describe a "standalone" algorithmic performance test in the context of an AI-only system. The device is a navigation system that guides a human surgeon. Its performance metrics, like accuracy, are inherently tied to the system's ability to guide to a planned trajectory, which can be measured quantitatively in bench tests. The bench testing mentioned covers aspects like "Accuracy," "Image Quality," "Image Transfer Speed," and "Image Registration Speed."

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

   • Based on the description of "Performance Testing - Bench" and "Accuracy verification on anatomical landmarks" (for the predicate), the ground truth for accuracy testing would typically involve precisely measured physical points or targets on a phantom or model, measured by a highly accurate reference system (e.g., CMM). For image quality, transfer, and registration speed, the ground truth would be objectively defined technical specifications or measurements.

7. The sample size for the training set:

   • No information provided about a "training set." The REMI system is a robotic navigation system, not described as a deep learning or machine learning-based algorithm that typically requires a large training dataset for model development. The system uses pre-programmed logic, image processing, and control algorithms.

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

   • Not applicable, as no training set for an AI/ML model is mentioned.

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The REMI Platform is intended to be used in healthcare settings where near real-time and/or remote EEG is warranted. REMI consists of Epilog disposable Sensors - a single patient, disposable, wearable sensor intended to amplify, capture, and wirelessly transmit a single channel of electrical activity of the brain for up to 48 hours. The REM-Mobile software and REMI-Tablet are intended to receive and transmit data from four Epilog Sensors to secure cloud storage for subsequent viewing and reviewing of EEG on third-party software.

REMI does not make any diagnosis or recommendations and is intended only as a physiological signal monitor. Epilog Sensors are intended for use by trained medical professional healthcare facility environment.

Epilog Sensors are intended for use with adult and pediatric patients (6+). (Rx only).

REMI amplifies the electroencephalogram (EEG) from a patient's scalp. After amplification, the EEG are sent to the REMI-Tablet running the REMI-Mobile Application. REMI-Mobile combines the EEG from four Epilog Sensors and patient information and relays the data to a cloud server running Persyst software. The EEG data is accessible through the Persyst Mobile interface. REMI is designed for use with adult and pediatric patients (6+). The user interface for the REMI-Tablet is an 10" LCD touchscreen display.

The user interface for Epilog Sensors is a single button kevpad overmolded in each Sensor, REMI-Tablet power is through A/C adapter as well as limited onboard rechargeable battery. Epilog Sensor power is through a single-use primary coin cell. Using its wireless link, the Epilog Sensors can exchange EEG data and commands with the REMI-Mobile application running on the REMI-Tablet.

REMI has three major components:

1. Epilog-D disposable EEG sensors,

2. REMI-Mobile - mobile OS application designed to run on a medical-grade tablet, acquire EEG data transmitted from Epilog devices along with user-entered patient and device information, and then transmit the EEG data and patient/device information via wireless encrypted WiFi to.

3. REMI-Cloud – A HIPAA-compliant cloud storage and data processing platform where data is processed into a format that a FDA-cleared (K171184) EEG reviewing software called Persyst can use, which will allow remote neurological review.

The firm Epitel, Inc. did not conduct a clinical study to prove the device meets acceptance criteria. Instead, they performed non-clinical performance testing and biocompatibility testing. The device is a physiological signal monitor and does not make diagnoses or recommendations. Therefore, the information provided below is a summary of the non-clinical and biocompatibility tests performed.

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

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

This section is not applicable as no clinical study was conducted. The performance data is based on non-clinical and biocompatibility testing. The document does not specify general sample sizes (e.g., number of devices tested) for the non-clinical or biocompatibility tests, nor does it specify data provenance in terms applicable to clinical studies.

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 section is not applicable as no clinical study was conducted that required ground truth established by experts.

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

This section is not applicable as no clinical study was conducted.

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

This section is not applicable as no clinical study, particularly an MRMC study, was conducted. The device is a physiological signal monitor and does not include AI for interpretation or diagnosis.

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

This section is not applicable as the device is a physiological signal monitor and does not involve a standalone algorithm for diagnostic performance. The submission explicitly states, "REMI does not make any diagnosis or recommendations and is intended only as a physiological signal monitor."

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

This section is not applicable as no clinical study requiring a ground truth was performed. The non-clinical and biocompatibility testing rely on established standards and laboratory results.

8. The sample size for the training set

This section is not applicable, as no machine learning algorithm requiring a training set for diagnostic or interpretative purposes was mentioned or evaluated in the context of device performance.

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

This section is not applicable, as no machine learning algorithm requiring a training set was mentioned or evaluated.

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