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NX01 is intended for use in healthcare or home settings to acquire, record, and transmit electrical activity of the brain by placing non-invasive electrodes in the ears of patients. It acquires, records and transmits one channel of electroencephalogram (EEG) data. The medical use of data acquired by NX01 is to be performed under the direction and interpretation of a licensed medical professional. NX01 does not provide any diagnostic conclusions about the patient's condition.

The NX01 is intended for use with adult and pediatric patients (6+).

NX01 is a wearable device for continuous recording of non-invasive EEG signals in healthcare and home settings. NX01 is intended to be prescribed by a trained healthcare professional. It consists of a pair of earbuds (one per ear) integrating a pair of active, dry electrodes, connected by a cable to a command panel. This command panel houses the battery, the internal memory to store the data, the main acquisition unit with function buttons and LEDs which display the device's status.

The NX01 solution is composed of:

• Two earbuds (1) connected by 45 cm cables, to a command panel (5). This command panel houses the battery, the internal memory to store the data, the main acquisition unit with function buttons (2 - Left button and 3 - Right button) and LEDs (4) which display the device's status.
• A set of eartips, to be placed on the earbuds for the recording. Eartips are single use consumable that should be discarded and replaced for every recording that takes place.
• A medical grade PSU with the following specifications: Input AC 100-240V, 50/60Hz; Output USB-C DC 5.0V, min 1.0A; Compliance IEC 60601-1, IEC 62368-1 or IEC 60950-1.

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The Ceribell Infant Seizure Detection Software is intended to mark previously acquired sections of EEG recordings in newborns (defined as preterm or term neonates of 25-44 weeks postmenstrual age) and infants less than 1 year of age that may correspond to electrographic seizures in order to assist qualified clinical practitioners in the assessment of EEG traces. The Seizure Detection Software also provides notifications to the user when detected seizure prevalence is "Frequent", "Abundant", or "Continuous", per the definitions of the American Clinical Neurophysiology Society Guideline 14. Delays of up to several minutes can occur between the beginning of a seizure and when the Seizure Detection notifications will be shown to a user.

The Ceribell Infant Seizure Detection Software does not provide any diagnostic conclusion about the subject's condition and Seizure Detection notifications cannot be used as a substitute for real time monitoring of the underlying EEG by a trained expert.

The Ceribell Infant Seizure Detection Software is a software-only device that is intended to mark previously acquired sections of EEG recordings that may correspond to electrographic seizures in order to assist qualified clinical practitioners in the assessment of EEG traces.

Ceribell Infant Seizure Detection Software: Acceptance Criteria and Supporting Study

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: 713 patients.
  • 25-36 weeks PMA: 155 patients
  • 37-44 weeks PMA: 321 patients

  • 44 weeks PMA: 237 patients

• Data Provenance: The EEG recordings were obtained from patients less than 1 year of age who received continuous EEG monitoring within the hospital environment. The study was retrospective. The country of origin is not explicitly stated in the provided text.

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

• Number of Experts: 3
• Qualifications of Experts: Expert pediatric neurologists who were fellowship-trained in epilepsy or clinical neurophysiology.

4. Adjudication Method for the Test Set

• Adjudication Method: A two-thirds majority agreement among the 3 expert pediatric neurologists was required to form a determination of seizures, establishing the reference standard for the test set.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. The study focused on the standalone performance of the algorithm against an expert-adjudicated ground truth.

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

• Yes, a standalone performance study was done. The performance metrics (PPA and FP/hr) were evaluated for the Ceribell Infant Seizure Detection Software algorithm without human intervention in the detection process. The reviewing neurologists for ground truth establishment were explicitly blinded to the software's output.

7. The Type of Ground Truth Used

• Type of Ground Truth: Expert consensus (adjudication by a panel of 3 expert pediatric neurologists).

8. The Sample Size for the Training Set

• The sample size for the training set is not provided in the document. The document states, "Importantly, none of the data in the validation dataset were used for training of the Seizure Detection algorithm; the validation dataset is completely independent."

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 only mentions that the validation dataset was independent and not used for training.

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DeepRESP is an aid in the diagnosis of various sleep disorders where subjects are often evaluated during the initiation or follow-up of treatment of various sleep disorders. The recordings to be analyzed by DeepRESP can be performed in a hospital, a patient's home, or an ambulatory setting. It is indicated for use with adults (18 years and above) in a clinical environment by or on the order of a medical professional.

DeepRESP is intended to mark sleep study signals to aid in the identification of events and annotations of traces; automatically calculate measures obtained from recorded signals (e.g., magnitude, time, frequency, and statistical measures of marked events); and infer sleep staging with arousals with EEG and in the absence of EEG. All output is subject to verification by a medical professional.

DeepRESP is a cloud-based software as a medical device (SaMD), designed to perform analysis of sleep study recordings, with and without EEG signals, providing data for the assessment and diagnosis of sleep-related disorders. Its algorithmic framework provides the derivation of sleep staging, including arousals, scoring of respiratory events, and key parameters such as the Apnea-Hypopnea Index (AHI) and Central Apnea-Hypopnea Index (CAHI).

DeepRESP (K252330) is hosted on a serverless stack. It consists of:

• A web Application Programming Interface (API) intended to interface with a third-party client application, allowing medical professionals to access DeepRESP's analytical capabilities.
• Predefined sequences called Protocols that run data analyses, including artificial intelligence and rule-based models for the scoring of sleep studies, and a parameter calculation service.
• A Result storage using an object storage service to temporarily store outputs from the DeepRESP Protocols.

Here's a breakdown of the acceptance criteria and study details for DeepRESP, based on the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state pre-defined acceptance criteria (e.g., "DeepRESP must achieve AHI ≥ 5 PPA% of at least X%"). Instead, it reports the performance of the device and compares it to predicate devices to demonstrate substantial equivalence and non-inferiority. The "Observed paired differences" columns, particularly those where the confidence interval does not cross zero, imply a comparison to show that the new DeepRESP v2.0 performs at least as well as the previous version or the additional predicate.

For the purpose of this analysis, I will present the reported performance of the Subject Device (DeepRESP v2.0) as the "reported device performance." Since no explicit acceptance criteria thresholds are given, the comparison to predicates and the demonstration of non-inferiority served as the implicit acceptance path for the FDA clearance.

Reported Device Performance (DeepRESP v2.0)

PPA%: Positive Percent Agreement, NPA%: Negative Percent Agreement, OPA%: Overall Percent Agreement.

2. Sample Sizes and Data Provenance

The clinical validation was conducted using retrospective data.

• Test Set Sample Size:

  • Type I/II Scoring Validation: 4,030 PSG recordings
  • Type III Scoring Validation: 5,771 sleep recordings
    • This comprised 4,037 Type I recordings and 1,734 Type II recordings, processed as Type III by using only the relevant subset of signals.

• Data Provenance:

  • Country of Origin: United States.
  • Data Type: Manually scored sleep recordings from sleep clinics, collected as part of routine clinical work for patients suspected of suffering from sleep disorders.
  • Settings: Urban, suburban, and rural areas.
  • Demographics: Included individuals in all age groups (18-21, 22-35, 36-45, 46-55, 56-65, >65) and all BMI groups (<25, 25-30, <30). The recording collection for Type I/II scoring consisted of 44% Females, and for Type III scoring, 35% Females. High level of race/ethnicity diversity (Caucasian or White, Black or African American, Other).

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated. The document refers to "manually scored sleep recordings" and "medical professional" for verification. It also mentions "board-certified sleep physicians" in the context of the training set. However, the specific number of experts used to establish the ground truth for the test set is not detailed.
• Qualifications of Experts: For the test set, it's implied that "medical professionals" performed the manual scoring, as the data originated from "routine clinical work." For the training set, "board-certified sleep physicians" were involved in establishing the ground truth.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1) for establishing the ground truth on the test set. It mentions "manually scored sleep recordings" but does not detail how potential disagreements between multiple scorers (if any were used) were resolved.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done.

The study design was a retrospective data study comparing the automatic scoring of DeepRESP to manual scoring (ground truth) and also comparing DeepRESP's performance to two predicate devices (DeepRESP K241960 and Nox Sleep System K192469). This is a standalone performance evaluation against expert-derived ground truth, with a direct comparison to existing automated systems, not an MRMC study assessing human reader improvement with AI assistance.

6. Standalone Performance Study

Yes, a standalone performance study was done. The reported PPA, NPA, and OPA values for DeepRESP v2.0 (Subject Device) represent its performance as a standalone algorithm without human-in-the-loop assistance. The subsequent comparison to the predicate devices also evaluated their standalone performance. The document explicitly states: "All output is subject to verification by a medical professional," indicating that while the device is intended to aid in diagnosis, its performance evaluation was conducted on its automated output before any human review.

7. Type of Ground Truth Used

The ground truth used was expert consensus (manual scoring). The study used "manually scored sleep recordings" from "routine clinical work" as the reference standard against which DeepRESP's automatic scoring was compared.

8. Sample Size for the Training Set

The document does not report the sample size used for the training set. It only states the sample sizes for the validation (test) sets: 4,030 PSG recordings for Type I/II validation and 5,771 sleep recordings for Type III validation.

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

The document states that the ground truth for the training set "was established through a rigorous process involving multiple board-certified sleep physicians." This implies an expert-driven process, likely involving consensus or reconciliation among several highly qualified professionals. However, the exact methodology (e.g., number of physicians, adjudication rules) is not detailed beyond "rigorous process."

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1. Persyst 15 EEG Review and Analysis Software is intended for the review, monitoring and analysis of EEG recordings made by electroencephalogram (EEG) devices to aid neurologists in the assessment of EEG. This device is intended to be used by qualified medical practitioners who will exercise professional judgment in using the information.

2. The Seizure Detection and Seizure Probability component of Persyst 15 is intended to mark previously acquired sections of the adult (greater than or equal to 18 years) EEG recordings that may correspond to electrographic seizures, in order to assist qualified clinical practitioners in the assessment of EEG traces. EEG recordings should be obtained with a full scalp montage according to the standard 10/20 system. Alternatively, the Seizure Detection can operate using reduced set of electrodes including Fp1, F7, T3, T5, O1, Fp2, F8, T4, T6, O2, but will have decreased sensitivity for seizures due to its limited spatial sampling.

3. The Persyst software's Electrographic Status Epilepticus (ESE) component is indicated for the diagnosis of Electrographic Status Epilepticus in patients greater than or equal to 18 years of age who are at risk for seizure. The (ESE) Component analyzes EEG waveforms and identifies patterns that may be consistent with electrographic status epilepticus as defined in the "(American Clinical Neurophysiology Society's Guideline 14)". EEG recordings used with this feature should be obtained with a full scalp montage (10/20 system) or a reduced set of electrodes (Fp1, F7, T3, T5, O1, Fp2, F8, T4, T6, O2). Using the reduced set of electrodes will result in some decrease in sensitivity and specificity for the detection of ESE in comparison to the full montage due to decreased spatial sampling.

   The (ESE) Component is intended to be used as an aid for determining patient treatment in acute-care environments. Detections from the (ESE) Component provide one input for the clinician that is intended to be used in conjunction with other elements of clinical practice to determine the appropriate treatment course for the patient. The (ESE) Component is intended for detection of electrographic status epilepticus only. The (ESE) Component does not substitute for the review of the underlying EEG by a qualified clinician with respect to any other types of pathological EEG patterns. The (ESE) Component is not intended for use in Epilepsy Monitoring Units or non-acute care environments.

4. The Neonatal Seizure Detection component of Persyst 15 is intended to mark previously acquired sections of neonatal patients' (defined as near-term or term neonates of conceptional age between 36 and 44 weeks and less than two weeks of chronologic age) EEG recordings that may correspond to electrographic seizures, in order to assist qualified clinical practitioners in the assessment of EEG traces. EEG recordings should be obtained with scalp-recorded EEG using the standard International 10-20 system electrode placement, modified for neonates (this includes electrode sites Fp1/2 or alternate F1/2, C3/4, T3/4, O1/2, and Cz, optionally including Fz). Alternatively, the Neonatal Seizure Detection component can operate using a more reduced set of electrodes including C3/4, Fp1/2 (F1/2), and O1/2 (recorded in such a manner to allow creation of montage C3-4, Fp1-O1, Fp2-O2), or an even more simplified electrode set including only C3/4 and Cz (arranged as C3-Cz and C4-Cz), but the three-electrode montage will have decreased sensitivity for seizures due to its limited spatial sampling.

5. The Spike Detection component of Persyst 15 is intended to mark previously acquired sections of the patient's EEG recordings that may correspond to spikes, in order to assist qualified clinical practitioners in the assessment of EEG traces. The Spike Detection component is intended to be used in patients at least one month old. Persyst 15 Spike Detection performance has not been assessed for intracranial recordings.

6. Persyst 15 EEG Review and Analysis Software includes the Persyst Imaging Workflow (PIW), an imaging viewer. It is intended for use by qualified clinical practitioners on both adult and pediatric subjects at least 12 years of age to interpret EEG data in conjunction with any type of neuroimaging including magnetic resonance imaging (MRI) or computed tomography scans (CT). Persyst Imaging Workflow is not intended to provide diagnostic information.

7. The Persyst ESI component of Persyst 15 is intended for use by a trained/qualified EEG technologist or physician on both adult and pediatric subjects at least 3 years of age for the visualization of human brain function by fusing a variety of EEG information with rendered images of an individualized head model and an individualized MRI image.

8. The Persyst 15 sleep state feature provides the user with output concerning wake-sleep states (wake or sleep,) present in an EEG recording as an aid in assessing which states are present and when they are present. The EEG being assessed for sleep state should utilize standard 10-20 system electrode recording positions and contain the expected EEG patterns of typical wake and sleep, with no major persistent pathological alterations. The sleep state output is subject to user confirmation via EEG waveform review and is not intended for the diagnosis of sleep disorders (e.g.: sleep apnea, narcolepsy, restless leg syndrome). The sleep state feature is intended for adult and pediatric subjects at least 13 years and older.

9. Persyst 15 includes the calculation and display of a set of quantitative measures intended to monitor and analyze the EEG waveform. These include FFT, Rhythmicity, Peak Envelope, Artifact Intensity, Amplitude, Relative Symmetry, and Suppression Ratio. Automatic event marking is not applicable to quantitative measures. These quantitative EEG measures should always be interpreted in conjunction with review of the original EEG waveforms.

10. Persyst 15 displays physiological signals, including the calculation and display of a heart rate measurement based on the ECG channel in the EEG recording, which are intended to aid in the analysis of an EEG. Heart rate measurement of Persyst 15 is not applicable to patients with pacemaker and/or active implantable devices.

11. The aEEG functionality included in Persyst 15 is intended to monitor the state of the brain. The automated event marking function of Persyst 15 is not applicable to aEEG.

12. Persyst 15 provides notifications for seizure detection, quantitative EEG and aEEG that can be used when processing a record during acquisition. These include an on screen display and the optional sending of an email message. Delays of up to several minutes can occur between the beginning of a seizure and when the Persyst 15 notifications will be shown to a user. Persyst 15 notifications cannot be used as a substitute for real time monitoring of the underlying EEG by a trained expert.

13. Persyst 15 AR (Artifact Reduction) is intended to reduce EMG, eye movement, and electrode artifacts in a standard 10-20 EEG recording. AR does not remove the entire artifact signal, and is not effective for other types of artifacts. AR may modify portions of waveforms representing cerebral activity. Waveforms must still be read by a qualified medical practitioner trained in recognizing artifact, and any interpretation or diagnosis must be made with reference to the original waveforms.

14. This device does not provide any diagnostic conclusion about the patient's condition to the user.

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EEG-1260A Neurofax System
The EEG-1260A series Neurofax is intended to record, measure and display cerebral and extracerebral activity for EEG and Sleep Studies. These data may be used by the clinician in sleep disorders, epilepsies and other related disorders as an aid in diagnosis. The device is intended for use by medical personnel in any location within a medical facility, laboratory, clinic, or nursing home or outside of a medical facility under direct supervision of a medical professional.

JE-940A EEG Amplifier Unit
The EEG amplifier unit is intended to acquire and record the EEG and other biological signals (ECG, EMG, respiration, EOG, snoring, body position, SpO2, pulse waveform) of a patient, and transmit the measurement data to the electroencephalograph. The transmitted measurement data is displayed on the electroencephalograph screen and provides information to evaluate the functional state of the brain, brain-related diseases and disorders, and sleep disorders.

The device is intended for use by qualified medical personnel within a medical facility, or by staff in an equivalent facility under the direct supervision of qualified medical personnel.

The device is available for use on any patient as determined by qualified medical personnel.

LS-940A Photic Light
The photic stimulator is a light source for photic stimulation to confirm the responsiveness of EEG to photic stimulation during EEG tests and to test visual evoked potentials.

The device is intended for use by qualified medical personnel within a medical facility, or by staff in an equivalent facility under the direct supervision of qualified medical personnel.

The device is available for use on any patient as determined by qualified medical personnel.

The EEG-1260A Neurofax is an electroencephalograph system specifically designed for use in healthcare facilities. This device is designed to measure and display the patient's electroencephalogram (EEG) and polysomnography (PSG) signals, providing information and analysis of brain electrical activity.

The JE-940A EEG amplifier unit is a new amplifier unit and is an input unit of the EEG-1260A. The JE-940A Amplifier unit acquires and measures EEG and other polysomnography signals (ECG, EMG, respiration, EOG, snoring, body position, SpO2, and pulse waveforms) associated with EEG/PSG testing, and transmits the acquired data to the EEG-1260A Neurofax. The JE940A operates on AC power or on battery power for mobile EEG measurements. The JE-940A offers an option to connect with the JE-944A Mini electrode junction box, which enhances the operational efficiency and mobility in EEG measurements.

The LS-940A Photic stimulator is a device which provides visual stimuli in the form of flashing light and is used to assess a patient's EEG responses to light stimulation. The parameters for flashing the light signal are controlled by the EEG-1260A Neurofax.

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The SignalNED Device is intended to record and store EEG signals for the statistical evaluation of the human electroencephalogram (EEG) and display Quantitative EEG (qEEG) measures intended to help the user analyze the EEG. These measures include relative band power (e.g., alpha, beta, delta, theta) and band power asymmetry (displayed as a z-score compared to a normative database). The SignalNED does not provide any diagnostic conclusion about the patient's condition. The device is intended to be used on adults by qualified medical and clinical professionals.

The SignalNED Model RE machine uses 10 patient electrodes (4 left, 4 right, 2 midline), which are used to form the 8 channels. The SignalNED machine requires the use of the SignalNED Sensor Cap, and the system includes the following components:

• Portable EEG machine (Device)
• Battery & External Battery Charger
• SignalNED Sensor Cap
• SignalNED Sensor Cap Cable

The primary function of the SignalNED Model RE is to rapidly record EEG and derive Quantitative EEG (qEEG) measurements. These measurements include Relative Band Power for multiple bands (e.g., alpha, beta, delta, theta) at each electrode and band power asymmetry (displayed as a z-score compared to a normative database). These measurements are intended to help the user analyze the underlying EEG. The SignalNED Model RE (client) achieves its intended without relying on wireless connectivity. The SignalNED RE does not provide any diagnostic conclusion about the patient's condition.

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The PVDF Effort Sensor is intended to measure and output respiratory effort signals from a patient for archival in a sleep study. The sensor is an accessory to a polysomnography system which records and conditions the physiological signals for analysis and display, such that the data may be analyzed by a qualified sleep clinician to aid in the diagnosis of sleep disorders.

The PVDF Effort Sensor is intended for use on both adults and children by healthcare professionals within a hospital, laboratory, clinic, or nursing home, or outside of a medical facility under the direction of a medical professional.

The PVDF Effort Sensor does not include or trigger alarms, and is not intended to be used alone as, or a critical component of,

• an alarm or alarm system;
• an apnea monitor or apnea monitoring system; or
• life monitor or life monitoring system.

The PV01 PVDF Effort Sensor is a respiratory effort monitoring accessory designed for use during sleep studies to assess breathing patterns by measuring chest and abdominal wall movement. The device functions as an accessory to polysomnography (PSG) systems, enabling qualified sleep clinicians to analyze respiratory data for the diagnosis of sleep disorders.

The sensor consists of two main components: a PVDF (polyvinylidene fluoride) sensor module and an elastic belt. The sensor module contains two plastic enclosures connected by a piezoelectric PVDF sensing element encased in a silicone laminate. The PVDF material generates a tiny voltage that is output through the lead wire to the sleep amplifier. The change in voltage as the tension on the PVDF film fluctuates corresponds to the breathing of the patient. Since the PVDF material generates voltage, the sensor does not require a battery or power from the amplifier. The output signal is processed by the sleep recording system for monitoring and post-study analysis.

The PV01 PVDF Effort Sensor is intended for prescription use only by healthcare professionals in hospitals, sleep laboratories, clinics, nursing homes, or in home environments under medical professional direction. The device is designed for use on both adult and children participating in sleep disorder studies. The sensor is intended to be worn over clothes and not directly on the patient's skin.

The 510(k) clearance letter for the PV01 PVDF Effort Sensor does not contain the specific details required to fully address all aspects of your request regarding acceptance criteria and the study proving the device meets them. This document is a regulatory approval letter, summarizing the basis for clearance, not a detailed study report.

However, based on the provided text, here's an attempt to extract and infer the information:

Overview of Device Performance Study

The PV01 PVDF Effort Sensor underwent "comprehensive verification and validation testing" including "functional and performance evaluations" and "validation studies" to confirm it meets design specifications and is safe and effective. Additionally, "comparative testing against the Reference Device" was performed.

This suggests that the performance evaluation primarily focused on:

1. Safety Tests: Compliance with UL 60601-1 standards to ensure electrical and liquid ingress safety.
2. Usability and Validation Test: Assessment of user experience and comfort during a simulated sleep study.
3. Performance Comparison Test: Electrical signal output comparison to a legally marketed predicate device under simulated breathing conditions.
4. Temperature Range Test: Verification of signal output performance at extreme operating temperatures.

Acceptance Criteria and Reported Device Performance

Based on the "Summary of Tests Performed" section, the following can be inferred:

Missing Information and Limitations:

The provided FDA 510(k) clearance letter is a high-level summary and does not contain the granular details typically found in a full study report. Therefore, most of the following requested information cannot be extracted directly from this document.

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

   • Test Set Size: Not specified for any of the performance tests. For the usability test, it mentions "Participants" (plural), but no number. For the performance comparison test, it states "Both devices were placed on a rig," implying a comparison, but no human subject or case count.
   • Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The usability test mentions "participants," potentially implying prospective data collection, but this is a broad inference.

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

   • Not Applicable/Not Specified: The device is a "PVDF Effort Sensor" that measures and outputs respiratory effort signals. Its purpose is to provide raw physiological data for a "qualified sleep clinician to aid in the diagnosis of sleep disorders." The device itself does not provide a diagnosis or interpretation that would require expert ground truth labeling in the traditional sense of an AI diagnostic device (e.g., image-based AI). The performance assessment appears to be against expected signal characteristics and comparison to a known device, not against clinical ground truth established by experts.

3. Adjudication method for the test set:

   • Not Applicable/Not Specified: Given the nature of the device (a sensor outputting physiological signals) and the described tests, a formal adjudication process (like for interpreting medical images) is not mentioned or implied.

4. 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: This type of study (MRMC for AI assistance) is not mentioned. The device is a sensor, not an AI interpretative tool designed to assist human readers directly. It provides raw data for clinicians to analyze.

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

   • Partially Yes (for the sensor itself): The "Performance Comparison Test" and "Temperature Range Test" assess the device's signal output performance independently without a human in the loop for interpretation. The "Safety Tests" are also standalone tests on the device's physical and electrical properties.

6. The type of ground truth used:

   • Physiological Simulation / Device Output Comparison: For the "Performance Comparison Test," the ground truth was essentially the simulated breathing patterns produced by a "rig" and the expected output signals of a known predicate/reference device.
   • User Feedback / Self-Reported Metrics: For the "Usability and Validation Test," the ground truth was the participants' subjective feedback on comfort and ease-of-use, and the absence of reported use errors or adverse events.
   • Compliance with Standards: For "Safety Tests," the ground truth was compliance with the specified clauses of the UL 60601-1 standard.

7. The sample size for the training set:

   • Not Applicable/Not Specified: The PV01 PVDF Effort Sensor is described as a passive hardware sensor ("generates a tiny voltage," "does not require a battery or power from the amplifier") that measures physical movement. It is not an AI/ML algorithm that requires a "training set" in the computational sense.

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

   • Not Applicable: As stated above, there is no mention or implication of a training set as this is a hardware sensor, not an AI/ML algorithm.

In summary, the provided document gives a high-level overview of the acceptance criteria met for regulatory clearance, primarily focusing on safety, basic functional performance relative to another device, and usability. It does not delve into the detailed statistical methodology and independent ground truth establishment typical of AI/ML device studies.

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The monitor B105M, B125M, B155M, B105P and B125P are portable multi-parameter patient monitors intended to be used for monitoring, recording, and to generate alarms for multiple physiological parameters of adult, pediatric, and neonatal patients in a hospital environment and during intra-hospital transport.

The monitor B105M, B125M, B155M, B105P and B125P are intended for use under the direct supervision of a licensed health care practitioner.

The monitor B105M, B125M, B155M, B105P and B125P are not Apnea monitors (i.e., do not rely on the device for detection or alarm for the cessation of breathing). These devices should not be used for life sustaining/supporting purposes.

The monitor B105M, B125M, B155M, B105P and B125P are not intended for use during MRI.

The monitor B105M, B125M, B155M, B105P and B125P can be stand-alone monitors or interfaced to other devices via network.

The monitor B105M, B125M, B155M, B105P and B125P monitor and display: ECG (including ST segment, arrhythmia detection, ECG diagnostic analysis and measurement), invasive blood pressure, heart/pulse rate, oscillometric non-invasive blood pressure (systolic, diastolic and mean arterial pressure), functional oxygen saturation (SpO2) and pulse rate via continuous monitoring (including monitoring during conditions of clinical patient motion or low perfusion), temperature with a reusable or disposable electronic thermometer for continual monitoring Esophageal/Nasopharyngeal/Tympanic/Rectal/Bladder/Axillary/Skin/Airway/Room/Myocardial/Core/Surface temperature, impedance respiration, respiration rate, airway gases (CO2, O2, N2O, anesthetic agents, anesthetic agent identification and respiratory rate), Cardiac Output (C.O.), Entropy, neuromuscular transmission (NMT) and Bispectral Index (BIS).

The monitor B105M, B125M, B155M, B105P and B125P are able to detect and generate alarms for ECG arrhythmias: Asystole, Ventricular tachycardia, VT>2, Ventricular Bradycardia, Accelerated Ventricular Rhythm, Ventricular Couplet, Bigeminy, Trigeminy, "R on T", Tachycardia, Bradycardia, Pause, Atrial Fibrillation, Irregular, Multifocal PVCs, Missing Beat, SV Tachy, Premature Ventricular Contraction (PVC), Supra Ventricular Contraction (SVC) and Ventricular fibrillation.

The proposed monitors B105M, B125M, B155M, B105P and B125P are new version of multi-parameter patient monitors developed based on the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490) to provide additional monitored parameter Bispectral Index (BIS) by supporting the additional optional E-BIS module (K052145) which used in conjunction with Covidien BISx module (K072286).

In addition to the added parameter, the proposed monitors also offer below several enhancements:

• Provided data connection with GE HealthCare anesthesia devices to display the parameters measured from anesthesia devices (Applicable for B105M, B125M and B155M).
• Modified Early Warning Score calculation provided.
• Separated low priority alarms user configurable settings from the combined High/Medium/Low priority options.
• Provided additional customized notification tool to allow clinician to configure the specific notification condition of one or more physiological parameters measured by the monitor. (Applicable for B105M, B125M and B155M).
• Enhanced User Interface in Neuromuscular Transmission (NMT), Respiration Rate and alarm overview.
• Provided Venous Stasis to assist venous catheterization with NIBP cuff inflation.
• Supported alarm light brightness adjustment.
• Supported alarm audio pause by gesture (Not applicable for B105M and B105P).
• Supported automatic screen brightness adjustment.
• Supported network laser printing.
• Continuous improvements in cybersecurity

The proposed monitors B105M, B125M, B155M, B105P and B125P retain equivalent hardware design based on the predicate monitors and removal of the device Trim-knob to better support cleaning and disinfecting while maintaining the same primary function and operation.

Same as the predicate device, the five models (B105M, B125M, B155M, B105P and B125P) share the same hardware platform and software platform to support the data acquisition and algorithm modules. The differences between them are the LCD screen size and configuration options. There is no change from the predicate in the display size.

As with the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490), the proposed monitors B105M, B125M, B155M, B105P and B125P are multi-parameter patient monitors, utilizing an LCD display and pre-configuration basic parameters: ECG, RESP, NIBP, IBP, TEMP, SpO2, and optional parameters which include CO2 and Gas parameters provided by the E-MiniC module (K052582), CARESCAPE Respiratory modules E-sCO and E-sCAiO (K171028), Airway Gas Option module N-CAiO (K151063), Entropy parameter provided by the E-Entropy module (K150298), Cardiac Output parameter provided by the E-COP module (K052976), Neuromuscular Transmission (NMT) parameter provided by E-NMT module (K051635) and thermal recorder B1X5-REC.

The proposed monitors B105M, B125M, B155M, B105P and B125P are not Apnea monitors (i.e., do not rely on the device for detection or alarm for the cessation of breathing). These devices should not be used for life sustaining/supporting purposes. Do not attempt to use these devices to detect sleep apnea.

As with the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490), the proposed monitors B105M, B125M, B155M, B105P and B125P also can interface with a variety of existing central station systems via a cabled or wireless network which implemented with identical integrated WiFi module. (WiFi feature is disabled in B125P/B105P).

Moreover, same as the predicate monitors B105M, B125M, B155M, B105P and B125P (K213490), the proposed monitors B105M, B125M, B155M, B105P and B125P include features and subsystems that are optional or configurable, and it can be mounted in a variety of ways (e.g., shelf, countertop, table, wall, pole, or head/foot board) using existing mounting accessories.

The provided FDA 510(k) clearance letter and summary for K242562 (Monitor B105M, Monitor B125M, Monitor B155M, Monitor B105P, Monitor B125P) do not contain information about specific acceptance criteria, reported device performance metrics, or details of a study meeting those criteria for any of the listed physiological parameters or functionalities (e.g., ECG or arrhythmia detection).

Instead, the documentation primarily focuses on demonstrating substantial equivalence to a predicate device (K213490) by comparing features, technology, and compliance with various recognized standards and guidance documents for safety, EMC, software, human factors, and cybersecurity.

The summary explicitly states: "The subject of this premarket submission, the proposed monitors B105M/B125M/B155M/B105P/B125P did not require clinical studies to support substantial equivalence." This implies that the changes introduced in the new device versions were not considered significant enough to warrant new clinical performance studies or specific quantitative efficacy/accuracy acceptance criteria beyond what is covered by the referenced consensus standards.

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

1. A table of acceptance criteria and the reported device performance: This information is not present. The document lists numerous standards and tests performed, but not specific performance metrics or acceptance thresholds.
2. Sample size used for the test set and the data provenance: Not explicitly stated for performance evaluation, as clinical studies were not required. The usability testing mentioned a sample size of 16 US clinical users, but this is for human factors, not device performance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, as detailed performance studies requiring expert ground truth are not described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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: Not applicable. This device is a patient monitor, not an AI-assisted diagnostic tool that would typically involve human readers.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: The document describes "Bench testing related to software, hardware and performance including applicable consensus standards," which implies standalone testing against known specifications or simulated data. However, specific results or detailed methodologies for this type of testing are not provided beyond the list of standards.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not explicitly stated for performance assessment. For the various parameters (ECG, NIBP, SpO2, etc.), it would typically involve reference equipment or validated methods as per the relevant IEC/ISO standards mentioned.
8. The sample size for the training set: Not applicable, as this is not an AI/ML device that would require explicit training data in the context of this submission.
9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document focuses on demonstrating that the new monitors are substantially equivalent to their predicate through feature comparison, adherence to recognized standards, and various non-clinical bench tests (e.g., hardware, alarms, EMC, environmental, reprocessing, human factors, software, cybersecurity). It does not contain the detailed performance study results and acceptance criteria typically found for novel diagnostic algorithms or AI-driven devices.

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1. LVIS NeuroMatch Software is intended for the review, monitoring and analysis of electroencephalogram (EEG) recordings made by EEG devices using scalp electrodes and to aid neurologists in the assessment of EEG. The device is intended to be used by qualified medical practitioners who will exercise professional judgement in using the information.

2. The Seizure Detection component of LVIS NeuroMatch is intended to mark previously acquired sections of adult EEG recordings from patients greater than or equal to 18 years old that may correspond to electrographic seizures, in order to assist qualified medical practitioners in the assessment of EEG traces. EEG recordings should be obtained with a full scalp montage according to the electrodes from the International Standard 10-20 placement.

3. The Spike Detection component of LVIS NeuroMatch is intended to mark previously acquired sections of adult EEG recordings from patients ≥18 years old that may correspond to spikes, in order to assist qualified medical practitioners in the assessment of EEG traces. LVIS NeuroMatch Spike Detection performance has not been assessed for intracranial recordings.

4. LVIS NeuroMatch includes the calculation and display of a set of quantitative measures intended to monitor and analyze EEG waveforms. These include Artifact Strength, Asymmetry Spectrogram, Autocorrelation Spectrogram, and Fast Fourier Transform (FFT) Spectrogram. These quantitative EEG measures should always be interpreted in conjunction with review of the original EEG waveforms.

5. LVIS NeuroMatch displays physiological signals such as electrocardiogram (ECG/EKG) if it is provided in the EEG recording.

6. The aEEG functionality included in LVIS NeuroMatch is intended to monitor the state of the brain.

7. LVIS NeuroMatch Artifact Reduction (AR) is intended to reduce muscle and eye movements, in EEG signals from the International Standard 10-20 placement. AR does not remove the entire artifact signal and is not effective for other types of artifacts. AR may modify portions of waveforms representing cerebral activity. Waveforms must still be read by a qualified medical practitioner trained in recognizing artifacts, and any interpretation or diagnosis must be made with reference to the original waveforms.

8. LVIS NeuroMatch EEG source localization visualizes brain electrical activity on a 3D idealized head model. LVIS NeuroMatch source localization additionally calculates and displays summary trends based on source localization findings over time.

9. This device does not provide any diagnostic conclusion about the patient's condition to the user.

NeuroMatch is a cloud-based software as a medical device (SaMD) intended to review, monitor, display, and analyze previously acquired and/or near real-time electroencephalogram (EEG) data from patients greater than or equal to 18 years old. The device is not intended to substitute for real-time monitoring of EEG. The software includes advanced algorithms that perform artifact reduction, seizure detection, and spike detection.

The subject device is identical to the NeuroMatch device cleared under K241390, with exception of the following additional features:

1. Source localization;
2. Source localization trends;

Source localization and source localization trends are substantially equivalent to the Epilog PreOp (K172858). Apart from the proposed additional software changes and associated changes to the Indications for Use and labeling there are no changes to the intended use or to the software features that were previously cleared. Below is a description of the software functions that will be added to the cleared NeuroMatch Device.

1. Source Localization

The NeuroMatch Source Localization visualization feature is used to visualize recorded EEG activity from the scalp in an idealized 3D model of the brain. The idealized brain model is based on template MR images. Each single sample of EEG-measured brain activity corresponds to a single point/pixel referred to as a source localization node (i.e., "node"). Together, the source localization nodes form a 3D cartesian grid where EEG signals with higher standardized current density are depicted in red and signals with lower standardized current density are depicted in blue. Source localization can be performed for any selected segment of the EEG data. The maximum and minimum of the source localization values are the absolute maximum and minimum values across the selected EEG signal, respectively. Users can also set an absolute threshold for the minimum value of the source localization outputs.

2. Source Localization Trends

NeuroMatch provides three automatic source localization trends to assist physicians investigating the amplitude and the frequency of the signal of interest (e.g. seizure onset) at the source space. Two of the trends provide simple 3D views of the sources of the high amplitude / high frequency across the signal of interest. The third trend provides a similar 3D view of the high frequency source movement across time.

• Maximum Amplitude Projection (MAP): This metric allows clinicians to readily determine which brain regions are active and have high amplitude source localization results. The metric is determined by iterating through each node within a specified analysis time window and outputting the maximum source localization amplitude at that node within the specified analysis time window. No value is reported for nodes which have not been identified as maximum at any time during the specified window. This metric can help show brain regions that have high amplitude during a seizure.

• Node Visit Frequency (NVF): This metric is reported as the number of times that a node has been labeled as maximum over time. This metric can help clinicians identify which brain regions are frequently active during a seizure.

• Node Transition Frequency (NTF): This metric allows clinicians to determine which brain regions are active in consecutive time frames over a selected time period. A node transition is defined as a transition from one maximum point to another over time, and the node transition frequency is calculated by iterating through all time points for a specified analysis window, counting the number of times a transition between two points occurs over that time, and then dividing it by the time window of analysis. This metric can help identify pairs of brain regions that are frequently active in sequential order.

Here's an analysis of the acceptance criteria and study details for the NeuroMatch device, based on the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA letter does not explicitly state "acceptance criteria" in the traditional sense of pre-defined thresholds for performance metrics. Instead, the study's primary objective for Source Localization was to demonstrate non-inferiority to a reference device (CURRY) and comparable performance to a predicate device (Epilog PreOp). Therefore, the "acceptance criteria" can be inferred from the study's conclusions regarding non-inferiority and comparability.

For Source Localization Trends, the acceptance criterion was functional correctness and clinician understanding.

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

• Sample Size (Source Localization Test Set): 43 patients.
• Data Provenance: Collected from three independent and geographically diverse medical institutions:
  • Two institutions in the United States.
  • One institution in South Korea.
  • The study utilized retrospective data, as it focused on "previously acquired sections" of EEG recordings and "normalized post-operative MRIs with distinctive resection regions," indicating these were historical cases with established outcomes.

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

• Number of Experts: Three (3) US board-certified epileptologists.
• Qualifications: "US board-certified epileptologists." (Specific years of experience are not mentioned, but board certification implies a high level of expertise in the field).

4. Adjudication Method for the Test Set

• Adjudication Method: The three board-certified epileptologists independently completed a survey. They were presented with source localization results from each device (NeuroMatch, CURRY, PreOp) and normalized post-operative MRIs with resection regions.
• Ground Truth Establishment: Each physician independently determined the resection region at the sublobar level and then assessed whether the SL output of each device had any overlap with this determined resection region. For each patient and device, they responded to a "Yes/No" question asking about concordance. The method doesn't explicitly mention a consensus or adjudication process between the three experts for the final ground truth, but rather their individual assessments were used to determine the concordance rate. However, implying the "resected brain areas" as the primary ground truth, their task was to evaluate if the SL output agreed with this established ground truth from the MRIs. The "concordance" was then aggregated across their individual assessments against the known resection region.

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

• Yes, a form of MRMC study was done, but not in the traditional sense of measuring human reader improvement with AI assistance.
  • The study involved multiple readers (3 epileptologists) assessing multiple cases (43 patients).
  • However, the comparison was between AI algorithms (NeuroMatch vs. CURRY vs. PreOp), with the human readers acting as independent evaluators to establish concordance with a post-operative ground truth (resected brain areas).
  • Effect Size of Human Reader Improvement with AI vs. Without AI Assistance: This specific metric was not assessed or reported. The study evaluated the standalone AI performance of NeuroMatch compared to other AI devices, using human experts to determine the "correctness" of the AI's output in relation to surgical outcomes. It did not measure how human readers' diagnostic accuracy or efficiency changed when using NeuroMatch as an aid.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Yes, a standalone study was done for the Source Localization feature. The study directly compared the performance of the NeuroMatch algorithm against the CURRY reference device and the PreOp predicate device. The output was a "Yes/No" concordance with the resected brain area, as assessed by the experts. The experts evaluated the device's output, not their own performance using the device.

7. Type of Ground Truth Used

• Source Localization: The ground truth used was the resected brain areas as identified on normalized post-operative MRIs. This is a form of outcomes data combined with anatomical pathology (surgical intervention). The epileptologists were tasked with identifying whether the source localization output (from the algorithms) "overlapped" with these resected regions.
• Source Localization Trends: For the trends (MAP, NVF, NTF), the ground truth for functional correctness was EEG datasets with known solutions (i.e., simulated or carefully crafted data where the expected output of the algorithms was precisely predictable). For clinical utility, the ground truth was clinical feedback and perceived understanding from the 15 clinicians.

8. Sample Size for the Training Set

• The document does not specify the sample size for the training set for any of the algorithms. It only details the test set used for validation.

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

• The document does not specify how the ground truth for the training set was established. Since the training set size is not provided, this information is also absent.

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Automatic scoring of sleep EEG data to identify stages of sleep according the American Academy of Sleep Medicine definitions, rules and guidelines. It is to be used with adult populations.

The Neurosom EEG Assessment Technology (NEAT) is a medical device software application that allows users to perform sleep staging post-EEG acquisition. NEAT allows users to review sleep stages on scored MFF files and perform sleep scoring on unscored MFF files.

NEAT software is designed in a client-server model and comprises a User Interface (UI) that runs on a Chrome web browser in the client computer and a Command Line Interface (CLI) software that runs on a Forward-Looking Operations Workflow (FLOW) server.

The user interacts with the NEAT UI through the FLOW front-end application to initiate the NEAT workflow on unscored MFF files and visualize sleep-scoring results. Sleep stages are scored by the containerized neat-cli software on the FLOW server using the EEG data. The sleep stages are then added to the input MFF file as an event track file in XML format. Once the new event track file is created, the NEAT UI component retrieves the sleep events from the FLOW server and displays a hypnogram (visual representation of sleep stages over time) on the screen, along with sleep statistics and other subject details. Additionally, a summary of the sleep scoring is automatically generated and added to the same participant in the FLOW server in PDF format.

The FDA 510(k) Clearance Letter for NEAT 001 provides information about the device's acceptance criteria and the study conducted to prove its performance.

Acceptance Criteria and Device Performance

The core acceptance criteria for NEAT 001, as demonstrated by the comparative clinical study, are based on its ability to classify sleep stages (Wake, N1, N2, N3, REM) with performance comparable to the predicate device, EnsoSleep, and within the variability observed among expert human raters.

Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state pre-defined numerical "acceptance criteria" for each metric (Sensitivity, Specificity, Overall Agreement) that NEAT 001 had to meet. Instead, the approach was a comparative effectiveness study against a predicate device (EnsoSleep), with the overarching criterion being "substantial equivalence" as interpreted by performance falling within the range of differences expected among expert human raters.

Therefore, the "acceptance criteria" are implied by the findings of substantial equivalence. The "reported device performance" is given in terms of the comparison between NEAT and EnsoSleep, and their differences relative to human agreement variability.

Study Details

Here's a breakdown of the study details based on the provided text:

1. Sample Size and Data Provenance

• Test Set Sample Size: The exact number of participants or EEG recordings in the test set is not explicitly stated. The document refers to "two data sets" (referred to as "BEL data set" and "CSF data set") used for testing both NEAT and EnsoSleep. The large resampling number (R=2000 resamples for bootstrapping) suggests a dataset size sufficient to yield small confidence intervals.
• Data Provenance:
  • Country of Origin: Not explicitly stated.
  • Retrospective or Prospective: Not explicitly stated, but the mention of "All data files were scored by EnsoSleep" and "All data files were scored by NEAT" implies these were pre-existing datasets, making them retrospective.

2. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated. The study refers to "established gold standard" and "human agreement variability" among "expert human raters," implying multiple experts.
• Qualifications of Experts: Not explicitly stated beyond "expert human raters." No details are provided regarding their specific medical background (e.g., neurologists, sleep specialists), years of experience, or board certifications.

3. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The document simply refers to "the established gold standard." It does not mention whether this gold standard was derived from a single expert, consensus among multiple experts, or a specific adjudication process (like 2+1 or 3+1).

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

• Was an MRMC study done? A direct MRMC comparative effectiveness study involving human readers assisting with AI vs. without AI assistance was not explicitly described. The study primarily focuses on comparing the standalone performance of NEAT (the AI) against the standalone performance of the predicate device (EnsoSleep), and then interpreting these differences in the context of human-to-human agreement variability.
• Effect Size of Human Reader Improvement: Since a direct MRMC study with human readers assisting AI was not detailed, there is no information provided on the effect size of how much human readers improve with AI vs. without AI assistance.

5. Standalone Performance (Algorithm Only)

• Was a standalone study done? Yes. The study evaluated the "segment-by-segment" performance of NEAT and EnsoSleep algorithms directly against the "established gold standard." This is a measure of the algorithm's standalone performance without human input during the scoring process.

6. Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the test set was based on an "established gold standard" for sleep stage classification. This strongly implies expert consensus or expert scoring of the EEG data according to American Academy of Sleep Medicine definitions, rules, and guidelines. Pathology or outcomes data were not used for sleep staging ground truth.

7. Training Set Sample Size

• Training Set Sample Size: The sample size for the training set is not explicitly stated in the provided document.

8. How Ground Truth for Training Set Was Established

• How Ground Truth for Training Set Was Established: The document states that neat-cli "leverages Python libraries for identifying stages of sleep on MFF files using Machine Learning (ML)." However, it does not explicitly describe how the ground truth for the training set was established. Typically, for ML models, the training data's ground truth would also be established by expert annotation or consensus, similar to the test set ground truth, but this is not confirmed in the provided text.

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