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The ANNE View application is intended for the display of physiological data from the ANNE Chest and ANNE Limb devices. The application is also compatible with third-party, FDA-cleared devices for the display of noninvasive blood pressure, SpO2, pulse rate, and body temperature measurements. The ANNE View application notifies healthcare professionals when physiological data fall outside selected parameters. The ANNE View application displays the orientation of patients to aid in the prevention of pressure ulcers for at-risk patients. The system notifies the user when the patient's position has not changed for a preset threshold of time. The ANNE View application communicates to compatible central monitoring platforms, including the Central Hub, for the display and storage of multiple patients' physiological data. The Central Hub has the ability to notify healthcare professionals when physiological data fall outside selected parameters.

The ANNE View and Central Hub are intended for use by trained, qualified healthcare professionals in the clinical or home healthcare environment. The device is not intended for use on critical care patients.

The ANNE View application is a bedside patient monitoring device that receives vital signs and physiological data from compatible devices for display and transmission to the Central Hub. The ANNE View application operates on the ANNE Tablet. When connected to the ANNE Chest Sensor, the ANNE View application displays electrocardiography (ECG), skin temperature, heart rate, respiratory rate, and body position data. When connected to the ANNE Limb Sensor, the ANNE View application displays photoplethysmography (PPG), SpO2, pulse rate, and skin temperature data. The ANNE View application is also compatible with optional third-party devices for SpO2, non-invasive blood pressure, and body temperature measurements. The ANNE View application alarms when physiological data are outside of the configured thresholds.

When the ANNE Tablet is connected to Wi-Fi, the vital signs and physiological data are uploaded from the ANNE View application to the Central Hub. The Central Hub allows healthcare professionals to view vitals data from up to 16 beds on one screen and provides storage of up to 48 hours of data from each patient. Alarm conditions are communicated to the Central Hub from the ANNE View application for visual and audio alarms on the Central Hub.

This is a clearance memo for a medical device and does not contain the detailed study information required to answer the prompt. Specifically, it states that "The following consensus standards and bench testing were used to evaluate the safety and effectiveness of ANNE View, Central Hub as compared to the predicate," followed by a list of general standards like electromagnetic compatibility testing, wireless coexistence testing, software verification and validation, usability testing, and cybersecurity evaluation. It also mentions "Verification of the ECG waveform display per IEC 60601-2-27:2011 and IEC 60601-2-47:2012" and "Verification of the alarm system to IEC 60601-1-8:2020," and "Performance evaluation of the HL7 FHIR and IEEE 11073 SDC communication."

However, it does not provide any specific quantitative acceptance criteria or detailed study results that would allow for a table of acceptance criteria vs. reported device performance, nor does it provide information on sample sizes, data provenance, ground truth establishment, expert qualifications, adjudication methods, or MRMC studies.

Therefore, I cannot fulfill the request using only the provided text. The document is a regulatory approval, not a clinical study report.

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The ANNE Chest is a wearable, wireless sensor intended for the measurement of electrocardioaraphy (ECG) waveforms, heart rate, respiratory rate, activity, fall detection, body position, and skin temperature. The ANNE Chest sensor is not intended to monitor or measure respiratory rate while the patient undergoes significant motion or is active. The ANNE Chest sensor communicates with compatible software applications for the display, storage, and analysis of data. The device is intended to provide continuous physiological information as an aid to diagnosis and treatment by healthcare professionals in general care patients who are 12 years of age or older in clinical and home environments. The device is not intended for use on critical care patients.

The ANNE Chest Sensor is a skin-mounted, bio-integrated sensor that collects real-time biosignals including electrocardiography (ECG), 3-axis accelerometry, and temperature to measure vital signs such as heart rate, respiratory rate, body position, fall detection, and skin temperature. The sensor communicates via Bluetooth to the Sibel SDK, which may be integrated within software applications for the display and storage of data. The ECG signal obtained by the ANNE Chest sensor is not intended for manual discrimination of any arrhythmias or cardiac conditions.

The provided document, primarily an FDA 510(k) clearance letter and summary, details the "ANNE Chest" device and its substantial equivalence to predicate devices. It includes performance data, but it does not contain the level of detail typically found in a comprehensive clinical study report for evaluating acceptance criteria and proving device performance for areas like AI/ML algorithms.

Specifically, the document lacks the following information crucial for a detailed response on acceptance criteria and study proving device meets them for AI/ML components:

• Explicit Acceptance Criteria Tables: While performance specifications are listed for heart rate, respiratory rate, and skin temperature, these are not presented as explicit "acceptance criteria" against which the device passed.
• Detailed Study Design for each feature: Only a brief summary of a clinical validation study for respiratory rate accuracy is provided. Details on ECG waveform analysis, activity, fall detection, and body position, if they incorporate AI/ML, are not detailed in terms of their dedicated performance studies.
• Sample Size for Test Set and Data Provenance for all features: Only n=40 is mentioned for respiratory rate. Data provenance (country, retrospective/prospective) is not specified.
• Expert Ground Truth Details: The number and qualifications of experts for establishing ground truth are not mentioned for any of the features.
• Adjudication Method: No information on adjudication is provided.
• MRMC Study Details: No information on multi-reader multi-case studies or effect sizes of human reader improvement with AI assistance is provided.
• Standalone AI Performance: The document describes the device as a sensor measuring physiological signals. It mentions software for display, storage, and analysis, but doesn't explicitly refer to AI/ML algorithms that operate in a "standalone" fashion where their performance metrics (e.g., sensitivity, specificity, AUC) against a ground truth would be relevant. The respiratory rate accuracy is presented as a direct comparison to a reference device.
• Ground Truth Type for all features: Only "End Tidal Carbon Dioxide (EtCO2) monitor reference" is mentioned for respiratory rate.
• Training Set Sample Size and Ground Truth Establishment: No information about training sets or how their ground truth was established is provided, suggesting that the device's functions might rely more on signal processing rather than intricate AI/ML models requiring large training datasets with defined ground truth methods.

Based on the limited information provided in the document, here's what can be extracted and inferred:

Acceptance Criteria and Reported Device Performance

The document provides performance specifications for certain physiological measurements. While not explicitly stated as "acceptance criteria," these are the performance targets the device claims to meet.

Table 1: Reported Device Performance

Note: The document explicitly states: "The ECG signal obtained by the ANNE Chest sensor is not intended for manual discrimination of any arrhythmias or cardiac conditions." This suggests the ECG waveform is for general display and rate calculation, not for specific diagnostic interpretation that might involve AI/ML for abnormality detection.

Study Details Proving Device Meets Acceptance Criteria

1. Sample Size for Test Set and Data Provenance:
* Respiratory Rate: n=40 healthy adult and adolescent subjects.
* Data Provenance: Not specified (e.g., country of origin). The study is described as "a clinical validation study," implying it was prospective for this evaluation.
* Other features (ECG, Heart Rate, Skin Temperature, Activity, Fall Detection, Body Position): No specific sample sizes for clinical validation studies are mentioned, only adherence to standards (e.g., IEC 60601-2-27, IEC 60601-2-47 for ECG) and "performance testing." This suggests these features might have been validated through bench testing or engineering verification rather than clinical studies of the type described for respiratory rate.

2. Number of Experts used to establish Ground Truth and Qualifications:
* Not specified. The ground truth for respiratory rate was an "End Tidal Carbon Dioxide (EtCO2) monitor reference," which is an objective measurement, not expert consensus. For other features, no details about expert involvement for ground truth are provided.

3. Adjudication Method for the Test Set:
* None specified. Given the use of an objective reference device (EtCO2 monitor) for respiratory rate, human adjudication would not be applicable for this specific metric.

4. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done:
* No. The document does not describe any MRMC studies or human reader improvement with AI assistance. The device focuses on physiological measurements rather than interpretive tasks for human readers.

5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:
* Implied for Respiratory Rate accuracy. The study directly compared the ANNE Chest sensor's respiratory rate measurement against a capnography reference, which aligns with standalone performance evaluation for that specific function. For other parameters like heart rate and skin temperature, this is also implied through performance specifications. The device itself is a measurement tool, outputs direct physiological values, and does not describe AI intended for interpretation or decision support that would typically have a human-in-the-loop component.

6. The Type of Ground Truth Used:
* Respiratory Rate: Objective reference standard: "End Tidal Carbon Dioxide (EtCO2) monitor reference."
* Other features: Not explicitly stated, but likely objective measurements from reference devices/standards (e.g., test ECG signals for ECG compliance, calibrated thermometers for skin temperature).

7. The Sample Size for the Training Set:
* Not specified. The document does not indicate the use of machine learning models that would require a distinct training set. The descriptions of the device functions point towards signal processing and known algorithms for extracting vital signs from raw sensor data, rather than complex AI requiring large, labeled training datasets.

8. How the Ground Truth for the Training Set was Established:
* Not applicable based on available information. Since no training set or complex AI model development requiring one is mentioned, this information is not provided.

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The ANNE Limb Sensor is a pulse oximeter intended for continuous monitoring of functional oxygen saturation of arterial hemoglobin (SpO2), pulse rate, and skin temperature. The ANNE Limb Sensor is not intended to monitor or measure SpO2 or pulse rate while the patient undergoes significant motion or is active. The ANNE Limb Sensor communicates with compatible software applications for the display, storage, and analysis of data. The device is indicated for use as an aid to diagnosis and treatment by healthcare professionals in general care patients who are 12 years of age or older in clinical and home environments. The device is not intended for use on critical care patients.

The ANNE Limb sensor is a wearable, bio-integrated sensor that collects real-time biosignals including photoplethysmography (PPG) and temperature. The ANNE Limb sensor has on-device processing of raw data for the calculation of SpO2 and pulse rate. The sensor communicates via Bluetooth to the Sibel SDK, which may be integrated within software applications for the display and storage of data.

The provided text describes the acceptance criteria and study proving the ANNE Limb device meets these criteria, specifically for SpO2 accuracy.

1. Table of acceptance criteria and reported device performance

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

• Sample Size: n=12 healthy subjects.
• Data Provenance: The text does not explicitly state the country of origin but implies a clinical study, which is typically prospective. It mentions "Enrolled subjects had skin tones varying from Fitzpatrick 2-5, with two subjects having darker skin pigmentation (Fitzpatrick 5)", indicating a controlled and prospective enrollment.

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

Not applicable for this study. The ground truth for SpO2 accuracy was established through blood gas analysis, a direct physiological measurement, not expert consensus reading of images or other data.

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

Not applicable. Ground truth was established by blood gas analysis.

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. This study focused on the ANNE Limb device's standalone performance for physiological measurements (SpO2, pulse rate, skin temperature) rather than AI assistance to human readers for interpretation.

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

Yes, the study described in section "VII. Clinical Studies" on page 4 evaluates the "SpO2 Accuracy" of the ANNE Limb device as a standalone measurement, comparing it to blood gas analysis.

7. The type of ground truth used

The ground truth for SpO2 accuracy was established by blood gas analysis.

8. The sample size for the training set

The document does not specify a training set for the current device's SpO2 accuracy evaluation. The clinical study described appears to be a validation study for the device's performance, not a training phase for a learning algorithm.

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

Not applicable, as a training set for a learning algorithm is not mentioned in the context of this specific SpO2 accuracy study. The device's SpO2 and pulse rate calculations involve on-device processing of raw PPG data, suggesting a more traditional signal processing and algorithmic approach as opposed to a deep learning model requiring a large labeled training dataset. The device leverages the "same sensor and algorithms for the calculation of SpO2, pulse rate, and skin temperature as the Limb Sensor of the predicate device, ANNE One," implying that any model training would have occurred during the development of the predicate device and is not detailed here.

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ANNE One is a wireless monitoring platform indicated for the measurement of electrocardiography (ECG) waveforms. heart rate, respiratory rate, function of arterial hemoglobin (SpO2), pulse rate, activity, body position, fall detection, skin temperature by qualified healthcare professionals in home and healthcare settings. ANNE One is compatible with third-party, FDA-cleared devices for noninvasive blood pressure, SpO2, pulse rate, and body temperature measurements. The device is indicated for monitoring ECG waveforms and heart rate on ambulatory patients. The device is not intended to monitor or measure respiratory rate. SpO2, pulse rate, or noninvasive blood pressure while the patient undergoes significant motion or is active.

ANNE One continuously monitors the orients to aid in the prevention of pressure ulcers for at-risk patients. The system provides visual notification when the pation has not changed from a preset threshold of time.

The device is intended for use on general care patients who are 12 years of age or older as a general patient monitor to provide continuous physiological information as an aid to diagnosis and treatment. The data from ANNE One are transmitted wirelessly for display, storage, and analysis. The device is not intended for use on critical care patients.

ANNE One is a wireless monitoring platform that streams and stores real-time biosignals including electrocardiography (ECG), photoplethysmography (PPG), 3-axis accelerometry, and temperature to measure vital signs such as heart rate, respiratory rate, SpO2, pulse rate, skin temperature, and body temperature. The ECG signal is not intended for automated arrhythmia detection or classification; rather it is intended for manual interpretation, and the automated computation of heart rate through QRS identification using the well-known Pan-Tompkins beat detection algorithm. The displayed waveform is only intended for display as a check for normal ECG rhythm. The waveform is not intended for manual discrimination of any arrhythmias or cardiac conditions. The system features two skin-mounted, bio-integrated sensors that pair with the ANNE View software application for the continuous display, storage, and analysis of vital sign measurements and physiological waveforms. The system is also compatible with optional FDA-cleared third-party devices for SpO2, non-invasive blood pressure, and body temperature measurements.

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

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document primarily focuses on demonstrating substantial equivalence to predicate and reference devices, with specific performance values provided for SpO2 and Respiratory Rate from clinical studies. The other parameters are stated to meet relevant standards or are equivalent to predicate/reference device performance.

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

• SpO2 Accuracy Study:

  • Sample Size: n=12 healthy subjects.
  • Data Provenance: Not explicitly stated (e.g., country of origin), but implies a controlled clinical setting. The study is described as "Sibel validated the accuracy...".
  • Retrospective/Prospective: Implied prospective as it's a validation study conducted by Sibel.

• Respiratory Rate Accuracy Study:

  • Sample Size: n=40 healthy adult subjects.
  • Data Provenance: Not explicitly stated (e.g., country of origin), but implies a controlled clinical setting. The study is described as "Sibel validated the accuracy...".
  • Retrospective/Prospective: Implied prospective as it's a validation study conducted by Sibel.

• Other Parameters: For other parameters like Heart Rate, ECG waveform display, Activity, etc., "performance testing" and adherence to "consensus standards" are mentioned, but specific sample sizes for these test sets are not provided in this summary. The comparison table also mentions "ambulatory databases" for ECG during motion, but no sample size is given.

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

• SpO2 Accuracy Study: Ground truth was established by "blood gas analysis." This typically involves laboratory analysis, not expert consensus on visual assessment.
• Respiratory Rate Accuracy Study: Ground truth was established by "etCO2" (end-tidal CO2). This is a physiological measurement, not an expert consensus.
• Other Parameters: No information is provided regarding experts or their qualifications for establishing ground truth for other parameters. Ground truth for these values would likely derive from established measurement techniques compliant with the referenced standards.

4. Adjudication Method for the Test Set

• Given that the ground truth for SpO2 and Respiratory Rate relied on objective physiological measurements (blood gas analysis and etCO2, respectively), there was likely no "adjudication method" in the sense of reconciling disagreements between multiple graders or clinicians. The measurements themselves serve as the ground truth.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No MRMC comparative effectiveness study involving human readers and AI assistance is mentioned in the provided text. The device is a "wireless monitoring platform" for physiological measurements. Its primary function is to collect and display vital sign data, not to interpret complex medical images or data that typically require a human reader for adjudication or enhancement by AI.

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

• Yes, the performance data provided for SpO2 and Respiratory Rate are standalone algorithm capabilities. The device measures these parameters and the reported accuracy is the algorithm's performance against a reference standard, without human intervention in the measurement or calculation process. The description of "automated computation of heart rate through QRS identification using the well-known Pan-Tompkins beat detection algorithm" also indicates a standalone algorithmic function.

7. The Type of Ground Truth Used

• SpO2: Blood gas analysis (objective physiological measurement).
• Respiratory Rate: EtCO2 (objective physiological measurement).
• Heart Rate: Implied to be derived from ECG signals, with validation against established standards (e.g., IEC 60601-2-27, IEC 60601-2-47), which would use a recognized reference for HR. The Pan-Tompkins algorithm is for beat detection, which is then used to compute HR.
• Other Parameters (Skin Temperature, Body Position, Activity, ECG Waveform Display, Pulse Rate): Ground truth is likely established through a combination of physical reference measurements and adherence to recognized consensus standards (e.g., ISO, IEC). Bench testing is mentioned for several parameters.

8. The Sample Size for the Training Set

• The document does not explicitly state the sample size for any training set for machine learning models. The device description mentions the use of "the well-known Pan-Tompkins beat detection algorithm" for heart rate, which is a classical signal processing algorithm and may not require a 'training set' in the modern machine learning sense. While algorithms are likely involved in respiratory rate and SpO2 calculations, the summary focuses on validation, not the development or training phase.

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

• As training set information is not provided, the method for establishing its ground truth is also not detailed.

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