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ANNE Pediatric is a wireless vital signs and physiological data monitoring platform indicated for the measurement of heart rate, skin temperature, and body temperature by qualified healthcare professionals in healthcare settings. The device is intended for use on neonatal patients of any gestational age up to infants 2 years of age as a general patient monitor to provide continuous physiological information as an aid to diagnosis and treatment. The device is not intended to monitor critically ill patients. The device is not intended to monitor or measure heart rate during motion. The device is not an apnea alarm.

ANNE Pediatric is a wireless vital signs and physiological data monitoring platform that collects electrocardiography (ECG) and temperature biosignals to measure vital signs such as heart rate (HR), skin temperature, and body temperature. The system features two skin-mounted, bio-integrated sensors that pair with the ANNE View application for the display of real-time vital measurements. Data is transmitted wirelessly from the sensors via Bluetooth to the mobile device.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for Heart Rate (HR) measurement are explicitly stated within the text as defined by IEC 60601-2-27. While the general standard is mentioned, the specific performance requirement is a tolerance of ±10% or ±5 bpm, whichever is greater.

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

• Sample Size for Test Set (Clinical Data for HR):
  • Total neonates assessed: n=137
  • Neonates included in the final closed-label round for HR analysis: n=20
  • Neonates included in the overall HR analysis (across multiple rounds): n=70
• Data Provenance: Clinical data was collected in Nairobi, Kenya at Aga Khan University, a tertiary healthcare facility. The study appears to be prospective as it involved assessing the functionality and performance of the ANNE Pediatric system in "n=137 neonates."

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

The document does not explicitly state the number of experts used or their specific qualifications for establishing ground truth. However, it indicates that the comparison was made against the Masimo Rad-97, which is a widely accepted medical device for physiological monitoring. This implies that the Masimo Rad-97 served as the reference standard, or "ground truth," rather than human expert interpretation of raw data.

4. Adjudication Method for the Test Set

The document mentions "one open label and three closed-label rounds" without detailing specific adjudication methods like 2+1 or 3+1. Given that the ground truth was a reference device (Masimo Rad-97), a traditional adjudication method for human interpretation would not be applicable. The "closed-label" rounds likely refer to blinding the assessors to the ANNE Pediatric output during data collection or analysis against the reference device.

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

No, an MRMC comparative effectiveness study was not done. The study detailed is a performance assessment of the device against a predicate device for heart rate measurement. There is no mention of human readers or AI assistance.

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

Yes, the described clinical study for heart rate measurement is a standalone performance assessment of the ANNE Pediatric device. It compares the device's output (presumably from its algorithms) directly against a reference medical device (Masimo Rad-97). There is no mention of human-in-the-loop performance in this specific clinical study.

7. The Type of Ground Truth Used

The ground truth used for heart rate measurement was established by a reference medical device, specifically the Masimo Rad-97.

8. The Sample Size for the Training Set

The document does not provide information regarding the sample size used for the training set. The clinical data described is explicitly for performance assessment/testing.

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

Since information regarding the training set and its sample size is not provided, the method for establishing its ground truth is also not available in this document.

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ANNE Sleep is a wearable sensor system intended for use in the collection, and storage of physiological parameters to aid in the evaluation of sleep-related breathing disorders of adult patients suspected of sleep apnea. The device is intended for use in the clinical and home setting under the direction and interpretation by a Healthcare Professional (HCP).

ANNE Sleep is a wireless physiological recorder for use as an aid in the diagnosis of sleep-related breathing disorders by healthcare professionals. The system features two skin-mounted, biointegrated sensors that pair with an associated proprietary software application for the transmission and storage of data. The sensors, which are placed on the chest and finger, measure peripheral arterial tonometry (PAT), oximetry (SpQ2), pulse rate, snoring, chest movement, body position, total sleep time, and heart rate. These data are uploaded to a cloud backend that can communicate with a compatible third-party software viewer, where they are reviewed by a qualified healthcare professional to identify sleep-disordered breathing events according to a scoring manual. The number of sleep-disordered breathing events per hour of sleep is the Sibel-AHI, a parameter that functions as an aid to diagnosis of sleep apnea by physicians. A sleep report is generated from this score and uploaded to a web portal, the ANNE Sleep Hub, where it can be stored and further reviewed.

Here's a breakdown of the acceptance criteria and the study details for the ANNE Sleep device, based on the provided text:

Acceptance Criteria and Reported Device Performance

Study Details

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

   • SpO2 Accuracy Study: n=12 healthy subjects.
   • Comparison to PSG Study: n=225 subjects.
   • Data Provenance: The SpO2 accuracy study was conducted using healthy subjects. The Comparison to PSG study was a single-arm, open-label, multi-center clinical study conducted in the United States. The study subjects were adults (22 years or older) suspected of having OSA. The demographic breakdown was 44% male, 56% female, and ethnicity was 73% white, 12% Black or African American, 9% Asian, 0.4% American Indian or Alaska Native, and 4% multi-racial. This indicates a prospective clinical study.

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

   • The document does not explicitly state the number of experts used for establishing ground truth for either the SpO2 accuracy study or the PSG comparison study.
   • For the SpO2 accuracy, the ground truth was "blood gas analysis", which typically refers to laboratory measurements and not expert interpretation.
   • For the PSG comparison, the ground truth was "gold standard, polysomnography (PSG)". While PSG studies are typically scored by trained sleep technologists and interpreted by physicians, the document does not specify the number or qualifications of these individuals involved in establishing the ground truth for this specific test set. The device itself states that its Sibel-AHI is "based on manual scoring of data by HCP," implying that qualified healthcare professionals are involved in interpreting the data for diagnosis.

3. Adjudication method for the test set:

   • The document does not specify an explicit adjudication method (e.g., 2+1, 3+1) for either study. For the PSG ground truth, standard PSG scoring guidelines are typically used, which implicitly involves a form of expert consensus, but a formal adjudication process beyond scoring rules is not mentioned.

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 comparative effectiveness study involving human readers with and without AI assistance was not done or reported. The study compared the ANNE Sleep system's performance as a diagnostic aid (implying human interpretation of the results to aid in diagnosis) against the PSG gold standard. The device generates a "Sibel-AHI (based on manual scoring of data by HCP)," suggesting that human interpretation is an integral part of its intended use but not in an AI-assistance vs. no AI-assistance comparative evaluation.

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

   • The device's Indications for Use state that it is "intended for use in the clinical and home setting under the direction and interpretation by a Healthcare Professional (HCP)." Furthermore, the "Sibel-AHI" is described as "based on manual scoring of data by HCP." This indicates that the device is not intended for standalone (algorithm-only) performance but rather as an aid requiring human interpretation. The reported sensitivity and specificity relate to the system as a "diagnostic aid," which aligns with the human-in-the-loop model defined in the indications.

6. The type of ground truth used:

   • For SpO2 accuracy: Blood gas analysis.
   • For OSA diagnosis accuracy: Polysomnography (PSG), considered the gold standard.

7. The sample size for the training set:

   • The document does not provide information regarding the sample size used for a training set if machine learning or AI models were used in the device's classification or analysis algorithms. The description focuses on clinical validation, not algorithm development.

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

   • Since information on a specific training set or machine learning model development is not provided, the method for establishing ground truth for a training set is not available in this document.

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ANNE One is a wireless vital signs and physiological data monitoring platform indicated for the measurement of heart rate, respiratory rate, step count, fall count, skin temperature, and body temperature by qualified healthcare professionals in healthcare settings. The device is intended for use on general care patients who are 18 years of age or older as a general patient monitor to provide continuous physiological information as an aid to diagnosis and treatment. The device is not intended for use on critical care patients. The device is not intended to monitor or measure respiratory rate or heart rate on ambulatory patients.

ANNE One is a wireless multi-parameter vital signs monitoring system that consists of the following components, and subsystems:
A. ANNE Tablet (Samsung Galaxy Tablet) - manufactured by Samsung
B. ANNE View Mobile Application (software) manufactured by Sibel Inc.
C. ANNE Chest Sensor manufactured by Sibel Inc.
D. ANNE Limb Sensor manufactured by Sibel Inc.
E. ANNE Wireless Charger manufactured by Sibel Inc.
The ANNE Chest Sensor is a battery-operated, skin-mounted sensor with wireless transceiver capabilities worn on the upper body to measure heart rate, respiratory rate, step count, fall count, and skin temperature.
The ANNE Limb Sensor is an additional battery-operated skin-mounted sensor with wireless transceiver capabilities worn on the finger to measure skin temperature. When placed underneath the axillae, the ANNE Limb Sensor can measure body temperature.
Both the ANNE Chest and ANNE Limb Sensors continuously gather vital signs data from the person being monitored and then transmit the encrypted data to the ANNE Tablet operating the ANNE View Mobile Application-a mobile device software application.

The provided text describes the ANNE One device, a wireless vital signs and physiological data monitoring platform, and its FDA 510(k) submission (K211305). While the document details various performance tests and validation methods, it does not provide specific acceptance criteria in a numerical format for each vital sign, nor does it present the reported device performance against such criteria in a table. It mostly states what was tested and how it was tested (e.g., according to specific ISO/IEC standards or against FDA-cleared reference devices).

Therefore, I cannot construct a table with acceptance criteria and reported device performance as requested, because that data is not explicitly present in the provided document. The document lists the types of measurements the device takes (heart rate, respiratory rate, step count, fall count, skin temperature, body temperature) and broadly states that bench and simulated use testing was conducted to demonstrate effectiveness and conformity to specifications.

However, I can extract information about the studies performed to demonstrate the device's capabilities:

Summary of Device Performance Studies for ANNE One (K211305)

The provided document describes various performance tests conducted to support the substantial equivalence determination for the ANNE One device. It does not explicitly state numerical "acceptance criteria" and "reported device performance" in a table format for each physiological parameter. Instead, it describes compliance with relevant standards and comparative studies against reference devices.

1. Table of Acceptance Criteria and Reported Device Performance

As mentioned, specific numerical acceptance criteria and reported performance values are not provided in the given text. The document broadly states that tests were performed to verify device specifications and ensure mechanical and electrical requirements were met, and that effectiveness was validated according to standards or against reference devices.

For example, for temperature measurement, it states: "Laboratory accuracy testing according to ISO 80601-2-56:2017 Section 201.101.2 validated the effectiveness of the ANNE Chest and Limb sensor for skin temperature measurements and the Limb Sensor for body temperature measurements when placed under the axillae." This indicates compliance with the standard but doesn't give a numerical accuracy. Similar statements are made for heart rate and respiratory rate.

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

• Heart Rate:
  • Simulated Use Study: 35 healthy subjects.
  • Data Provenance: Not explicitly stated, but clinical studies are generally prospective. The phrase "healthy subjects" implies volunteers, likely from a single or a few sites.
• Respiratory Rate:
  • Study: 50 healthy subjects.
  • Data Provenance: Not explicitly stated, but generally prospective for such studies.
• Skin Temperature, Step Count, Fall Detection:
  • Simulated Use Testing: n=10 subjects.
  • Data Provenance: Not explicitly stated, but generally prospective.
• Biocompatibility Testing: All patient-contacting materials were tested.
• Electrical Safety & EMC Testing: Conducted on the device.
• Software Verification & Validation Testing: Conducted on the software.
• Animal Studies: Not required or performed.
• Clinical Studies: Not required or performed for safety and effectiveness. (Instead, substantial equivalence was supported by Bench and Simulated Use testing).
• Usability Testing: Conducted, but sample size not specified.

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

The document does not mention the use of experts to establish a "ground truth" in the context of human reader studies (e.g., for image interpretation). Instead, the ground truth for physiological measurements was established using FDA-cleared reference devices or manual observation.

4. Adjudication Method for the Test Set

Not applicable. The studies described are not "human-in-the-loop" studies requiring expert adjudication of reader performance. Measurements were compared against reference devices or manual counts.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not performed, nor was it relevant to this device's function. The ANNE One is a vital signs monitoring device, not an imaging analysis AI intended to assist human readers in diagnosis. Therefore, there is no information on how human readers might improve with AI assistance. The performance studies focused on the accuracy of the device's measurements compared to established reference methods.

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

Yes, the performance studies described (accuracy of heart rate, respiratory rate, temperature, step count, fall count) essentially describe the standalone performance of the ANNE One system in measuring these vital signs. The device generates the data, and its accuracy is validated against gold standards (reference devices or manual counts). There isn't a human processing the algorithm's output for further interpretation in the way one might with an AI for medical image analysis.

7. The Type of Ground Truth Used

The ground truth for the vital signs measurements was primarily established using:

• FDA-cleared reference devices: For heart rate (against an FDA-cleared reference device) and respiratory rate (against an FDA-cleared End Tidal Carbon Dioxide (EtCO2) reference device).
• Reference standard/protocol conformance: For temperature (Laboratory accuracy testing according to ISO 80601-2-56:2017).
• Manual counts: For step and fall counts, demonstrated to conform to design specifications through comparison to manual counts.

8. The Sample Size for the Training Set

The document does not specify any training set size. This is likely because the device is a hardware sensor system with associated software for data collection and display, rather than a machine learning model that requires a large separate training dataset in the typical sense of AI/ML-driven diagnostics. The "software verification and validation testing" refers to the traditional software development lifecycle testing, not machine learning model training.

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

As no training set is mentioned in the context of a machine learning model, this question is not applicable. The device's functionality is based on established physiological measurement principles and signal processing, not a trained AI model in the common sense.

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