Search Results

The Otsuka Digital Feedback Device consists of a miniaturized, wearable sensor for ambulatory recording of physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity (body position), and time-stamped patient-logged events, including events signaled by co-incidence with, or co ingestion with, the ingestible sensor accessory. When the ingestible sensor is ingested, the Otsuka Digital Feedback Device is intended to log, track, and trend intake times. When co-ingested with medication, the tracking and trending of intake times may be used as an aid to measure medication adherence. The Otsuka Digital Feedback Device may be used in any instance where quantifiable analysis of event-associated physiological and behavioral metrics is desirable and enables unattended data collection for clinical and research applications.

The Otsuka Digital Feedback Device consists of 3 components: a wearable sensor, an ingestible sensor accessory, and software that aggregates, processes, and enables display of data collected by the sensors.

The wearable sensor in the Otsuka Digital Feedback Device is a body-worn sensor that consists of a single-use patch known as the D-Tect wearable sensor or D-Tect Patch. The D-Tect Patch collects physiological and behavioral metrics such as heart rate, activity, body angle, and time stamped patient-logged events, including events signaled by the co-incidence with, or co-ingestion with, the ingestible sensor accessory. Note: While the device includes automated heart rate (HR) measurement, it does not provide an ECG waveform recording for display or analysis. The device is not intended to diagnose heart-related conditions and does not include alarms. HR measurement is not intended to be used in alarm system. HR data may not be accurate for patients with pacemakers.

The ingestible sensor is embedded inside an inactive tablet (the pill or sensor-enabled pill) for ease of handling and swallowing. After the ingestible sensor reaches the stomach, it activates and communicates its presence with a unique identifier to the wearable sensor. When the ingestible sensor is co-ingested with medication, the Otsuka Digital Feedback Device is intended to log, track, and trend medicine intake times to measure medication adherence.

The software on the general computing device receives the data from the wearable sensor for further processing and analysis of metrics. The processed data is then sent to the user interface for display, as well as being saved in a local record database for storage. The software component includes firmware running on the wearable sensor that collects and records the data from the sensors and a software that receives data from the wearable sensor for further analysis, processing, storage, and display to the user.

For purposes of this 510(k), the changes from the predicate device (Otsuka Digital Feedback Device-RW, K223463) to the Otsuka Digital Feedback Device with D-Tect Patch (the device subject of this 510[k]) are the wearable sensor and the software for the user interface. The ingestible sensor remains the same as the predicate device.

Here's a breakdown of the acceptance criteria and the study that proves the Otsuka Digital Feedback Device meets them, based on the provided FDA 510(k) clearance letter.

1. Table of Acceptance Criteria and Reported Device Performance

The document implicitly defines acceptance criteria through its performance testing sections. For the purpose of this analysis, I will focus on the most explicitly quantified performance metrics from the provided text.

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

• Sample Size (Clinical Performance Data): A total of 55 subjects were tested for the clinical performance evaluation.
• Data Provenance: The study was conducted at 1 site in the United States. It was a prospective noninterventional validation study in healthy subjects.

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

The document does not specify the number or qualifications of experts used to establish a "ground truth" for the test set in the conventional sense (e.g., radiologists reviewing images). The clinical performance study focused on objective measurements like wear duration, comfort ratings self-reported by subjects, and skin irritation scores, which are typically assessed by trained clinical staff rather than expert consensus on a diagnostic outcome. Skin irritation scores are based on established clinical grading scales.

4. Adjudication Method for the Test Set

The document does not describe an explicit adjudication method (like 2+1 or 3+1 consensus) for the test set. For the wear duration, comfort, and skin irritation metrics, the data collection methods (instrumental recording for wear duration, self-reporting for comfort, and clinical scoring for irritation) are inherently less reliant on such adjudication compared to subjective diagnostic tasks.

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 was mentioned in this document. The device, an "Ingestible Event Marker with wearable patch," is primarily focused on automated data collection for physiological and behavioral metrics and medication adherence, not on aiding human readers in interpreting complex diagnostic images or data where "AI assistance" would directly improve a reader's performance in a diagnostic task.

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

Yes, the testing described appears to largely cover standalone algorithm and device performance, particularly for:

• Pill Detection Capability: "Enhanced low-noise analog front end improves sensitivity of the IEM detection by >50%, increasing the likelihood of detection." This is an intrinsic algorithmic/hardware capability.
• Step Counting: "Improved step metric based on passive fully continuous step counting." This is an algorithmic function.
• Heart Rate Measurements: "Verified against real ECG signals and 100% measurement accuracy for simulated ECG signal." This tests the algorithm's performance.
• Wear Duration: This is objectively measured by the device's logging capability.

The clinical performance study focuses on the overall system's ability to log data correctly and be comfortable and safe, which inherently relies on the standalone performance of its components.

7. The Type of Ground Truth Used

• Wear Duration: Objective logging by the device itself, validated against study protocols of subjects wearing patches for specified periods.
• Comfort: Subject self-reporting on a defined scale.
• Skin Irritation: Clinical assessment based on established grading scales (Grade 0, 1, 2).
• Pill Detection, Step Counting, Heart Rate: Verified against reference methods/signals (e.g., real ECG signals, simulated ECG signals).
• Mechanical, Electrical, Software, Cybersecurity, Biocompatibility, Shelf Life: Ground truth is defined by compliance with applicable standards, specifications, and successful completion of pre-defined test protocols.

8. The Sample Size for the Training Set

The document does not provide details on the sample size used for the training set of any algorithms within the device. It focuses on the validation of the finalized device.

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

Since the document does not mention the training set size, it also does not elaborate on how the ground truth for any potential training data was established. The improvements noted (e.g., improved pill detection, step counting, HR measurements) suggest iterative development and potentially machine learning, but the specifics of their training data and ground truth are not detailed in this regulatory summary.

Ask a specific question about this device

The Otsuka Digital Feedback Device-RW consists of a miniaturized, wearable sensor for ambulatory recording of physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity (body position), and timestamped patient-logged events, including events signaled by the co-incidence with, or co-ingestible sensor accessory. When the ingestible sensor is ingested, the Otsuka Digital Feedback Device-RW is intended to log, track and trend intake times. When co-ingested with medication, the tracking of intake times may be used as an aid to measure medication adherence. The Otsuka Digital Feedback Device-RW may instance where quantifiable analysis of event-associated physiological and behavioral metrics is desirable, and enables unattended data collection for clinical and research applications.

The Otsuka Digital Feedback Device-RW consists of three components: a wearable sensor, an ingestible sensor accessory, and software that aggregates, processes and enables display of data collected by the sensors. The wearable sensor is a body-worn sensor (also called the patch) that collects physiological and behavioral metrics such as heart rate, activity, body angle, and time-stamped patient-logged events, including events signaled by the co-incidence with, or co-ingestion with, the ingestible sensor accessory. The wearable sensor in the Otsuka Digital Feedback Device-RW is a 2-component patch known as the RW2 wearable sensor or RW2 patch. The ingestible sensor is embedded inside an inactive tablet (the pill or sensor-enabled pill) for ease of handling and swallowing. After the ingestible sensor reaches the stomach, it activates and communicates its presence with a unique identifier (ID) to the wearable sensor. When the ingestible sensor is co-ingested with medication, the Otsuka Digital Feedback Device-RW is intended to log, track and trend medicine intake times to measure medication adherence. The software on a general computing device (eg, mobile device) receives the data from the body-worn sensor or patch for further processing and analysis of the behavioral and physiological metrics. The processed data is then sent to the user interface (UI) for display as well as being saved in a local record database for storage.

The provided text describes the Otsuka Digital Feedback Device-RW, which is an ingestible event marker with a wearable patch. The primary function highlighted is logging and trending medication intake times to aid in measuring medication adherence, as well as tracking physiological and behavioral metrics.

However, the document is an FDA 510(k) clearance letter and its associated summary. While it mentions "acceptance criteria" for "firmware verification, mechanical verification, electrical verification, biocompatibility evaluation, and system verification testing," it does not provide specific numerical acceptance criteria for the device's performance in its intended use (i.e., accurately tracking ingestion or medication adherence). It also clarifies that no new clinical studies were performed to support the changes in this submission, indicating that the performance data for the core functionality would likely be derived from previous predicate device studies or non-clinical testing.

Therefore, many of the requested details about a study proving the device meets acceptance criteria related to its clinical performance (e.g., accuracy of ingestion detection, improved human reader performance with AI) cannot be extracted directly from this document. The document focuses on showing substantial equivalence to a predicate device, based on similar technology, non-clinical testing, and existing clinical data from the predicate for some aspects.

Based on the provided text, here's what can be extracted and what cannot:

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

The document mentions acceptance criteria were met for:

• Firmware verification
• Mechanical verification
• Electrical verification
• Biocompatibility evaluation
• System verification testing

No specific numerical acceptance criteria or performance metrics related to the core function of "logging, tracking, and trending intake times" or "measuring medication adherence" are provided in this document. The document states "The results demonstrate that the acceptance criteria for firmware verification, mechanical verification, electrical verification, biocompatibility evaluation, and system verification testing were met," but it does not quantify these.

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

The document explicitly states: "However, new clinical studies were not performed to support the changes proposed in this submission." It implies that any performance data for the core functions relies on the predicate devices. Therefore, details about sample size, provenance, and retrospective/prospective nature of a new clinical test set are not available in this document.

The document mentions: "Clinical studies were conducted to support the expanded wear location of the patch to include the entire front abdomen." This suggests some clinical data was generated for this specific change, but details on sample size, design, or performance metrics for that specific study are not provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

Not applicable, as no new clinical study on the primary function is detailed, and the document does not describe efforts to establish ground truth with experts for the device's performance (e.g., in medication adherence).

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

Not applicable, as no new clinical study on the primary function is detailed.

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 an ingestible event marker and wearable sensor, not an AI-assisted diagnostic imaging device that would typically involve human "readers" interpreting images. Its purpose is data collection (ingestion times, physiological metrics), not interpretation by human experts.

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

The device itself is essentially a "standalone" data collection system. It's intended to continuously log data without constant human intervention.

• Performance: The document indicates non-clinical verification and validation testing was done (firmware, mechanical, electrical, system testing), and those "acceptance criteria were met." However, no quantifiable performance metrics for the device's accuracy in detecting ingestion events are provided.

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

The document does not specify the method for establishing ground truth for ingestion events or medication adherence from any past studies. Given the nature of the device (detecting ingestion), ground truth likely involves direct observation or very high-fidelity data from a controlled setting.

8. The sample size for the training set

Not applicable to this document. The document describes a medical device cleared via 510(k), not an AI/ML model for which a "training set" in the computational sense would be explicitly outlined. While the device contains firmware and software, the development process and data used for its internal algorithms are not detailed here as a separate "training set."

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

Not applicable, as explained in point 8.

Ask a specific question about this device

The ID-Cap System consists of a wearable reader for ambulatory recording of events signaled by swallowing the ID-Capsule which contains the ID-Tag, an ingestible sensor. The ID-Cap System is intended to log, track, and trend intake times and enables unattended data collection for clinical applications. The ID-Cap System may be used in any instance where quantifiable analysis of ingestion events, including events signaled by the co-incidence with or co-ingestion with the ID-Capsule, is desirable.

The ID-Cap System is an ingestible event marker. It utilizes an in vivo communications technology that emits a very low power radio frequency (RF) digital message from within the patient after a sensor is ingested and detects the signal using a wearable Reader. The ID-Cap System is comprised of the ID-Capsule, the ID-Cap Reader, and related software which allows data to be displayed for the patient and clinician. The ID-Capsule consists of a standard pharmaceutical capsule shell containing the ID-Tag (the ingestible sensor). The ID-Cap Reader is a wearable device, which receives the message from the ID-Tag, verifies the message as being a valid ingestion event, and forwards the data using the Bluetooth Low Energy (BLE) protocol to data display systems utilized by clinicians and patients.

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

Device Name: ID-Cap System
Predicate Device: Ingestion Event Marker (IEM) Data recorder (Patch) (K150494 - Proteus Digital Health Feedback Device)

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list "acceptance criteria" as a separate, quantitative table. Instead, it presents a comparison with a predicate device and implicitly sets performance targets based on the predicate's performance and the device's intended function. The "Comments" column in Table 5 functions as a statement of equivalence or acceptable difference.

Here's a re-organized table focusing on the performance metrics presented, drawing implications of acceptance from the comparison:

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

• Test Set Sample Size: Not explicitly stated as a single number for a "test set." The clinical studies included participants (18 – 79 years old, mean 41.9 years, stratified by gender and BMI). The specific number of ingestions or unique patients contributing to the PDA/NDA calculations is not provided, only the resulting percentages.
• Data Provenance: Retrospective or prospective is not explicitly stated. However, the mention of "clinical studies" and "direct observation clinical study" implies prospective clinical data collection. The country of origin of the data is not specified.

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

Not applicable. The ground truth for this device (ingestion detection) appears to be established by direct observation in clinical settings, rather than expert review of independent data (like image annotations by radiologists). The device's function is to detect an event that is directly observable.

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

Not applicable for this type of device. The "ground truth" for ingestion is likely established by direct clinician observation or participant logging combined with the device's own detection, not by independent adjudication of outputs from a black-box system.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size

Not applicable. This device is an ingestible event marker, not an AI-powered diagnostic imaging tool that assists human readers. Its primary function is automated detection, not interpretation requiring human readers.

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

Yes, the core performance metrics (Positive Detection Accuracy, Negative Detection Accuracy, Time to Detect, Duration of Detected Signal) are presented as standalone algorithm performance (the ID-Cap System's ability to detect ingestion events). The device is designed for "unattended data collection," indicating a standalone operational mode.

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

The ground truth for ingestion events was established through direct observation in clinical studies. For example, the "Time to Detect" and "Duration of Detected Signal" metrics were derived from a "direct observation clinical study." Proper excretion was confirmed by post-ingestion X-rays.

8. The sample size for the training set

Not applicable. This document describes a medical device, not a machine learning algorithm that requires a "training set" in the conventional sense. The "performance testing" section refers to clinical studies and bench testing, not an ML model's training data.

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

Not applicable, as this is not a machine learning model. Performance validation was done through clinical studies and extensive engineering/bench testing against established standards and internal requirements.

Ask a specific question about this device

The Proteus® Digital Health Feedback Device consists of a miniaturized, wearable sensor for ambulatory recording of physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity (body position), and time-stamped patient-logged events, including events signaled by the co-incidence with, or co-ingestion with, the ingestible sensor accessory. When the ingestible sensor is ingested, the Proteus Digital Health Feedback Device is intended to log, track and trend intake times. When co-ingested with medication, the tracking and trending of intake times may be used as an aid to measure medication adherence. The Proteus Digital Health Feedback Device may be used in any instance where quantifiable analysis of event-associated physiological and behavioral metrics is desirable, and enables unattended data collection for clinical and research applications.

The Proteus Digital Health Feedback Device consists of a wearable sensor, an ingestible sensor, and a software application.

The Proteus wearable sensor is a body-worn sensor that collects physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity (body position), skin temperature, and time-stamped user-logged events signaled by the co-incidence with, or coingestion with, the Proteus ingestible sensor. The display application of the Proteus Digital Health Feedback Device may be used to analyze circadian rhythms and patterns.

The ingestible sensor is embedded inside an inactive tablet. (the Proteus Pill or sensorenabled pill) for ease of handling and swallowing. After the ingestible sensor reaches the stomach, it activates and communicates its presence with a unique identifier to the wearable sensor. When the ingestible sensor is co-ingested with medication, the Proteus device is intended to log, track, and trend medicine intake times as an aid to measure medication adherence.

The software application receives the data from the wearable sensor for further processing and analysis of the physiological and behavioral metrics. The processed data is then sent to the user interface (UI) for display as well as to Proteus databases for storage and sharing.

Here's an analysis of the provided text, focusing on the acceptance criteria and study information for the Proteus Digital Health Feedback Device:

Based on the provided text, a comprehensive study proving the device meets acceptance criteria, including specific quantifiable acceptance criteria and detailed study results, is not present. The document is a 510(k) summary for FDA clearance, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical study report with acceptance criteria.

However, we can extract information regarding non-clinical performance data and the general approach to validating different aspects of the device.

1. Table of Acceptance Criteria and Reported Device Performance

As noted above, explicit acceptance criteria with specific thresholds are not provided in this document. The text describes how different parameters were validated or quantified, rather than defining performance targets.

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

• Test Set Sample Size: Not specified. The document mentions "tested using selected guidelines" or "validated against," but does not provide details on the number of subjects or samples used in these non-clinical evaluations.
• Data Provenance: The document does not specify the country of origin. It indicates that the data are from "Non-Clinical Performance Data" and implies laboratory or controlled test environments. The data is retrospective in the sense that it was collected prior to this submission for FDA clearance.

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

• Not Applicable / Not Specified. The evaluations described are primarily technical performance tests of sensors and algorithms, not evaluations against human expert ground truth for diagnostic purposes. For example, the accelerometer was "validated against a known acceleration," which implies a controlled physical standard rather than expert consensus. Similarly, heart rate was tested against "selected guidelines set forth in the ANSI/AAMI EC 13 standard," which refers to industry standards, not human interpreters.

4. Adjudication Method for the Test Set

• Not Applicable / Not Specified. Since there is no mention of human experts establishing ground truth or making judgments, there is no adjudication method described.

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, an MRMC comparative effectiveness study was not done or described. This device focuses on objective physiological and behavioral data collection, not on aiding human interpretation of complex medical images or data that typically require MRMC studies.

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

• Yes, standalone non-clinical performance data was done for individual device components. The section "Summary of Non-Clinical Performance Data" describes evaluating the accelerometer, biopotential low-frequency amplifier (for heart rate), thermistor, and ingestible sensor activation/lifetime independently. For example, "The three-axis accelerometer provided motion and angle relative to gravity (body position) data and was validated against a known acceleration applied against each of its three axes." This indicates direct evaluation of the device's technical output.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• The ground truth used was primarily controlled physical inputs, laboratory standards, and industry guidelines.
  • For the accelerometer: "known acceleration."
  • For heart rate: "selected guidelines set forth in the ANSI/AAMI EC 13 standard."
  • For skin temperature: "compared to a reference resistor" (implying a controlled reference temperature/voltage).
  • For ingestible sensor: Testing "activation time and lifetime" implies comparison against internal specifications or direct observation.

8. The Sample Size for the Training Set

• Not specified. The document does not provide details on a separate training set for any algorithms. It describes a "proprietary algorithm" for heart rate, but does not detail its development or training data. Given the context of a 510(k) for a sensor device, emphasis would be on device performance rather than complex machine learning algorithm training.

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

• Not specified, as no training set details are provided.

Ask a specific question about this device

The Proteus Patch is a miniaturized, wearable data-logger for ambulatory recording of physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity, and time-stamped, patient-logged events, including events signaled by swallowing the Ingestible Sensor accessory. The Proteus Patch enables unattended data collection for clinical and research applications. The Proteus Patch may be used in any instance where quantifiable analysis of event-associated physiological and behavioral metrics is desirable.

The Proteus Patch is a body-worn sensor that collects physiological and behavioral metrics such as heart tate, activity, body angle relative to gravity, and time-stamped user-logged events generated by swallowing the Proteus Ingestible Sensor. The Ingestible Sensor is embedded inside an inactive tablet (the Pill) for ease of handling and swallowing. Once the Ingestible Sensor reaches the stomach, it activates and communicates its presence and unique identifier to the Patch stores and wirelessly sends the physiological, behavioral, event, and ingestion data to a general computing device for display.

Here's an analysis of the acceptance criteria and study information provided in the document:

1. Table of Acceptance Criteria and Reported Device Performance

The provided summary does not explicitly state formal "acceptance criteria" with specific thresholds for performance. Instead, it describes general methods for validation and testing. Therefore, the table below interprets the "acceptance criteria" as the method of validation itself rather than a numerical threshold, and the "reported device performance" as the statement of successful validation.

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

The document does not provide specific sample sizes for test sets used for the individual performance validations. It mentions testing methods but not the number of subjects or items tested.
The data provenance is not specified (e.g., country of origin, retrospective/prospective). This is a non-clinical evaluation, so "patients" might not have been involved in all tests.

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

This document describes technical device validation against standards or known inputs (e.g., "known acceleration"). It does not involve human expert interpretation of data to establish ground truth in the way a diagnostic AI system would. Therefore, this information is not applicable or provided.

4. Adjudication Method for the Test Set

Not applicable, as this is a technical validation against established standards and known physical inputs, not a subjective interpretation requiring expert adjudication.

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

No, an MRMC comparative effectiveness study was not done. The document states: "No additional clinical data were required to confirm substantial equivalence to predicate device." This indicates the device was cleared based on non-clinical performance and technological characteristics in comparison to a predicate, not through a study involving human readers or AI assistance.

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

The performance data described (heart rate algorithm, accelerometer validation, ingestion sensor testing) are inherently "standalone" in the sense that they are assessing the device's technical capabilities without a human-in-the-loop for the primary validation. The device's overall intended use does involve data collection for clinical and research applications, implying potential future human interpretation, but the core performance described here is the device's ability to accurately log these metrics.

7. The Type of Ground Truth Used

The ground truth used for validation was primarily:

• Established industry standards: For heart rate, "guidelines set forth in the ANSI/AAMI EC 13 standard."
• Known physical inputs: For activity/body angle, "a known acceleration applied against each of its three axes."
• Device-specific measurements/specifications: For the ingestion sensor, "activation time and lifetime after activation" would be measured against the sensor's designed parameters.

8. The Sample Size for the Training Set

The document does not mention any "training set" in the context of machine learning. The device utilizes algorithms (e.g., modified Hamilton-Tompkins for R-wave frequency) that would have been developed and potentially trained previously, but the details of such training (including sample size) are not provided or relevant to this 510(k) summary, which focuses on validation rather than algorithm development.

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

As no training set is discussed, this information is not provided.

Ask a specific question about this device

The Proteus® Patch is a miniaturized, wearable data-logger for ambulatory recording of physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity, and time-stamped, patient-logged events, including events signaled by swallowing the Ingestible Sensor accessory. The Proteus Patch enables unattended data collection for clinical and research applications. The Proteus Patch may be used in any instance where quantifiable analysis of event-associated physiological and behavioral metrics is desirable.

The Proteus Patch is a body-worn sensor that collects physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity, and time-stamped user-logged events generated by swallowing the Proteus Ingestible Sensor. The Ingestible Sensor is embedded inside an inactive tablet (the Pill) for ease of handling and swallowing. Once the Ingestible Sensor reaches the stomach, it activates and communicates its presence and unique identifier to the Patch. The Patch stores and wirelessly sends the physiological, behavioral, event, and ingestion data to a general computing device for display.

Here's a breakdown of the acceptance criteria and the study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state quantitative "acceptance criteria" in the traditional sense (e.g., minimum sensitivity, specificity, or accuracy percentages). Instead, it focuses on validating the functionality of different components. Therefore, the table below reflects the validation methods and what was "proven" or "tested."

2. Sample Size for the Test Set and Data Provenance

The document does not explicitly state the sample size used for the test set for each validation. It also does not provide details on data provenance (e.g., country of origin, retrospective or prospective nature of the data). The validation efforts appear to be laboratory-based rather than real-world patient data in some cases.

• Accelerometer: "validated against a known acceleration applied against each of its three axes." This suggests controlled laboratory testing.
• Heart Rate: "tested using guidelines set forth in the ANSI/AAMI EC 13 standard." This standard typically involves using databases or simulators for testing, but details are not provided.
• Ingestion Sensor: "tested for activation time and lifetime after activation." This would likely be laboratory testing of the ingestible sensor units.

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

The document does not mention the use of human experts to establish ground truth for the device's performance. The validation appears to be against established physical standards, known inputs, or existing guidelines (like ANSI/AAMI EC 13).

4. Adjudication Method for the Test Set

No adjudication method is mentioned. Given the nature of the "testing" described (validation against known physical inputs and standards), an adjudication process among human experts would not be applicable.

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

No MRMC comparative effectiveness study was done or mentioned. The device, the Proteus Patch Including Ingestible Sensor, is a data-logger and sensor, not an interpretive AI system that assists human readers. Therefore, the concept of human readers improving with or without AI assistance is not applicable to this device.

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

The described performance evaluation is a standalone (algorithm only) evaluation. The validation tests were conducted on the device's sensors and algorithms directly (e.g., accelerometer output, R-wave frequency detection, sensor activation). There is no human-in-the-loop component mentioned in these performance assessments.

7. Type of Ground Truth Used

The ground truth used in the validation studies appears to be:

• Known physical inputs/standards: For the accelerometer, "known acceleration applied against each of its three axes."
• Standardized guidelines/algorithms: For heart rate, "guidelines set forth in the ANSI/AAMI EC 13 standard" and a "modified Hamilton-Tompkins algorithm."
• Intrinsic device characteristics/specifications: For the ingestion sensor, "activation time and lifetime after activation" refers to its expected functional behavior.

There is no mention of expert consensus, pathology, or outcomes data being used as ground truth for these specific performance validations.

8. Sample Size for the Training Set

The document does not specify a "training set" or its sample size. This device is described as a "data-logger" and sensor system. While it contains algorithms (e.g., for R-wave detection), there is no indication that these algorithms were developed using a machine learning approach that would typically involve a distinct training set. The focus is on validation against established engineering and physiological standards.

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

Since no training set and corresponding machine learning approach are explicitly described, the method for establishing ground truth for a training set is not provided.

Ask a specific question about this device

The Proteus® Patch, also called the Patch, is a miniaturized, wearable data-logger for ambulatory recording of physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity, and time-stamped, patient-logged events, including events signaled by swallowing the Ingestible Sensor accessory. The Proteus Patch enables unattended data collection for clinical and research applications. The Proteus Patch may be used in any instance where quantifiable analysis of event-associated physiological and behavioral heart rate, activity, and body position is desirable.

Like the Proteus Personal Monitor (K113070), the Proteus Patch is a body-worn sensor that collects physiological and behavioral metrics including heart rate, activity, body angle, and time-stamped user-logged events generated by swallowing the Proteus Ingestible Sensor. The Ingestible Sensor is embedded inside an inactive tablet (the Pill) for ease of handling and swallowing. Once the Ingestible Sensor reaches the stomach, it activates and communicates its presence and unique identifier to the Patch. The Patch stores and wirelessly sends the physiologic, event, accelerometry, and Ingestible Sensor data to a general computing device for display.

Here's a breakdown of the acceptance criteria and study information for the Proteus Patch including Ingestible Sensor, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document provides functional descriptions and validation methods for different parameters, but does not present explicit, quantitative acceptance criteria in a table format. However, based on the Summary of Non-Clinical Performance Data, we can infer the tested characteristics and the type of validation performed.

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

The summary states:

• "No additional clinical data were required to confirm substantial equivalence to predicate devices."
• The "Summary of Non-Clinical Performance Data" describes laboratory testing.

Therefore, there appears to be no distinct clinical test set used for this 510(k) submission as no additional clinical data was required. The "testing" mentioned refers to non-clinical, laboratory-based validation.

• Sample Size for Test Set: Not applicable for clinical data. For non-clinical validation, specific sample sizes for the accelerometer and Ingestible Sensor tests are not provided in this summary.
• Data Provenance: Not applicable for clinical data. The non-clinical testing would have been performed by the manufacturer, Proteus Digital Health, Inc.

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

Not applicable, as no additional clinical test set was used, and ground truth in this context would likely refer to clinical expert consensus. For the non-clinical tests:

• For the accelerometer, ground truth was "known acceleration."
• For the heart rate, the ANSI/AAMI EC 13 standard provides the reference for accurate ECG measurement.
• For the ingestible sensor, activation time and lifetime would be objectively measured, not requiring expert consensus for ground truth.

4. Adjudication Method for the Test Set

Not applicable, as no clinical test set requiring human adjudication was utilized for this submission based on the provided information.

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 performed or submitted as part of this 510(k). The document explicitly states "No additional clinical data were required." Therefore, there is no effect size reported for human readers improving with or without AI assistance.

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

This device does not involve an "algorithm only" or "AI" in the sense of image interpretation or diagnostic assistance. It's a data logger and sensor. The "non-clinical performance data" describes the validation of the device's sensing capabilities (accelerometer, heart rate measurement algorithm, ingestible sensor activation). These are standalone functional tests of the device components.

• Heart Rate Algorithm Standalone Performance: The biopotential low-frequency amplifier and its accompanying algorithm (modified Hamilton-Tompkins) were "tested using guidelines set forth in the ANSI/AAMI EC 13 standard." This indicates a standalone validation of the algorithm's performance against a known standard.

7. The Type of Ground Truth Used

• Accelerometer: "Known acceleration applied against each of its three axes." (Physics-based objective measurement)
• Heart Rate: Adherence to "ANSI/AAMI EC 13 standard" which provides criteria for accurate ECG measurement, implying a reference standard for ground truth.
• Ingestible Event Marker: Objective measurement of "activation time and lifetime after activation."

8. The Sample Size for the Training Set

The document does not mention any "training set." This type of device (data logger, physiological sensor, ingestible event marker) typically undergoes engineering verification and validation, not machine learning model training.

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

Not applicable, as there is no mention of a training set for machine learning.

Ask a specific question about this device

The Proteus Personal Monitor is a miniaturized, wearable data-logger for ambulatory recording of heart rate, activity, body angle relative to gravity, and time-stamped, patientlogged events, including events signaled by swallowing the Ingestion Event Marker (IEM) accessory. The Proteus Personal Monitor enables unattended data collection for clinical and research applications. The Proteus Personal Monitor may be used in any instance where quantiffable analysis of event-associated heart rate, activity, and body position is desirable.

The Proteus Personal Monitor, also called the "Patch", is a body-worn sensor that collects physiological and behavioral metrics including heart rate, activity, body angle and time-stamped user-logged events generated when a user marks an event by swallowing an Ingestion Event Marker (IEM) or by manually pressing an event marker button on the Patch. The Patch stores and wirelessly sends the IEM data to a general computing device.

The Proteus Personal Monitor Ingestion Event Marker system is comprised of three main subsystems; (1) the ingestion event marker (IEM), (2) the data recorder (Patch), and (3) the Proteus software.

1. Ingestion Event Marker (IEM)
   The grain-of-sand sized IEM is designed to communicate the time-stamped confirmation of IEM device ingestion as a unique identifier to the Proteus Personal Monitor worn on the skin. The ingestion signal is communicated via volume conduction communication also known as intrabody communication. The IEM is attached to an inert pharmaceutical excipient tablet for ease of handling and swallowability.

2. Proteus Personal Monitor (Patch)
   The Proteus Personal Monitor (Patch) receives, stores, and wirelessly sends ingestion confirmation data to a general computing device.

3. Software
   The Proteus software is used to pair the Patch with a mobile computing device. The software organizes and displays ingestion events.

Here's the breakdown of the acceptance criteria and the study details for the Proteus Personal Monitor including Ingestion Event Marker:

Acceptance Criteria and Device Performance

Study Information

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

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

• Human Clinical Test Set:

  • Number of subjects: 219 subjects ingested IEMs across multiple studies, with 254 subjects wearing the Proteus Personal Monitor (Patch) in total.
  • Number of IEM ingestions: 11,655 cumulative IEM ingestions.
  • Subject/days of system use: 3,811 cumulative subject-days.
  • Data Provenance: Not explicitly stated, but the mention of "The PROMITTER substudy was conducted on the Proteus campus and enrolled 5 healthy volunteer subjects" suggests some studies were conducted in the US. The overall clinical data likely comes from studies conducted by Proteus Biomedical, Inc. (based in Redwood City, CA). The data appears to be prospective as it involves active participation in clinical trials.

• Animal Test Set:

  • Number of animals: Forty-two (42) in-vivo studies in rodent, canine, and porcine models. The specific number of animals per study is not detailed.
  • Data Provenance: Not explicitly stated, but likely from studies commissioned by Proteus Biomedical, Inc. and conducted in laboratory settings. These are prospective animal studies.

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

• Clinical Studies (IEM Ingestion Detection): The document does not specify the number of experts or their qualifications for establishing the ground truth of IEM ingestion events in the clinical studies. Typically, this would involve direct observation, patient logs, or other objective measures as the ground truth for "ingestion event" rather than expert interpretation of a signal.
• ECG Performance Testing: For the MIT-BIH arrhythmia database, the ground truth is established by cardiologists and is inherent to the database itself. For the PROMITTER substudy, it is not specified how the ECG ground truth was established, but typically this would involve a reference ECG device and interpretation by cardiology experts.

4. Adjudication method for the test set:

• The document does not explicitly describe an adjudication method (like 2+1 or 3+1) for disagreement among experts, which typically applies to subjective interpretations. For IEM ingestion events, the ground truth is likely directly observed or logged, rather than relying on interpretation that would require adjudication.

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, a multi-reader multi-case (MRMC) comparative effectiveness study focusing on human readers improving with or without AI assistance was not conducted or reported in this document. The device's primary function is to automatically detect ingestion events, not to assist human readers in interpreting medical images or other data that typically involves MRMC studies.

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

• Yes, the core performance metrics "Positive Detection Accuracy (PDA)" and "Negative Detection Accuracy (NDA)" for IEM ingestion detection represent the standalone (algorithm only) performance of the Proteus Personal Monitor system. The system automatically detects and logs these events without direct human-in-the-loop intervention for each individual ingestion detection.

7. The type of ground truth used:

• IEM Ingestion Detection: The ground truth for IEM ingestion events in human clinical studies is implicitly based on documented ingestions by the subjects. This would likely involve patient logs, direct observation during controlled settings, or other objective confirmation of "patient-logged events" as described in the Indications for Use.
• Physiological Measurements (ECG, Accelerometer, Respiratory Rate): For these, the ground truth was established using:
  • Standard databases: MIT-BIH arrhythmia database for ECG performance.
  • Reference measurements: Applied values for accelerometers, controlled breathing rates for respiratory rate.
• Safety (Toxicity, Biocompatibility): Ground truth established through established laboratory testing protocols, histopathology, clinical observations, and established toxicology assessments by experts (e.g., Gradient Corp for metal toxicology).

8. The sample size for the training set:

• The document does not provide details on the sample size used for training the algorithms. This information is typically proprietary or not included in regulatory summaries focused on validation.

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

• The document does not provide details on how the ground truth for the training set was established. Similar to the test set, it would likely involve objective data related to IEM ingestion, physiological signals with known parameters, or established clinical diagnoses for specific outcomes targeted by any machine learning components.

Ask a specific question about this device