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The AeviceMD is a non-invasive battery-operated device, including a wearable component, intended to longitudinally acquire, record and store lung sounds from pediatric patients (aged 3 years and above) in a clinical or non-clinical setting. The device stores the data for later playback, review, and analysis by a clinician and comparison with earlier data from the same patient.

The AeviceMD is designed as an electronic stethoscope to acquire and record lung sounds from users for healthcare professionals (HCP) to playback and interpret the sounds recorded. AeviceMD does not contain any alarm feature and it is not intended for emergency use. It is also not a sleep apnea device. The device is not intended for self-diagnosis.
The AeviceMD consists of hardware and embedded software. It is a five-part system that includes the following components:

1. AeviceMD Sensor – an embedded electronic wearable device that detects and records lung sounds and transmits data to an electronic gateway via Bluetooth.
2. AeviceMD Silicone Patch – silicone patch that houses and attaches the Sensor to the user's body (i.e., chest). This silicone patch undergoes biocompatibility testings which allow AeviceMD Sensor to be worn on the skin.
3. AeviceMD Docking Station – gateway device that serves as a computational hub and linkage from the Sensor to the Cloud Platform, and as a charger for the Sensor.
4. AeviceMD App (for patients) / AeviceMD HCP Web App (for healthcare professionals) - The AeviceMD App is a mobile app that downloads the post-processed information from the Cloud Platform and presents users with their recorded lung sounds at the auscultation locations which they can share with their HCP during their next consultation. A separate app, AeviceMD HCP Web App is tailored for HCP to review their patient's data in a clinical setting.
5. AeviceMD Cloud Platform – secure cloud server that receives data from gateway units and analyzes user's data using meaningful output information.

I'm sorry, but the provided FDA 510(k) Clearance Letter for AeviceMD (K243603) does not contain the detailed information necessary to fully answer your request.

Specifically, the document does not include any acceptance criteria or a study demonstrating that the device meets such criteria. It primarily focuses on:

• Substantial Equivalence: Comparing the AeviceMD to a predicate device (AeviceMD K223382) and a reference device (Eko CORE K200776) to establish similar intended use and technological characteristics.
• Non-Clinical Performance Data: Listing the standards and additional testing performed (e.g., biocompatibility, electrical safety, usability, shipping validation, cleaning validation, frequency response test, stethoscope performance test). However, it does not provide the results of these tests or specific performance metrics that could be construed as acceptance criteria.
• Indications for Use: Defining what the device is intended for.

Therefore, I cannot extract the following information from the provided text:

1. A table of acceptance criteria and the reported device performance: This information is not present.
2. Sample size used for the test set and the data provenance: While a "Stethoscope Performance Test against a 510(k) cleared reference stethoscope" is mentioned, no details about the sample size, data provenance, or the results are provided. The statement "The reference device was used to demonstrate effective performance in a pediatric population aged 3 years and above" suggests a study was done, but no details are given.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not mentioned.
4. Adjudication method: Not mentioned.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size: Not mentioned. The device is for recording and analysis by a clinician, but no study on AI assistance is detailed.
6. If a standalone performance (i.e., algorithm only without human-in-the-loop performance) was done: The document describes the device as recording sounds for later "playback, review, and analysis by a clinician," implying human-in-the-loop. However, it also mentions the "AeviceMD Cloud Platform" analyzes user data using "meaningful output information," which could hint at an algorithm, but no standalone performance data for such an algorithm is provided.
7. The type of ground truth used: Not mentioned.
8. The sample size for the training set: Not mentioned.
9. How the ground truth for the training set was established: Not mentioned.

In summary, the provided document from the FDA clearance process primarily focuses on demonstrating substantial equivalence through comparison with existing devices and compliance with safety and performance standards, rather than detailing a specific clinical performance study with acceptance criteria and results.

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The CORE 500 Digital Stethoscope is intended to be used by clinicians or lay users to electronically amplify, filter, and transfer body sounds and three lead electrocardiogram (ECG) waveforms. The CORE 500 Digital Stethoscope also displays ECG waveforms and heart rate on the display and accompanying mobile application (when prescribed or used under the care of a clinician or by lay users).

A lay user is not intended to interpret or take clinical action based on the device output without consulting with a qualified healthcare professional.

CORE 500 Digital Stethoscope (CORE 500) is an electronic stethoscope with integrated electrodes for electrocardiogram (ECG). The device consists of a chestpiece, detachable earpiece (Eko Earpiece) and a mobile application (Eko App) and is intended as a digital auscultation tool on patients requiring physical assessment by the clinicians or lay users. CORE 500 provides the ability to amplify, filter, and transfer body sounds with the chestpiece diaphragm, and three lead ECG through electrodes integrated around the chestpiece. The device can be used in a professional healthcare facility and for home use.

CORE 500 features three auscultation modes for a better auscultation experience by filtering acoustic data and enhancing the primary frequency range of particular body sounds: Cardiac Mode for heart sounds, Pulmonary Mode for lung sounds, and Wide Band Mode for general auscultation. CORE 500 also detects and computes the heart rate in real time based on the phonocardiogram (PCG) data.

This FDA 510(k) summary for the Eko Health, Inc. CORE 500 Digital Stethoscope (K233609) describes the device's technical specifications and how it compares to a predicate device. Regarding acceptance criteria and detailed study results, the document provides a general overview rather than specific performance metrics.

Here's an analysis of the provided information concerning acceptance criteria and study details:

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

The document does not provide a table of acceptance criteria with corresponding reported device performance values for the CORE 500 Digital Stethoscope in the way one might expect for a clinical performance study. Instead, it lists the types of nonclinical testing performed and asserts that the device complies with standards or demonstrates performance.

Here's a summary of the reported performance without specific numerical acceptance criteria from the document:

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

The document does not provide specific sample sizes for test sets, data provenance, or whether studies were retrospective or prospective. The performance data section focuses on nonclinical testing.

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

This information is not provided in the document. The performance data is described as "nonclinical testing" and does not appear to involve expert-adjudicated ground truth as typically found in clinical studies assessing diagnostic accuracy.

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

This information is not provided. As the document focuses on nonclinical performance, an adjudication method on a clinical test set is not 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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. The device, the CORE 500 Digital Stethoscope, is primarily an electronic stethoscope for amplifying, filtering, and transferring body sounds and ECG waveforms, and displaying ECG and heart rate. It is not described as having an AI diagnostic interpretation component that would typically be evaluated in an MRMC study with human readers.

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

The document does not explicitly state that a standalone (algorithm only) performance study was done for any specific AI functionality. The device displays ECG waveforms and heart rate, but the document does not describe it as having an autonomous diagnostic algorithm for complex conditions.

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

Given that the performance data described is "nonclinical testing" (bench testing, biocompatibility, electrical safety, software V&V, usability), the concept of "ground truth" as it applies to clinical diagnostic accuracy (e.g., expert consensus, pathology) is not applicable or described in this section. The testing would have focused on meeting technical specifications and regulatory standards.

8. The sample size for the training set

The document does not mention a training set or its sample size. This type of information would typically be provided for devices involving machine learning or AI algorithms with extensive training phases, which is not the primary focus of the performance data in this submission.

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

Since no training set is mentioned (see point 8), there is no information on how ground truth for a training set was established.

Summary of Device and Performance Context:

The K233609 submission for the CORE 500 Digital Stethoscope primarily focuses on demonstrating substantial equivalence to its predicate device (K230111) and a reference device (K200776), particularly for its expanded "Over-The-Counter Use" and inclusion of "lay users." The performance data provided are centered on foundational nonclinical tests to ensure safety, efficacy, and compliance with general device regulations and standards. It's not a submission for a novel diagnostic AI algorithm requiring extensive clinical performance studies with ground truth establishment by experts. The "nonclinical testing" confirms the device's technical functionality, biocompatibility, electrical safety, software validation, and usability for its intended purpose of amplifying, filtering, and transferring body sounds and ECG waveforms, and displaying basic heart rate and ECG.

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The AeviceMD is a non-invasive battery-operated device, including a wearable component, intended to longitudinally acquire, record and store lung sounds from adult patients in a clinical setting. The device stores the data for later playback, review, and analysis by a clinician and comparison with earlier data from the same patient.

The AeviceMD is designed as an electronic stethoscope to acquire and record lung sounds from users for healthcare professionals (HCP) to playback and interpret the sounds recorded. AeviceMD does not contain any alarm feature and it is not intended for emergency use. It is also not a sleep apnea device. The device is not intended for self-diagnosis.

The AeviceMD consists of hardware and embedded software. It is a five-part system that includes the following components:

1. AeviceMD Sensor – an embedded electronic wearable device that detects and records lung sounds and transmits data to an electronic gateway via Bluetooth.

2. AeviceMD Silicone Patch - silicone patch that houses and attaches the Sensor to the user's body (i.e., chest). This silicone patch undergoes biocompatibility testings which allow AeviceMD Sensor to be worn on the skin.

3. AeviceMD Docking Station - gateway device that serves as a computational hub and linkage from the Sensor to the Cloud Platform, and as a charqer for the Sensor.

4. AeviceMD App (for patients) / AeviceMD HCP Web App (for healthcare professionals in a clinical setting) - The AeviceMD App is a mobile app that downloads the post-processed information from the Cloud Platform and presents users with their recorded lung sounds at the auscultation locations which they can share with their HCP during their next consultation. A separate app, AeviceMD HCP Web App is tailored for HCP to review their patient's data in a clinical setting.

5. AeviceMD Cloud Platform – secure cloud server that receives data from gateway units and analyzes user's data using meaningful output information.

The AeviceMD is a non-invasive, battery-operated device intended to acquire, record, and store lung sounds from adult patients for later review and analysis by a clinician.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document details non-clinical performance tests but does not explicitly state specific quantitative acceptance criteria or corresponding reported device performance values in a table format for the device's primary function of acquiring and recording lung sounds.

However, based on the non-clinical performance data section, the device likely aims to perform "as well as" a legally marketed predicate device, implying equivalence in its core function. The "Stethoscope Performance Test against a 510(k) cleared reference stethoscope" suggests that the AeviceMD's acoustic performance was compared to an already cleared device.

Implicit Acceptance Criteria (inferred from the document):

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

The document does not specify the sample size for any "test set" in the context of clinical or performance data for lung sound acquisition accuracy. The studies mentioned are primarily non-clinical validation tests (e.g., biocompatibility, electrical safety, usability, software, shipping, cleaning, frequency response, stethoscope performance comparison). These typically involve specific test conditions and components rather than human subject data sets in the way an AI algorithm test set would.

For the "Stethoscope Performance Test against a 510(k) cleared reference stethoscope", the exact number of data points or recordings used for comparison is not provided.

The data provenance for these non-clinical tests is not explicitly stated in terms of country of origin but would generally originate from the manufacturer's testing facilities or accredited third-party labs carrying out these standardized tests. The studies are described as "non-clinical performance data," implying laboratory or engineering testing rather than retrospective or prospective clinical human studies to evaluate diagnostic performance.

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

This information is not provided in the document. The document describes a device for acquiring, recording, and storing lung sounds for later playback, review, and analysis by a clinician. It does not mention any automated interpretation or diagnostic capabilities that would necessitate a ground truth established by experts interpreting sounds. Therefore, there's no mention of experts establishing a ground truth for diagnostic accuracy for the device itself.

4. Adjudication method for the test set

Not applicable, as no expert-adjudicated test set for diagnostic performance is mentioned.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No MRMC study is mentioned. The device's indications for use emphasize acquisition, recording, storage, playback, and review by a clinician, not AI-assisted interpretation or diagnosis. There is no mention of AI features intended to improve human reader performance.

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

No standalone algorithm performance study is mentioned. The device is a "Medical Magnetic Tape Recorder" and "Stethoscope, Electronic" intended for clinicians to interpret the recorded sounds, not for an algorithm to provide a standalone diagnosis. The device's cloud platform "analyzes user's data using meaningful output information," but the nature of this "meaningful output" is not specified to be diagnostic or requiring standalone performance evaluation in the context of this 510(k) summary.

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

Not applicable, as the device doesn't have a stated diagnostic function that requires ground truth for clinical accuracy. The "Stethoscope Performance Test" would likely use a reference cleared stethoscope as its "ground truth" for acoustic fidelity, rather than clinical ground truth like pathology or expert consensus on a diagnosis.

8. The sample size for the training set

Not applicable. The document does not describe the development or evaluation of a machine learning algorithm for diagnostic purposes that would require a "training set." The "AeviceMD Cloud Platform" is mentioned to "analyze user's data using meaningful output information," but the details of this analysis, particularly if it involves machine learning and a corresponding training set, are not provided or assessed in this 510(k) summary for substantial equivalence.

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

Not applicable, as no training set for a diagnostic algorithm is mentioned.

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The Eko Murmur Analysis Software (EMAS) is intended to provide decision support to clinicians in their evaluation of patients' heart sounds. The software analyzes heart sounds and phonocardiograms (and ECG signals, when available). The software will automatically detect murmurs that may be present, and the murmur timing and character, including S1, S2, innocent heart murmurs, structural heart murmurs, and the absence of a heart murmur.

The Eko Murmur Analysis Software is not intended as a sole means of diagnosis and is for use in environments where health care is provided by clinicians. The interpretations of heart sounds offered by the software are meant only to provide decision support to the clinician, who may use the result in conjunction with their own evaluation and clinical judgment. The interpretations are not diagnoses. The Eko Murmur Analysis Software is intended for use on pediatric and adult patients.

Eko Murmur Analysis Software (EMAS) is a cloud-based service that allows users to upload heart sound/phonocardiogram (PCG) and optional electrocardiogram (ECG) data via an application programming interface (API) for analysis. The software uses signal processing (such as waveform filtering), as well as algorithms derived from machine learning, to analyze the acquired data and generate clinical decision support output for clinicians. EMAS is designed to evaluate data derived by the company's two previously cleared devices, the Eko DUO (K170874) and Eko CORE (K151319, K200776). The heart sound data from those devices can be transmitted to the Eko Cloud using either the Eko mobile application or thirdparty applications that use a software development kit (SDK). The EMAS algorithm analyzes the heart sound data and outputs a JSON file with the algorithm results, which is passed down to the requesting application and displayed by the requesting application to the user in the humanreadable format.

The analysis will assess the signal quality of the phonocardiogram; detect heart murmurs and classify them as innocent or structural; determine the timing of S1 and S2 heart sounds; and distinguish between systolic and diastolic heart murmurs. As an integral part of a physical assessment, clinicians' interpretations of EMAS' output can help them rule in or out different pathological conditions in a patient.

The EMAS consists of the following algorithm components:

• Signal Quality Detection Algorithm:
  This pre-processing algorithm accepts as input the PCG sound from the API controller (e.g., a mobile smartphone application). The algorithm is used to classify PCG recordings based on their signal quality as good or poor.

• Heart Sound Timing Algorithm:
  This algorithm detects the presence and timing of specific heart sounds including S1, S2, the systole region, and the diastole region.

• Murmur Detection & Classification Algorithm: This algorithm is used to identify and classify heart sounds as having "No Murmur", an "Innocent Murmur" (i.e., not pathologic), or a "Structural Murmur" (i.e., pathologic).

• Murmur Timing Algorithm:

This algorithm is used to identify in which regions of the heart cycle (systole vs diastole) a heart murmur occurs if either an "Innocent Murmur" or "Structural Murmur" is identified by the Murmur Detection and Classification Algorithm.

Here's an analysis of the Eko Murmur Analysis Software (EMAS) acceptance criteria and the study proving its performance, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Size: The document does not explicitly state a separate "test set" size. However, it indicates that the clinical validation used a database of 2,380 unique heart sound recordings from 615 unique subjects.
  • Of these, "recordings identified as being good signal by the expert cardiologists" (meaning suitable for analysis) included:
    • 45.8% (approx. 1090 recordings) with a confirmed structural murmur.
    • 54.2% (approx. 1290 recordings) with confirmed no murmur or innocent murmur.
  • For heart sound timing, 299 heart sound recordings were annotated.
• Data Provenance: Retrospective analysis on a proprietary database. The country of origin is not specified, but the applicant (Eko Devices, Inc.) is based in Oakland, California, USA.

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

• Number of Experts: "Multiple cardiologists" were used. The exact number is not specified.
• Qualifications of Experts: "Cardiologists." No further details on their years of experience or specific subspecialties are provided.

4. Adjudication Method for the Test Set

• Recordings were "annotated by multiple cardiologists."
• There's no explicit mention of an adjudication method like 2+1 or 3+1. However, the ground truth for murmur classification was obtained via "pairing cardiologist annotations with gold standard echocardiogram," suggesting that the echocardiogram served as the definitive ground truth reference alongside expert opinion.

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 reported. The study focuses on the standalone performance of the EMAS algorithm against a ground truth. There is no information provided about human readers improving with or without AI assistance.

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

• Yes, a standalone study was done. The reported performance metrics (Sensitivity, Specificity, PPV) are directly attributed to the "EMAS algorithm testing" and represent the algorithm's performance against the established ground truth. The device is intended as "decision support" and "not intended as a sole means of diagnosis," indicating it operates standalone and then informs a clinician.

7. The Type of Ground Truth Used

• For Murmur Classification: Ground truth was established by pairing cardiologist annotations with gold standard echocardiogram.
• For S1/S2 Timing: Ground truth was established via expert cardiologist annotations.

8. The Sample Size for the Training Set

• The document explicitly states: "No study subjects included in the training datasets were included in the test database." However, it does not provide the sample size for the training set. It only mentions that the algorithms were validated using "retrospective analysis on a proprietary database."

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

• The document does not explicitly describe how the ground truth for the training set was established. It only refers to a "proprietary database" used for training and then tested on a separate, distinct set of subjects. Assuming a consistent approach, it's likely similar methods (expert annotations, potentially with echocardiogram correlation) were used, but this is not stated.

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The Alio Medical Remote Monitoring System is a wireless remote monitoring system intended for use by healthcare professionals to intermittently collect physiological data in home use settings. The data includes skin temperature, auscultation sound data and heart rate. Data is transmitted wirelessly from the SmartPatch wearable sensor to a web-based portal for the healthcare provider's (HCP) review.

The Alio Medical RMS is intended for use on general care patients who are 18 years of age or older. The SmartPatch sensor is indicated to measure skin temperature and pulse rate where clinically indicated. The SmartPatch sensor is indicated to record and transmit auscultation sound data where clinically indicated.

The device is not intended for use in critical care or other high-acuity environments. The Alio Medical RMS is a secondary, adjunct patient monitor and is not intended to replace existing standard-of-care patient monitoring practices.

Alio Medical Remote Monitoring System, or "Alio Medical RMS", utilizes a wearable device (SmartPatch) on the skin to gather physiological data and then transmits it to a device (Bedside Hub) located in the subject's home. The Bedside Hub then relays this raw data to the Alio Medical Cloud where it is processed and analyzed using Alio's proprietary algorithms. Data is accessible to Healthcare Professionals and the Alio clinical team via a web-based Clinician Portal. The SmartPatch and Bedside Hub are intended to be used on general care patients who are 18 years of age or older in a non-clinical environment. The web-based Clinical Portal is to be used by healthcare professionals in an office environment.

The Alio Medical Remote Monitoring System includes the following components:

• SmartPatch
• Bedside Hub
• Alio Medical Cloud (backend only - not user facing)
• Clinician Portal

SmartPatch: A flexible, silicone-encased patch that can be worn where clinically indicated for up to seven days at a time. It houses numerous sensor technologies, which include a microphone, accelerometer, temperature sensors, and a PPG sensors collect physiological data including skin temperature, auscultation sound data, and heart rate. Data is transmitted to the Cloud, via the Hub, where it is analyzed and sent to a Healthcare Professional via the Web Portal.

Bedside Hub: The Bedside Hub has the form and finish of an at-home device. It automatically communicates with the activated SmartPatch and uploads physiological data to the Alio Medical Cloud.

Alio Medical Cloud: The Cloud features a database that supports storage, analytics, system monitoring and visualization capabilities. The Alio Medical Cloud is encrypted and HIPAA compliant. All patient data is fully traceable to device and patient ID via the database.

Clinician Portal: The Clinician Portal is the interface tool between a user (healthcare professional users only) and the system that enables the user to visualize and interact with data being generated by the system.

The provided text is a 510(k) summary for the Alio Medical Remote Monitoring System. It details the device's indications for use, components, and a comparison to predicate and reference devices, as well as listing compliance with various safety and performance standards. However, it does not contain the specific acceptance criteria or the study data that proves the device meets those criteria.

The section titled "9. Performance Data" states that "Nonclinical verification and validation test results established that the device meets its design requirements and intended use, that it is as safe, as effective, and performs as well as the predicate devices, and that no new issues of safety and effectiveness were raised." It also mentions "extensive safety and performance testing as shown in the test results provided in this submission."

Despite these statements, the actual acceptance criteria, reported device performance (e.g., accuracy metrics for heart rate or temperature), sample sizes, ground truth establishment, or details of a multi-reader multi-case study are not included in this summary. The summary focuses on regulatory compliance and substantial equivalence argument rather than detailed performance study results against specific criteria.

Therefore, I cannot populate the requested table and answer many of the questions based solely on the provided text. To fulfill the request, one would need access to the full submission documents, specifically the detailed performance study reports.

Based on the provided text, the following information is available (and what is not):

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

• Not provided in the text. The text only generally states that "the device meets its design requirements and intended use." Specific numerical acceptance criteria for measured parameters (skin temperature, auscultation sound, heart rate) and corresponding achieved performance values are absent.

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

• Not provided in the text. The text mentions "extensive safety and performance testing" but does not specify the sample sizes of patients or data points for any performance tests. Data provenance is also not mentioned.

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

• Not provided in the text. No information about expert involvement in establishing ground truth is present.

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

• Not provided in the text. No details on adjudication methods for test sets are mentioned.

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/Not mentioned. The device described is a "remote monitoring system" that collects physiological data for review by HCPs. It is not an AI-powered diagnostic imaging device typically subject to MRMC studies comparing human reader performance with and without AI assistance. The text does not describe any AI component that directly assists in human interpretation or diagnosis.

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

• Partially inferred/Limited information: The system uses "Alio's proprietary algorithms" to process and analyze raw data in the Alio Medical Cloud. While a standalone algorithm performance evaluation would logically be done internally to ensure accuracy of processed data (heart rate, temperature, sound data), the detailed results of such a standalone performance or how its accuracy was quantitatively measured against a gold standard are not provided in this summary. The stated function is to transmit data for HCP review, implying the algorithm's role is data processing rather than a standalone diagnostic output.

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

• Not provided in the text. The method for establishing ground truth for any measurements (e.g., a clinically validated temperature probe for skin temperature, or a gold standard ECG for heart rate) is not detailed.

8. The sample size for the training set:

• Not applicable/Not provided. While the system uses "proprietary algorithms," the document does not explicitly state that these algorithms are machine learning models requiring "training sets" in the typical sense. Even if they are, the training set size is not mentioned.

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

• Not applicable/Not provided. As with the training set itself, the method for establishing ground truth for any potential training data is not mentioned.

In summary, the provided 510(k) summary serves as a high-level overview for regulatory purposes, demonstrating substantial equivalence. It does not delve into the detailed technical performance study results, acceptance criteria, or statistical validations typically found in comprehensive study reports.

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