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The Remote Presence System is a clinical communications tool that provides a means of transmitting, receiving, and storing real-time audio and video, and patient data. The Remote Presence System may also be used in conjunction with 510(k)-cleared devices that transmit patient biometric data including vital signs information. The Remote Presence System transmits and receives information over a high speed connection between patients, health professionals and critical transport teams. The Remote Presence System can be used in communications for active patient monitoring in . high acuity clinical environments where immediate clinical action may be required, e.g., pre-, perioperative and post-surgical, cardiovascular, neurological, pre-natal, psychological and critical care assessments and examinations. Clinical judgment and experience are required to review and interpret the information transmitted.

The Remote Presence System is a telecommunications platform that enables real-time videoconferencing and clinical communications, and provides a means for transmitting, receiving, and storing real-time audio and video, and patient data. The Remote Presence System consists of a Control Station ("CS"), (i.e., desktop or laptop computer) and an end point, which may be controlled by an input device (i.e., mouse or joystick) that the operator uses to control the movement of the end point in the remote location (e.g., RP-70), or manually located by the user (e.g., RP-Lite®, KSEA VisitOR1 Cart, RP-Vantage®, or RP-Xpress™), or used in a restricted clinical environment, such as an operating room, where it is boom mounted (e.g., BoomBot (aka RP-B and KSEA VisitOR1)). The end point and CS are each equipped with various combinations of cameras, displays, microphones and speakers, depending upon the specific device, which facilitate two-wav audio-video communication. Optional accessories include Class II devices, including an integrated electronic stethoscope, which are used for the same purpose for which they received 510(k) clearance. Communication between the CS and the end-point is established wia broadband Internet and an 802.11 wireless network or a broadband cellular connection.

The provided text does not describe the acceptance criteria or the study that proves the device meets the acceptance criteria. Instead, it contains a 510(k) summary for a general-purpose telemedicine system (Remote Presence System- RP-7i® etc.).

Here's what can be extracted from the document, and what cannot:

Information that can be extracted from the provided text:

• Device Description and Intended Use: The Remote Presence System is a telecommunications platform for real-time videoconferencing and clinical communications, transmitting, receiving, and storing real-time audio and video, and patient data. It can also be used in conjunction with 510(k)-cleared devices that transmit patient biometric data. It is intended for use in high acuity clinical environments for active patient monitoring, assessments, and examinations.
• Predicate Device: The legally marketed predicate device is the InTouch Remote Presence Robotic System, Model RP-7 (K073710).
• Comparison to Predicate Device: The new device expands on the predicate by introducing new cart-based and hand-held models, providing improved system flexibility and adapting to additional clinical environments (e.g., operating rooms, emergency departments, intensive care units, patient transport). It also broadens the connectivity options (wired or wireless broadband, USB, Bluetooth®), communication protocols (Session Initiation Protocol), and types of devices that can be interfaced.
• Validation Testing: The document states, "The effectiveness of these improvements was demonstrated by the validation testing performed on the system." and "The performance data discussed in this 510(k) application demonstrate that the Remote Presence System is as safe and effective, and performs as well as or better than the predicate device."

Information that cannot be extracted from the provided text (and why):

The document is a 510(k) summary for a telemedicine system. It describes the device's function, its predicate, and differences. It does not present clinical study data or acceptance criteria related to a specific AI/ML medical device's performance for diagnosis or prognostication. Therefore, the specific details requested regarding acceptance criteria, study design, sample sizes for test/training sets, expert qualifications, ground truth establishment, MRMC studies, or standalone performance are not present in this document.

The "validation testing" mentioned is general and typically refers to engineering verification and validation testing for functionality, safety, and effectiveness in a broader sense for a communication system, not clinical performance metrics for an AI-powered diagnostic tool.

To answer your request, if this were an AI/ML device submission, here's what would typically be expected, and why the provided text doesn't contain it:

1. A table of acceptance criteria and the reported device performance: This would list specific metrics (e.g., sensitivity, specificity, AUC) and the agreed-upon thresholds (acceptance criteria) along with the actual performance achieved by the device in testing. Not present.
2. Sample sized used for the test set and the data provenance: Details on the number of cases/samples in the test set, and whether the data was retrospective/prospective, and its geographical origin. Not present.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Information about the raters/readers who established the truth label for the test data. Not present.
4. Adjudication method for the test set: How disagreements among experts were resolved (e.g., 2 majority vote, 3+1 with a tie-breaker). Not present.
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: Details of studies comparing human performance with and without AI, including an effect size. Not present.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Metrics for the algorithm's performance without any human intervention. Not present.
7. The type of ground truth used: Whether the truth was derived from expert consensus, pathology, long-term outcomes, etc. Not present.
8. The sample size for the training set: The number of samples used to train the AI model. Not present.
9. How the ground truth for the training set was established: The methodology for creating the reference labels for the training data. Not present.

In summary, the provided document describes a communication device, not an AI/ML diagnostic or predictive device, and therefore does not contain the specific performance study details you are asking for.

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The purpose of the TCx-I Remote Care Management System is to collect and transmit medical information such as weight, blood pressure and pulse rate, and blood glucose from the patients on completion of their testing and transmit these results to their healthcare provider at another facility. The system supports videoconferencing, multimedia education and messages. This system is installed by or with support from trained professionals. This device is not intended to provide time sensitive data or alarms. This system may not be used as a substitute for direct medical intervention or emergency care. Interpretation of the information collected and transmitted requires clinical judgment by an experienced medical professional.

TCx-I Remote Care Management system collects and transmits measurement information such as weight, blood pressure and pulse rate, and Blood Glucose data from the patients on completion of their testing and transmit these results to their healthcare provider at another facility.

The BL Healthcare Inc. TCx-I Remote Care Management System (RCMS) is a telemedicine system designed to collect and transmit medical information (weight, blood pressure, pulse rate, blood glucose) from patients to healthcare providers. The provided document is a 510(k) summary and submission acceptance letter, which details its regulatory approval based on demonstrating substantial equivalence to predicate devices, rather than a full clinical study with specific acceptance criteria and performance metrics for an AI algorithm.

Therefore, much of the requested information regarding detailed acceptance criteria, specific performance metrics, sample sizes for test/training sets, ground truth establishment methods, expert qualifications, adjudication methods, MRMC studies, and standalone performance for an AI-powered device is not applicable or not provided in this document. The device described here is a data collection and transmission system, not an AI diagnostic or assistive tool in the way current AI medical devices are typically evaluated.

Here's what can be extracted from the document based on your questions:

1. Table of acceptance criteria and the reported device performance

Based on the document, there are no specific numeric acceptance criteria or reported device performance metrics in terms of accuracy, sensitivity, specificity, etc., as typically associated with AI performance. The "acceptance criteria" here relate to regulatory compliance and the system's ability to correctly transmit data.

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

• Sample size for test set: Not applicable/Not provided. The evaluation was based on non-clinical software validation and risk-based verification testing, not a clinical "test set" of patient data in the context of an AI algorithm.
• Data provenance: Not applicable/Not provided in the context of an AI algorithm's test data. The testing focused on the system's functional integrity.

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

Not applicable. Ground truth, in the sense of expert-labeled data for algorithm evaluation, was not established for this type of system validation. The "ground truth" implicitly was whether the system correctly collected and transmitted the input data.

4. Adjudication method for the test set

Not applicable. No adjudication method for a test set was mentioned, as it was not a study evaluating human interpretation or AI performance on medical images/signals.

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 done. This device is a data transmission system, not an AI-assisted diagnostic tool.

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

No standalone performance evaluation (in the context of an AI algorithm's diagnostic or predictive capabilities) was done. The focus was on the reliability of data transmission.

7. The type of ground truth used

The "ground truth" for this system's validation would be the accurate transmission and display of the measured physiological parameters (weight, BP, pulse, blood glucose). This is established through engineering and software testing protocols, verifying that what is measured at the patient end is accurately reflected at the healthcare provider end. It does not involve expert consensus, pathology, or outcomes data in the typical sense of AI evaluation.

8. The sample size for the training set

Not applicable. This device is not an AI algorithm that undergoes "training" on a dataset. Its primary function is data acquisition and transmission.

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

Not applicable, as there is no training set for this type of device.

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