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The TELEVISIT software allows healthcare professionals to conduct remote medical check-ups through interactive sessions with patients who have limited mobility or live in remote locations. The TELEVISIT software is intended for the purposes of collecting physiological data such as: Non-Invasive Blood Pressure; Pulse Oximetry, Temperature and Breath Sounds (Auscultation). The software provides physicians with a tool to schedule appointments with remote patients; to initiate and manage a medical check-up session, to provide data acquisition of medical device data, and to view session reports.

The intended use of the TELEVISIT software is in hospital/clinical environments with established specialized care providing access to each patient in their home and/or local community health center. TELEVISIT software is a non-invasive medical device, which does not flag any abnormal results from the medical device inputs. The TELEVISIT software does not monitor, assess or diagnose a disease, a disorder, or an abnormal physical state. The intended use for the software is data acquisition and collection only, with no treatment function or danger to the end user. The data obtained from TELEVISIT software can be used only as an aid in the diagnosis and treatment of the patient.

TELEVISIT is consist of the following 3 main components: Televisit Terminal (Patient/Physician), TELEVISIT Medical Device Gateway Box and the TELEVISIT Management Software. FDA Cleared accessories are provided to record physiological data such as: Breath Sounds (Auscultation); Non-Invasive Blood Pressure; Temperature / Thermometry; Pulse Oximetry.

The provided 510(k) summary for the TELEVISIT device does not contain detailed acceptance criteria or a comprehensive study report with specific performance metrics and statistical analysis typically found in modern AI/ML device submissions.

This submission is for a "Physiological Data Collection System" that gathers data from other FDA-cleared medical devices and transmits/stores it. The focus of the performance data in this type of submission is on the functional operation of the system itself, rather than the diagnostic or predictive accuracy of an AI algorithm.

However, based on the information provided, here's an attempt to extract and interpret the requested details:

Acceptance Criteria and Reported Device Performance

Given that this is a data collection and transmission system, the "performance" is primarily حول the accuracy and reliability of data transfer and system functionality. Quantitative accuracy metrics (like sensitivity, specificity, AUC) for diagnosis or prediction are not applicable here as the device explicitly states it "does not monitor, assess or diagnose a disease."

Study Details:

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

   • Sample Size: Not specified. The document only mentions "Performance testing included verification and validation." There is no indication of the number of patient sessions, data points, or clinical scenarios used in this testing.
   • Data Provenance: Not specified. It's likely that the testing was conducted internally by PHD Medical, possibly in a simulated or controlled environment, as opposed to a multi-site clinical trial with geographically diverse data. There is no mention of country of origin for the data or whether it was retrospective or prospective.

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

   • Number of Experts: Not applicable. For a device focused on data collection and transmission validation, "ground truth" would likely refer to the verifiable accuracy of data transfer (e.g., comparing source data values to received data values) rather than expert interpretation of medical images or patient conditions.
   • Qualifications of Experts: Not specified or applicable in the context of this device's function.

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

   • Not applicable. Adjudication methods are typically used for establishing ground truth in diagnostic studies where there might be disagreement in expert interpretation. For functional testing of data transmission, adjudication is not a standard practice.

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

   • No, an MRMC study was not done. This device is a data collection system, not an AI diagnostic tool. It explicitly states it "does not monitor, assess or diagnose a disease" and "does not flag any abnormal results." Therefore, there is no AI component that would assist human readers in interpretation or diagnosis.

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

   • This question is based on the assumption of an AI algorithm. The TELEVISIT is a software system for data collection and transmission, not an AI algorithm performing diagnosis or analysis. Its performance is about the functionality of the system itself.

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

   • The "ground truth" for this device would be the accurate and verifiable transmission of physiological data. For example, if a blood pressure monitor reads 120/80 mmHg, the "ground truth" is that the TELEVISIT system accurately transmits and records "120/80 mmHg" without alteration or loss. There is no mention of expert consensus, pathology, or outcomes data being used since the device is not for diagnosis or treatment.

7. The sample size for the training set:

   • Not applicable. This device is not an AI/ML algorithm that requires a "training set." It is a software system performing specified functions.

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

   • Not applicable, as there is no training set for an AI/ML algorithm.

Summary of Limitations due to Device Type and Submission Age:

It's crucial to understand that this 510(k) submission is from 2006 for what is essentially a telehealth/telemonitoring data transmission and management system, not an AI-powered diagnostic or predictive tool. The regulatory expectations and the type of performance data required for such a device at that time differ significantly from what would be expected for a modern AI/ML device submission. The performance testing was focused on system functionality, data integrity, and compliance with general software validation and risk management standards (like ISO 14971).

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The intended use of NPOSES (Nocturnal Pulse Oximetry Study Expert System) software is to collect, analyze, report and archive oximetry trend data to provide information to a Medical Doctor specializing in sleep medicine, as a supplemental tool to assist in the timely diagnosis of pediatric obstructive sleep apnea (OSA).

NPOSES is intended for use by an MD/Respiratory Specialist through the following process steps (1) recording and transferring data from a pulse oximeter to a computer in order to maintain unique records per patient of pulse oximetry data, (2) analyzing. reviewing and validating patient data and summary statistics according to customized. user-selected parameters, and (3) generating and archiving reports.

NPOSES in itself is not a diagnosis tool. It is a decision management tool which allows medical personnel to upload and view data related to a sleep study and provide output reports as feedback which may be used by an MD to form a diagnosis.

The PHD Medical NPOSES (Nocturnal Pulse Oximetry Study Expert System) application scores data in the patient history, physician comments and test results to automatically produce an analysis report as input to the identification of pediatric obstructive sleep apnea which is presented to Respiratory Specialists and Medical Doctors. The suggested diagnosis is used to assist the Medical Director in the diagnosis of the severity of obstructive sleep apnea. The application enables the efficient processing of patient sleep evaluation studies while allowing the medical staff to concentrate on critical cases.

The provided text describes a 510(k) submission for the NPOSES device, a Nocturnal Pulse Oximetry Study Expert System. However, it does not include detailed information regarding specific acceptance criteria for performance, the study design, or the results of a study to prove the device meets acceptance criteria.

The "Performance Data" section merely states: "Testing was performed to confirm that NPOSES software is capable of meeting all of its intended functional requirements. NPOSES passed all tests." This is a very high-level statement and does not provide the granular details requested.

Therefore, many of the requested fields cannot be filled from the provided text.

Here's a breakdown of what can be inferred and what is missing:

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

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

• Sample size for test set: Not specified.
• Data provenance: Not specified.

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 specified. The device is intended to "automatically produce an analysis report as input to the identification of pediatric obstructive sleep apnea which is presented to Respiratory Specialists and Medical Doctors." This suggests the output is reviewed by experts, but the process of establishing ground truth for a test set is not described.

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

• Not specified.

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 is a "management tool" and a "supplemental tool to assist in the timely diagnosis," not an AI diagnostic tool that human readers would directly interact with to improve performance. The description focuses on its function in processing and organizing data.

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

• The device is explicitly stated as "not a diagnosis tool. It is a management tool which allows medical personal to input and view data relating to a study and give feedback which may be used by an MD to form a diagnosis." This implies it's not a standalone diagnostic algorithm, but rather a tool to generate output for a medical doctor specializing in sleep medicine. The "Performance Data" section vaguely states "NPOSES software is capable of meeting all of its intended functional requirements" and "NPOSES passed all tests," which likely refers to its functional capabilities (data processing, reporting) rather than diagnostic accuracy as a standalone algorithm.

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

• Not specified.

8. The sample size for the training set

• Not specified. The document does not describe a machine learning model that would typically have a "training set" in the modern sense. It refers to an "Expert System" but does not detail how this system was developed or "trained."

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

• Not applicable/Not specified, as no training set or its ground truth establishment is described.

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