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The SMDIE Device Interfacing System is indicated for data transfer between standalone physiological monitors that collect noninvasive blood pressure, temperature, and blood oxygenation level through external cuffs and thermometers to external clinical information systems. The SMDIE is also indicated for patient weight transfer between Scale-Tronix 5002 class physician office scales. The SMDIE Device Interfacing System is not intended for continuous vital sign monitoring purposes.
Prescription Use

The SMDIE Device Interfacing System Software is intended for use with standalone physiological monitor and Scale-Tronix 5002 scales to transfer data from the physiological monitor and scale to external devices via Health Level Seven data exchange protocols for vitals results and patient information.
Serial interfaces between physiological monitors and the SMDIE allow patient weight, non-invasive blood pressure, blood oxygenation level, and temperature to be communicated to the Device Connectivity & User Interface Component.

The provided text describes a 510(k) premarket submission for the "SMDIE Device Interfacing System." This system is described as software intended to transfer data from physiological monitors and scales to external devices using Health Level Seven (HL7) protocols. The submission's focus is on establishing substantial equivalence to predicate devices, rather than presenting a standalone study with acceptance criteria and device performance metrics in the typical sense for a diagnostic or therapeutic device.

Therefore, many of the requested elements (like sample sizes for test/training sets, number/qualifications of experts, adjudication methods, MRMC studies, standalone performance metrics, and specific ground truth types) are not explicitly addressed because the regulatory pathway chosen (510(k) for a device interfacing system) primarily relies on demonstrating equivalence through functional and software testing rather than clinical performance trials with patient data.

Acceptance Criteria and Reported Device Performance

The submission states that "Software Testing was performed on the SMDIE Device Interfacing System and was determined to be acceptable." However, it does not explicitly define specific quantitative acceptance criteria or report detailed performance metrics (e.g., accuracy, sensitivity, specificity) in the way a diagnostic device would.

Based on the information provided, the "acceptance criteria" are implied to be the successful execution of software tests demonstrating the device's ability to reliably transfer data as intended, and that these tests found the device to be acceptable, leading to a determination of substantial equivalence.

Implied Acceptance Criteria and Reported Performance:

Study Details (Based on available information in the 510(k) Summary)

Given the nature of this 510(k) submission for an interfacing system, a traditional clinical study with patient cohorts, "test sets," "training sets," and "ground truth" derived from expert consensus or pathology, as one might see for a diagnostic AI, is not described. Instead, the "study" referenced is primarily software testing and a comparison to predicate devices for substantial equivalence.

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

   • The document mentions "Software Testing was performed," but does not specify the sample size of data points, transactions, or devices used in this testing.
   • Data Provenance: Not specified. It's likely simulated or controlled test data used for software validation, rather than patient data from a specific country or retrospective/prospective collection.

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

   • Not applicable/Not specified. For a device interfacing system focused on data transfer, "ground truth" would typically refer to the correctness and integrity of the data transfer process itself. This is usually validated through technical verification and validation (V&V) procedures by software engineers and quality assurance professionals, rather than clinicians establishing "ground truth" on medical findings.

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

   • Not applicable/Not specified. Adjudication methods are typically used in studies involving human interpretation (e.g., radiologists reviewing images) to resolve discrepancies in expert opinions for diagnostic ground truth. This is not relevant to software testing of a data interfacing system.

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 MRMC study was done. This type of study assesses the impact of an AI algorithm on human reader performance, typically in diagnostic tasks. The SMDIE is a data transfer system, not an AI-powered diagnostic tool intended to assist human readers.

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

   • Yes, in a sense. The "Software Testing" can be considered a standalone performance evaluation of the algorithm's ability to transfer data correctly. However, a specific "standalone performance" metric (like sensitivity/specificity for a diagnostic algorithm) is not provided, only a general statement of "acceptability."

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

   • Not explicitly stated in terms of expert consensus, pathology, or outcomes data, as these are typically for diagnostic/prognostic devices. For this data interfacing system, the "ground truth" would refer to the expected correct data values and their successful transfer according to HL7 standards. This would be established by technical specifications and expected system behavior, verified through testing.

7. The sample size for the training set:

   • Not applicable/Not specified. The SMDIE system is not described as an AI/ML device that requires a "training set" in the context of machine learning model development. Its functionality is based on predefined protocols and logic for data transfer.

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

   • Not applicable. As there is no described training set for an AI/ML model, the concept of establishing ground truth for it does not apply here.

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