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The SC6000/SC6000P enhanced with ST Segment Analysis is intended to be used in the environment where patient care is provided by Healthcare Professionals, trained in the use of the device, i.e. physicians, nurses, and technicians, who will determine when use of ST Segment Analysis is indicated, based upon their professional assessment of the patient's medical condition.

ST Segment Analysis is intended for use in the adult population.

The SC6000/SC6000P is not for home use.

The SC 6000 / SC 6000P Bedside Monitoring Series Enhanced with ST Segment Analysis is an updated software version of the SC 6000 / SC 6000P Bedside Monitoring Series. The modification adds software to determine the ST Segment of the ECG signal and to compute the deviation of this ST Segment from the isoelectric point (baseline). This is the same algorithm that is used in the Siemens 1481 (T) Digital Telemetry System with ST Segment Analysis Option (K951371). The hardware of the SC 6000 / SC 6000P (510(k) K944350) is unchanged.

The ST Segment Analysis is not active when the SC 6000/SC 6000P is in the neonatal mode.

The modified software (version VC0) is not compatible with all previously sold versions of the monitor. Therefore, a software upgrade will be offered to the owners of units with previous software revisions. No hardware changes are required for the upgrade.

The provided text describes the Siemens SC6000/SC6000P Bedside Monitoring Series enhanced with ST Segment Analysis. The submission is a 510(k) for a modified device, meaning it seeks to prove substantial equivalence to a predicate device. Therefore, the "study" described is primarily focused on demonstrating this equivalence rather than a de novo clinical trial.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the comparison to the predicate device, especially regarding ST Segment deviation measurement accuracy.

Note: The document explicitly states "The ST Segment Analysis of the SC6000/SC6000P series patient monitors is equivalent to the ST Segment Analysis of the predicate device." This statement, along with the "Same" annotations in the table, indicates that the acceptance criteria for the ST Segment analysis are met by matching the performance of the predicate device. The changes in "Leads processed" and "Update interval" are noted but not presented as deviations from an acceptance criterion for ST segment accuracy itself, but rather as functional differences that were presumably deemed acceptable.

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

The document does not specify a sample size for a test set or data provenance (e.g., country of origin, retrospective/prospective). As this is a 510(k) submission primarily relying on software equivalence to a predicate device, it appears to leverage existing data or validation processes from the predicate device rather than conducting a new, independent clinical study with a defined test set.

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

The document does not provide information on the number or qualifications of experts used to establish ground truth for a test set. This type of detail is typically associated with de novo clinical studies involving human interpretation, which is not the primary focus of this 510(k) submission for a software modification to a monitoring device.

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

The document does not mention any adjudication method for a test set.

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 was reported. This device is an ST Segment monitor, and the focus is on the algorithm's performance in measuring ST deviation, not on human reader improvement with AI assistance.

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

Yes, a standalone assessment of the algorithm's performance is implicit in the comparison of the ST Segment Analysis algorithm to the predicate device. The document explicitly states: "The ST Segment Analysis of the SC6000/SC6000P series patient monitors is equivalent to the ST Segment Analysis of the predicate device." The comparison table directly addresses the accuracy ("± 1mm / ± 0.1mV") of the algorithm itself. This suggests that the algorithm's output (ST segment deviation values) is being evaluated directly against predefined accuracy standards inherent to the predicate device's performance.

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

The explicit type of ground truth used is not specified. However, given the context of a 510(k) submission for a monitoring device's ST segment analysis, the implicit ground truth would likely be a combination of:

• Physiological standards: The accepted medical definition and measurement of ST segment deviation.
• Comparison to predicate device: The performance of the predicate device (Siemens 1481T Digital Telemetry) itself serves as the benchmark against which the modified algorithm's "truth" or accuracy is compared.
• Compliance with industry standards: The device complies with "Performance Measurements for Algorithms to Detect Transient Ischemic ST Segment Changes", IEEE 1992 and "ST Segment Monitor Preliminary Guidance", US Department of Health and Human Services, July 1994, which would imply certain performance expectations that serve as ground truth for algorithm design and testing.

8. The sample size for the training set

The document does not specify a sample size for the training set.

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

The document does not provide information on how the ground truth for a training set was established. Given the age of the submission (1997) and the nature of the device (ECG monitoring with ST segment analysis), it's highly probable that traditional signal processing and rule-based algorithms were in use, rather than machine learning models requiring explicit training sets with labeled ground truth in the modern sense. The "ground truth" for developing such algorithms would typically be based on established physiological principles, expert knowledge of ECG interpretation, and potentially large databases of ECG recordings analyzed by cardiologists.

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Olympus Communications Network: To act as a communications network for the SC 9000/9015 (K946306) and SC 6000/6000P (K944350) Patient Monitoring Systems, the SC 3000 Workstation, R 50 (K944350) and R 100 Recorders, and laser printers.
SC 3000 Workstation: To act as a central monitoring device for the SC 9000/9015 (K946306) and SC 6000/6000P (K944350) Patient Monitoring Systems through its connection to the Siemens Olympus Communications Network.
SC 3000 Remote Display: To act as a Slave Display for the SC 3000 Workstation.

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This document describes the safety and effectiveness information for the Siemens Olympus Communications Network, SC 3000 Workstation, and SC 3000 Remote Display. However, the provided text does not contain acceptance criteria for device performance or a study demonstrating that the device meets such criteria.

The document primarily focuses on:

• General Information: Device classification, common names, trade names, intended uses, and establishment details.
• Safety and Effectiveness Information II: Compliance with electrical standards (IEC 601-1, UL 544, UL 433, EN 60950, UL 1950, CSA 22.2 No. 950, CE Mark), operational instructions, and adherence to a product development process for design and testing.
• Substantial Equivalence III: Comparison to previously cleared predicate devices from Siemens (SIRENET Communications Network, SIRECUST 120/121C Central Station, SIRECUST 120/121S Slave Display) and Marquette (Unity Network, CentralScope™ 12 Central Station, Remote Color Display).

Therefore, I cannot provide the requested information regarding acceptance criteria and performance study details from the given text.

To answer your request, the input document would need to include sections detailing:

• Specific performance metrics (e.g., accuracy, reliability, latency for communication networks; display resolution, refresh rates for workstations/displays).
• Quantitative acceptance thresholds for these metrics.
• Protocols and results of a study (e.g., validation testing, clinical trials) designed to demonstrate that the device meets these thresholds.

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