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ESSENZ Patient Monitor is a modularly structured program package that is exclusively used with LivaNova Heart-Lung Machines. The system allows detailed recording of perfusion data during cardiopulmonary bypass as well as the processing and evaluation of this data. The data may be recorded automatically or entered manually.

The ESSENZ Patient Monitor is used to collect and view data generated during cardiopulmonary bypass surgery. It interfaces with LivaNova Heart-Lung machines to capture this data.

The ESSENZ Patient Monitor includes the following main components:

• Software: The data-recording software installed on the Perfusion System Monitor hardware; known as "ESSENZ Patient Monitor". It is used to save and process the collected data.
• Hardware: The Perfusion System Monitor. It is used to display and transfer the collected data.
• Hardware: A mast holder to mount the Perfusion System Monitor on a vertical mast of the respective LivaNova HLM.

This document describes the ESSENZ Patient Monitor, a device used with LivaNova Heart-Lung Machines to record, process, and evaluate perfusion data during cardiopulmonary bypass. The information provided is for a 510(k) premarket notification (K212003).

Here's an analysis of the acceptance criteria and supporting studies based on the provided text:

1. Table of Acceptance Criteria and the Reported Device Performance:

The document does not explicitly present a table of acceptance criteria with specific numerical targets. Instead, it states that the device was evaluated against recognized standards and through various non-clinical tests to demonstrate substantial equivalence to a predicate device (Sorin CONNECT, K170460). The general acceptance criterion is that the modified device is "as safe and effective" as the predicate.

For the purpose of this request, we can infer the categories of acceptance criteria from the performed tests and their general outcome:

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

• Test Set Sample Size: The document does not specify a quantitative "test set sample size" in terms of patient data or specific number of tests. The testing described (Electrical Safety, EMC, Performance, Software V&V, Human Factors, Shipping) is related to hardware and software requirements, not clinical data sets in the typical sense of a diagnostic or therapeutic algorithm.
• Data Provenance: The tests conducted are non-clinical hardware and software verification and validation tests performed by the manufacturer, LivaNova Deutschland GmbH. The data provenance is internal to the manufacturer's testing processes. There is no mention of country of origin for a clinical data set because no clinical testing was performed or relied upon. The report states, "No animal testing was submitted to support the substantial equivalence of the modified device to the cleared Sorin CONNECT," and "No clinical testing was conducted in support of ESSENZ Patient Monitor."

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

This information is not applicable to the submitted document. The "ground truth" in this context refers to the compliance with engineering and regulatory standards established through non-clinical testing. These tests are evaluated against predefined specifications and industry standards, not against an expert-established clinical ground truth for a diagnostic outcome.

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

This information is not applicable. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies for interpretation of images or clinical outcomes, which were not part of this 510(k) submission for the given device. The testing was against engineering specifications and recognized standards.

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 conducted or is relevant to this device's submission. The ESSENZ Patient Monitor is a data management system for perfusion data during cardiopulmonary bypass; it is not an AI-assisted diagnostic tool that would involve human readers or image interpretation.

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

The concept of "standalone performance" for an algorithm often applies to diagnostic or predictive AI. In this case, the "standalone performance" can be considered as the device's ability to accurately record, process, and evaluate perfusion data according to its specifications and in compliance with the relevant standards (IEC 60601-1, IEC 60601-1-2, IEC 62304, etc.) without human intervention in the data handling itself. The software verification and validation, along with performance testing, would cover this aspect. However, the device is explicitly used "with LivaNova Heart-Lung Machines" and the data can be "recorded automatically or entered manually," implying it is part of a larger system where human interaction remains.

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

The "ground truth" for the non-clinical testing of the ESSENZ Patient Monitor is compliance with established engineering and safety standards (e.g., IEC 60601 series, IEC 62304, ISO 14971). The ground truth for its functional performance (data recording, processing, evaluation) would be derived from comparisons against expected computational and display outputs based on defined inputs, ensuring accuracy and reliability as specified in the device's design.

8. The sample size for the training set:

This information is not applicable. The ESSENZ Patient Monitor is a data management system, not a machine learning or AI model that requires a "training set." The software is re-engineered from a legacy version and verified/validated against specifications.

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

This information is not applicable, as there is no "training set" for this device.

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The IntelliVue GuardianSoftware is intended for use by healthcare providers whenever there is a need for generation of a patient record.

The IntelliVue GuardianSoftware is indicated for use in the collection, storage and management of data from Philips specified measurements, Philips Patient Monitors and qualified 3rd party measurements that are connected through networks.

The IntelliVue GuardianSoftware is a stand-alone 'Clinical Information Management System (CIMS)' software application with client-server architecture and designed to be used in professional healthcare facilities (i.e. hospitals, nursing homes) and is intended to be installed on a 'customer-supplied', compatible off-the-shelf (OTS) information technology (IT) equipment.

The IntelliVue GuardianSoftware is a documentation, charting, and decision-support software that is configurable by the hospital to suit the needs of individual clinical units. The device collects data/vital signs from the following Philips compatible patient monitor/measuring devices.

Using the collected data, the device provides trending, review, reporting and notification. The 'Guardian Early Warning Score (EWS)' application is integrated into the IntelliVue GuardianSoftware to provide the healthcare professional/provider basic assessment and the ability to recognize early signs of deterioration in patients.

The IntelliVue GuardianSoftware is not an alarming device and displays alarms from patient monitors as supplemental information only.

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not contain specific quantitative acceptance criteria or a side-by-side performance comparison of the new device versus these criteria. Instead, the document focuses on demonstrating substantial equivalence to a predicate device. The acceptance is implied by the successful software verification and validation, as well as human factors testing, which collectively show the device "met all safety and reliability requirements and performance claims."

The closest we get to "performance" in the context of the comparison is in the "Substantial Equivalence Determination" column, which consistently states "IDENTICAL" or provides explanations for differences that do not affect substantial equivalence.

Key areas assessed for equivalence (and thus, implicit "performance" against the predicate):

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

The document does not specify a "test set" in terms of patient data. The evaluation relies on:

• Software Verification and Validation Testing: This is typically performed on software builds and simulated environments, not directly on patient data.
• Human Factors and Usability Testing: This involves human users interacting with the device. The sample size for this specific testing is not mentioned.
• Data Provenance: Not applicable in the context of patient data testing, as no patient data was used for performance evaluation of the software itself.

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. Since the evaluation focused on software verification/validation and human factors/usability, and not on clinical performance with patient data requiring expert ground truth, this type of detail is absent.

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

No adjudication method is mentioned, as there was no test set requiring expert adjudication for clinical ground truth.

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 conducted or mentioned. The device is a "Clinical Information Management System" and not an AI-assisted diagnostic tool for human readers. It collects, stores, and manages data, and provides decision support (e.g., Early Warning Score), but it doesn't appear to directly assist human readers in interpreting medical images or other complex data where "improvement" with AI would be measured.

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

The device is inherently a "Clinical Information Management System (CIMS) software application" that operates in a standalone manner as software. However, its function as data collection, storage, management, and providing decision support (like EWS) means it is intended for use by healthcare providers and is integrated into clinical workflows. The performance testing focuses on its software functionality, reliability, and human factors, rather than a diagnostic algorithm's standalone performance. The "Guardian Early Warning Score (EWS)" is an algorithm, and its performance would be assessed for accuracy in calculating scores, but the document doesn't provide details on its standalone performance metrics.

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

For the software verification and validation, the "ground truth" would be the expected functional behavior and output of the software as defined by its requirements and specifications. For human factors testing, the ground truth would be the expected safe and effective interaction of users with the device. There isn't an external clinical ground truth (like pathology or outcomes) applied to the software's performance itself in this submission.

8. The sample size for the training set:

Not applicable. This is not an AI/machine learning model where a specific training set (of patient data) would be used. The software is developed based on engineering principles and regulatory requirements.

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

Not applicable, as there is no training set for an AI/machine learning model.

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The Essence 55SP Large Monitor System (Essence 55SP) is intended to be used by health care professionals to integrate the video output from various commercially-available instruments commonly used in a medical procedure laboratory into a single video display.

The Essence 55SP Large Monitor System is a large color LCD monitor for viewing images derived from several video sources on a single display and to rearrange these images to the situational requirements of the user. The system consists of a 55" TFT color LCD Monitor, and optionally a video wall controller, optical graphic extension module, a common PC, and cables.

The provided document is a 510(k) Summary for the Essence 55SP Large Monitor System. It describes the device and claims substantial equivalence to a predicate device. However, it does not contain the kind of detailed study information (sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth details for training/test sets) that would typically be found for an AI/ML medical device submission.

This document describes a medical monitor, not an AI/ML device. Therefore, the specific criteria requested, which are typically for assessing the performance of AI/ML algorithms against a clinical ground truth, are not applicable or present in this submission. The tests mentioned are performance tests for the display itself, ensuring it meets technical specifications and is equivalent to the predicate device.

Here's an attempt to answer the questions based on the available information, noting where the requested information is not present due to the nature of the device:

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

The document compares the proposed device (Essence 55SP) with a predicate device (Essence 55S) based on technical specifications. The acceptance criteria essentially appear to be demonstrating "substantial equivalence" to the predicate device, meaning its technical performance is either the same or, if different, the difference does not raise new questions of safety or effectiveness.

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

Not applicable. This is a medical monitor, and the "tests" performed are bench tests and display validation tests to confirm its technical specifications and equivalence to a predicate device. It does not process or analyze medical data in a way that would involve a "test set" of patient data.

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 applicable. Ground truth, in the context of AI/ML, refers to clinically verified labels or diagnoses. This device is a display system, not a diagnostic AI. Its performance is assessed against technical specifications, not clinical judgment.

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

Not applicable. There is no test set in the sense of clinical cases requiring adjudication.

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. This device is a display monitor, not an AI system. It does not provide AI assistance for human readers, therefore, an MRMC study comparing human readers with and without AI would not be relevant.

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

Not applicable. This is a monitor, not an algorithm.

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

Not applicable. The performance is based on meeting technical specifications as specified by standards and comparison to a predicate device's measured performance. There is no "ground truth" related to medical disease states as one would find in an AI/ML submission.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a training set.

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

Not applicable. There is no training set or medical ground truth for this device.

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The Vios CSM/CS Software is indicated for use by healthcare professionals for the purpose of centralized monitoring of patient data within a healthcare facility. The Vios CSM/CS SW receives, stores, manages, and displays patient physiological and waveform data and alarms generated by Vios proprietary patient vitals monitoring software.

The Vios CS SW enables the Vios Monitoring System to be used in networked mode within a healthcare IT network and runs on commercial IT equipment. The Vios CS SW operates as a communications hub that can pass the data generated by Vios proprietary vitals monitoring software to one or more remote viewing software applications, without modifying the data. The Vios CSM SW is the remote viewing software of the Model 2050 system. It allows up to 16 devices to be displayed on one screen and runs on a commercial IT device that satisfies defined Vios-defined technical specifications.

The provided text does not contain information about the acceptance criteria and study proving device performance for Vios Central Station Monitor/Central Server Software Model 2050. The document is an FDA 510(k) clearance letter and summary, which confirms substantial equivalence to a predicate device but does not detail specific performance studies with acceptance criteria, sample sizes, ground truth establishment, or expert involvement.

Therefore, I cannot fulfill your request for the detailed performance information based on the provided input.

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The IntelliVue GuardianSoftware is intended for use by healthcare providers whenever there is a need for generation of a patient record.

The IntelliVue GuardianSoftware is indicated for use in the collection, storage and management of data from Philips specified measurements and Philips Patient Monitors that are connected through networks.

The IntelliVue GuardianSoftware (866009) is a Clinical Information Management System. It collects and manages vital signs data acquired from the Philips specified measurements and Philips Patient Monitors. The IntelliVue GuardianSoftware provides review, reporting, clinical documentation, remote viewing, operating, interfacing, storage, printing and predictive trend analytics, meaning trending, notification, calculations and clinical advisories including EWS deterioration status. The IntelliVue GuardianSoftware is a software only product. It is intended to be installed on customer supplied compatible off-the shelf information technology equipment that meet the technical requirements as specified by Philips.

The provided text primarily details an FDA 510(k) submission for the Philips IntelliVue GuardianSoftware and an administrative change letter. It does not contain an elaborative study description with acceptance criteria and its proof for device performance in the requested format.

However, based on the general information provided in the 510(k) summary, I can extract and infer some information, but it will not be a complete answer to all your specific questions:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or detailed device performance metrics in a table format. It broadly states:

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

• Sample Size for Test Set: Not specified. The document only mentions "software testing on integration level (functional testing and regression testing) and software testing on system level (hazard analysis testing and dedicated software performance testing)." It does not indicate the number of patient records or data points used in these tests.
• Data Provenance: Not specified. It does not mention the country of origin of the data or whether the data was retrospective or prospective.

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

Not specified. The document does not describe the establishment of a "ground truth" for the test set using human experts. The testing appears to be functional and performance-based against specifications, not clinical outcomes evaluated by experts.

4. Adjudication method for the test set

Not specified. Since no expert ground truth establishment is mentioned, there's no mention of an adjudication method.

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. The document describes the Philips IntelliVue GuardianSoftware as a Clinical Information Management System for collecting, storing, and managing vital signs data, and providing review, reporting, clinical documentation, remote viewing, operating, interfacing, storage, printing, and predictive trend analytics. It is not an AI-assisted diagnostic device, and therefore, an MRMC comparative effectiveness study comparing human readers with and without AI assistance is not applicable to this device and was not mentioned.

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

Yes, implicitly. The testing described is for the software itself, independently verifying its performance, functionality, and reliability according to specifications and safety standards. The device is a "software only product" and its testing would inherently be standalone.

7. The type of ground truth used

For the software verification and validation, the "ground truth" would be the pre-defined specifications, requirements, and safety standards (e.g., ANSI/AAMI/IEC 62304:2006). The software was tested to ensure it met these established criteria. It does not involve medical ground truth like pathology, expert consensus on images, or outcomes data.

8. The sample size for the training set

Not applicable/Not specified. This device is described as a "Clinical Information Management System" that handles data collection, storage, and management, including predictive trend analytics and clinical advisories. While "predictive trend analytics" could potentially involve machine learning, the document does not elaborate on the specific algorithms used or if a training set, characteristic of machine learning models, was employed or is relevant to its substantial equivalence claim. The focus here is on the functionality and safety of the data management software itself.

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

Not applicable/Not specified, for the same reasons as #8. If predictive trend analytics involve trained models (which is not explicitly stated but hinted at), the method for establishing ground truth for such a training set is not described. The document emphasizes testing against a predicate device's cleared specifications and general software safety standards.

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The Barco Nexxis OR system is designed to allow transfer, selection and distribution of A/V signals and medical images from various commercially available instruments that are commonly used in a medical procedure laboratory or operating room.

The Barco Nexxis OR system is also designed for transfer, selection and distribution of human interface signals and control signals of non-medical room equipment,

The Barco Nexxis OR system allows control and selection of these signals from a central point.

The Barco Nexxis OR system is not intended to be used for remote or robotically assisted surgery,

The Barco Nexxis OR system is not intended to be used in the vicinity of MRI or other devices that use strong magnetic fields.

The Nexxis OR system is a solution for video distribution in operating rooms and medical procedure laboratories. The Nexxis OR can consist of any possible combination of the Nexxis encoders/decoders that transfer video signals from various available devices to one or more output devices (displays). Additionally the Nexxis components can transfer audio signals and USB signals. The USB communication is intended only for keyboard and mouse control and control signals for non-medical room equipment. The selection of sources and output devices is done by control software (NMS API). The Nexxis OR system can also contain, optionally, the MNC-180 compositor device, a MDSC-8258 MNA display and/or the touch user interface MUIP-2112. The Nexxis OR system may now consist of any combination of the following components: a) Nexxis OR components (Medical network adaptors) b) Nexxis Compositor with NCS composition software (MNC-180) c) Nexxis Management Suite (NMS). d) A 58-inch large screen LCD monitor: Barco MDSC-8258 MNA.(optional) e) touch user interface MUIP-2112 (optional) f) Cables g) Documentation

The provided document is a 510(k) premarket notification for the Barco Nexxis OR system, which is a video distribution solution for operating rooms. The document focuses on demonstrating substantial equivalence to a predicate device rather than detailing specific acceptance criteria and a study to prove meeting those criteria in the context of diagnostic performance.

Therefore, many of the requested elements for a diagnostic device's acceptance criteria and study cannot be extracted from this document.

Here's what can be gathered and what cannot:

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

The document does not provide a table of acceptance criteria in the sense of specific performance metrics (e.g., sensitivity, specificity, accuracy) for a diagnostic output. Instead, the "acceptance criteria" can be inferred as demonstration of similar characteristics in terms of safety and effectiveness compared to the predicate device.

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

This information is not provided in the document. The testing described ("Display bench tests," "Display validation tests," "Nexxis OR qualification tests," "System tests") appears to be engineering and system validation rather than clinical performance testing with a "test set" of medical images or patient data.

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 applicable/not provided. The device is a video distribution system, not a diagnostic algorithm that relies on expert interpretation of output.

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

This information is not applicable/not provided.

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

This information is not applicable/not provided. This is not a diagnostic device with an AI component for image interpretation.

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

This information is not applicable/not provided. This is a system for distributing video signals, not a standalone diagnostic algorithm.

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

This information is not applicable/not provided. Ground truth in a clinical sense would not be relevant for the device itself, though the medical images it transmits would have their own ground truth established outside the scope of this device.

8. The sample size for the training set

This information is not applicable/not provided. This is not an AI/ML device that requires a training set.

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

This information is not applicable/not provided.

Summary based on information provided in the document:

The Barco Nexxis OR system is a medical video distribution system. The study to demonstrate its performance and meet acceptance criteria primarily consisted of bench tests and system qualification tests. These tests aimed to show that the device has similar characteristics to its predicate device (Nexxis OR cleared under K170537) and did not reveal new issues of safety and effectiveness. The document explicitly states that "Animal or clinical testing have not been performed," indicating that the "study" was not a clinical trial in the traditional sense, but rather engineering and functional validation.

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The Essence 55S Large Monitor System (Essence 55S) is intended to be used by health care professionals to integrate the video output from various commercially-available instruments commonly used in a medical procedure laboratory into a single video display.

The Essence 55S Large Monitor System is a large color LCD monitor for viewing images derived from several video sources on a single display and to rearrange these images to the situational requirements of the user. Thanks to the advantages of the large widescreen monitor, the data can be collected from the video sources using the optional video wall controller AVS-540 and fed into the large display via the DVI lines and / or DP. The optical graphic extension module M1-201SA-TR can be used to convert / transmit the video signal, and the common PC can be taken as a video device.

The provided 510(k) summary (K172969) describes a medical monitor, the Essence 55S Large Monitor System. The acceptance criteria and the study that proves the device meets them are based on comparisons to a predicate device and bench testing.

1. Table of Acceptance Criteria and Reported Device Performance

The device is a medical monitor, and its "performance" is primarily defined by its technical specifications and its ability to integrate video output for medical professionals. The acceptance criteria are implicitly met by demonstrating substantial equivalence to a predicate device and successful bench and system tests.

Comparison Table for Key Technical Specifications (Excerpt from Table 2 in document):

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

This is a medical display device, not an AI or diagnostic imaging device. Therefore, the concept of a "test set" in the context of patient data or clinical images is not applicable. The "tests" performed are engineering and performance validation tests.

• Sample Size: Not applicable in the context of patient data. The "sample" would be the device itself (Essence 55S Large Monitor System).
• Data Provenance: Not applicable. The "data" are technical specifications and test results generated from the device in a laboratory setting.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. Ground truth, in the context of expert consensus on medical images, is not relevant for a medical display device. The "ground truth" for this device's performance is its adherence to technical specifications and safety standards, as verified through engineering tests.

4. Adjudication Method for the Test Set

Not applicable. Adjudication methods (like 2+1 or 3+1) are used for resolving disagreements among human readers in interpreting medical images. This device is a display, not an interpretation tool.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study was not done. Such studies are typically performed for diagnostic devices or AI algorithms that assist in interpretation, to measure the impact of the device on human reader performance. This device is a monitor, not an interpretative aid.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, but not in the typical sense of an "algorithm-only" performance for a diagnostic task. The "standalone" performance here refers to the device's technical specifications and functionality as a display system, independent of a human's diagnostic interpretation using the displayed images. The "Display bench tests," "Display validation tests," and "System tests" are essentially standalone performance evaluations of the monitor.

7. The Type of Ground Truth Used

The "ground truth" for this device is based on:

• Technical Specifications: Adherence to defined physical and electrical parameters (e.g., resolution, brightness, contrast, response time).
• Regulatory Standards: Compliance with relevant medical device safety and performance standards (e.g., EN60601-1, ANSI/AAMI ES60601-1, FCC-B, RoHS).
• Predicate Device Equivalence: The performance and characteristics of the legally marketed predicate device (RadiForce Large Monitor System) serve as a benchmark for substantial equivalence.

8. The Sample Size for the Training Set

Not applicable. This device is a hardware display system, not a machine learning model that requires a "training set" of data.

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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The device is a modularly structured program package that is exclusively used with Sorin/Stockert heart lung machines. The system allows detailed recording of perfusion data during cardiopulmonary bypass as well as the processing and evaluation of this data. The data may be recorded automatically or entered manually.

The Sorin CONNECT is used to collect and view data generated during cardiopulmonary bypass surgery. It interfaces with Sorin Heart-Lung machines to capture this data.

The Sorin CONNECT includes the following main components:

• The Datapad display monitor. It's used to display the collected data.
• The data recording software installed on the Datapad; known as "CONNECT Recorder". . lt's used to save the collected data.
• The PC software for reviewing data; known as "CONNECT Manager". It's used to view ● the recorded data and manually enter additional data including clinician's comments.
• . An RFID card reader used to transfer the data from the Heartlink card to the Datapad.
• . A pole mounting system (holder) to attach the Datapad to an IV pole.

The provided text is a 510(k) summary for the Sorin CONNECT, a data management system for heart-lung machines. It focuses on demonstrating substantial equivalence to a predicate device and outlines hardware and software updates. However, it does not contain information regarding acceptance criteria and a study proving the device meets those criteria, as typically found in clinical performance studies of AI/ML devices.

Specifically, the document states:

• NON CLINICAL TEST RESULTS: Electrical safety (IEC 60601-1) and electromagnetic compatibility (IEC 60601-1-2) were determined.
• IN VITRO TEST RESULTS: "Verification, validation, and testing activities, were conducted to demonstrate compliance to the product's specifications and compliance to safety and effectiveness requirements."

This implies that the testing performed for this 510(k) submission was primarily focused on hardware updates, software compatibility, and meeting electrical and EMC standards, rather than evaluating the performance of an AI/ML algorithm against a predefined set of diagnostic or predictive acceptance criteria.

Therefore, for your specific request regarding acceptance criteria and a study proving the device meets them, the following information is not present in the provided text:

1. Table of acceptance criteria and reported device performance: Not available. The document refers to "product's specifications" and "safety and effectiveness requirements" in a general sense, but no specific performance metrics or acceptance thresholds are listed.
2. Sample size used for the test set and data provenance: No test set involving patient data or clinical outcomes is described. The "testing activities" refer to non-clinical and in-vitro testing for compliance.
3. Number of experts used to establish the ground truth and qualifications: Not applicable, as there's no clinical performance evaluation involving expert consensus on patient data.
4. Adjudication method: Not applicable.
5. Multi Reader Multi Case (MRMC) comparative effectiveness study: Not applicable.
6. Standalone (algorithm only) performance: Not applicable, as it's a data management system hardware/software update, not an AI/ML algorithm.
7. Type of ground truth used: Not applicable.
8. Sample size for the training set: Not applicable, as no AI/ML model training is described.
9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document describes a 510(k) submission for updates to a medical device focused on data management (Sorin CONNECT), demonstrating substantial equivalence through non-clinical and in-vitro testing for hardware/software functionality, electrical safety, and EMC. It does not provide the kind of performance data, acceptance criteria, or study details typically associated with AI/ML device evaluations.

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The Barco Nexxis OR system is designed to allow transfer, selection and distribution of A/V signals and medical images from various commercially available instruments that are commonly used in a medical procedure laboratory or operating room.
The Barco Nexxis OR system is also designed for transfer, selection and distribution of human interface signals and control signals of non-medical room equipment,
The Barco Nexxis OR system allows control and selection of these signals from a central point.
The Barco Nexxis OR system is not intended to be used for remote or robotically assisted surgery,
The Barco Nexxis OR system is not intended to be used in the vicinity of MRI or other devices that use strong magnetic fields.

The generic Nexxis OR system is a solution for video distribution in operating rooms and medical procedure laboratories. The generic nexxis OR can consist of any possible combination of the nexxis encoders/decoders that transfer video signals from various available devices to one or more output devices (displays). Additionally the nexxis components can transfer audio signals and USB signals. The USB communication is intended only for keyboard and mouse control and control signals for non-medical room equipment. The selection of sources and output devices is done by control software (NMS API). The generic Nexxis OR system can also contain, optionally, the MNC-180 compositor device and/or a MDSC-8258 MNA display. The Nexxis OR system may consist of any combination of the following components: a) Nexxis OR components (MNA adapters (encoders and decoders), NMS management SW, network switches, SFP+ modules) b) A 58-inch large screen LCD monitor: Barco MDSC-8258 MNA. (optional) c) Nexxis Compositor (MNC-180) (optional) d) Cables e) Documentation

The provided document describes the FDA 510(k) premarket notification for the Barco Nexxis OR system, a device for A/V signal and medical image distribution in operating rooms. The document focuses on demonstrating substantial equivalence to a predicate device, as required for 510(k) submissions.

Based on the provided text, here's an analysis regarding acceptance criteria and the study that proves the device meets them:

Core Finding: This document does not describe a study that establishes detection or diagnostic performance metrics against a clinical ground truth for the Nexxis OR system. Instead, it describes bench testing to demonstrate the device meets specified performance characteristics compared to a predicate device. The acceptance criteria and "studies" are focused on engineering and functional equivalence rather than clinical diagnostic accuracy, as this is a video distribution system, not a diagnostic imaging device.

Acceptance Criteria and Reported Device Performance

The document states:
"The tests showed that the device has similar or superior characteristics compared to the predicate device and did not reveal new issues of safety and effectiveness."

While specific quantitative acceptance criteria are not explicitly detailed in a table, the document lists the types of tests performed:

The overall acceptance criterion is that the Nexxis OR system performs comparably or better than the predicate device without introducing new safety or effectiveness concerns, specifically for its function of distributing A/V signals and medical images.

Study Details (as inferable from the document)

Given that this is a video distribution system rather than a diagnostic AI or imaging device, the typical clinical study parameters requested (e.g., sample size for test set with ground truth, expert readers, MRMC, standalone performance) are not applicable in the context of this 510(k) submission. The "study" here refers to engineering and system performance testing.

1. Sample Size Used for the Test Set and Data Provenance:

   • Test Set: Not applicable in the traditional sense of a clinical test set with patient data. The "test set" would be the configurations and operational scenarios of the Nexxis OR system itself. The document does not specify the number of test cases or images used; it refers to "bench tests," "display validation tests," "Nexxis OR qualification tests," and "system tests."
   • Data Provenance: Not applicable. The device handles existing clinical image data, but it does not generate or interpret new diagnostic data. The focus is on the faithful transmission and display of signals.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

   • Not applicable. Ground truth in this context would relate to the technical specifications and fidelity of A/V signal transmission, not clinical diagnoses. These tests would have been performed by engineers or qualified technical personnel, verifying signal integrity, latency, resolution, etc., against predefined technical standards.

3. Adjudication Method for the Test Set:

   • Not applicable. There is no "adjudication" in the clinical sense mentioned, as no human readers are interpreting diagnostic outcomes. Technical test results would be compared against engineering specifications.

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, not done. This type of study is for diagnostic or AI-assisted devices that impact human reader performance. The Nexxis OR system is a signal distribution system, not an AI or diagnostic aid.

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

   • Not applicable. The device itself is a "standalone" system in its function (i.e., it performs its function of signal distribution independently). The concept of "standalone performance" for an algorithm's diagnostic accuracy is not relevant here. The bench and system tests would assess its technical performance.

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

   • The "ground truth" would be engineering specifications and technical standards for A/V signal quality, latency, resolution, and system functionality. For example, testing might involve comparing displayed image quality to source image quality, measuring signal latency, or verifying the correct routing of signals according to control inputs. This is not a clinical ground truth for diagnostic accuracy.

7. The sample size for the training set:

   • Not applicable. The Nexxis OR system is a hardware and software system for signal distribution, not a machine learning model that requires a training set.

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

   • Not applicable. No machine learning training set is involved.

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The kit is intended to be used by healthcare professionals to integrate the video from various commercially available instruments commonly used in a medical procedure into a single video display.

The NIVR58-T kit is a set of devices bundled together by Barco as a system, to be used in a larger system of a customer.

The system that Barco composes, consists of the following devices:
a) A 58-inch large screen LCD monitor: Barco MDSC-8258 MNA.
b) Nexxis Compositor (MNC-180)
c) Nexxis OR components
d) A control unit panel PC (tablet PC): Barco MTPP-0212 (other name: Proscribe) with control user interface.
e) Cables
f) Documentation

The NIVR58-T kit is the visualization part in an operating room or cathlab. The video sources from different medical devices in the OR are connected to the NIVR58-T kit. The NIVR58-T kit makes a composition of the video signals and sends the composition signals to the 58-inch display.

The selection of the video sources that have to be displayed and the layout of the composition is done by the Nexxis OR component and controlled by the control unit MTTP 0212, on which a Graphical User Interface is installed.

The provided text is a 510(k) premarket notification for the Barco NIVR58-T Kit, a medical device intended to integrate video from various instruments into a single display for healthcare professionals, primarily in operating rooms or cath labs.

Based on the information provided in the document, here's a breakdown of the acceptance criteria and the study that proves the device meets them:

Core Finding: The document does NOT contain information regarding acceptance criteria or a study proving the device meets those criteria, as typically understood for AI/ML or diagnostic performance.

Instead, this 510(k) focuses on demonstrating substantial equivalence to a previously cleared predicate device (SIVR56-T, K133994). The "studies" mentioned are bench tests to confirm that the new device has similar or superior characteristics without introducing new safety or effectiveness issues.

Therefore, many of the requested items (e.g., sample size for test set, number of experts for ground truth, MRMC study, standalone performance, training set details) are not applicable to this type of regulatory submission, as it's not a diagnostic AI/ML device requiring clinical performance validation in the way implied by the question.

However, I can extract and present the relevant information that is present, addressing the spirit of the question within the context of a substantial equivalence claim for a display and video integration system.

Overview from the Document:

The NIVR58-T kit is a video integration and display system for medical use, specifically designed to combine video streams from various medical instruments onto a single large-screen display in an operating room or cath lab. The regulatory pathway chosen is a 510(k) premarket notification, which relies on demonstrating substantial equivalence to a legally marketed predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

As this is a 510(k) for substantial equivalence of a medical display and video integration system, the "acceptance criteria" are not performance metrics like sensitivity/specificity for a diagnostic AI. Instead, they relate to characteristics being similar or superior to the predicate device and not introducing new safety/effectiveness issues.

• Display validation tests
• Nexxis OR qualification tests
• System tests
  These tests are implicitly the "study" proving the device meets the characteristics. The document does not provide numerical results from these tests, only the conclusion that they showed similar or superior characteristics. |
  | Safety and Performance: No new issues of safety and effectiveness are revealed. | Met: "Bench testing... did not reveal new issues of safety and effectiveness." This is a qualitative assessment based on the bench testing. The document explicitly states: "Animal or clinical testing have not been performed." This is common for devices demonstrating substantial equivalence where bench testing sufficiently addresses the changes. |

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

• Sample Size: Not specified in terms of clinical cases or patient data. The "test set" for this device consists of the physical device components themselves undergoing bench testing. The testing is a series of engineering and performance tests on the hardware and software, not a study of patient data.
• Data Provenance: Not applicable in the sense of patient data. The tests were performed on the device components and system as engineered and manufactured by Barco N.V. (located in Kortrijk, Belgium, as indicated by the address). The tests are internal validation tests by the manufacturer.

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

• Not Applicable: For this type of device (medical display and video integrator), "ground truth" as typically understood in AI/ML or diagnostic studies (e.g., expert labels on images) is not relevant. The "ground truth" for the bench tests would be the engineering specifications and performance standards of the device. These are established by the manufacturer's engineers and quality assurance teams, not by medical experts interpreting data.

4. Adjudication Method for the Test Set

• Not Applicable: Adjudication methods (like 2+1 or 3+1 consensus) are used for resolving disagreements in human expert labeling of data, especially in diagnostic tasks. Since the "test set" involves bench testing of hardware/software against engineering specifications, such adjudication is not performed. Test failures would be investigated and resolved by engineering and quality teams.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No: The document explicitly states: "Animal or clinical testing have not been performed." An MRMC study is a type of clinical study involving multiple human readers interpreting cases, often with and without AI assistance, to assess diagnostic performance. This was not required or performed for this 510(k).

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

• Not Applicable in the traditional sense of a diagnostic algorithm: While the device's components (e.g., Nexxis Compositor MNC-180 for video composition) perform algorithmic functions, this is not a diagnostic AI algorithm that operates "standalone" to produce a medical finding. Its "standalone" performance would be its ability to correctly process and display video signals according to specifications, which is covered by the bench tests.

7. The Type of Ground Truth Used

• Engineering Specifications and Performance Standards: The "ground truth" for proving substantial equivalence here relies on demonstrating that the device's technical specifications and measured performance (e.g., display quality, video processing capability, signal latency) meet or exceed those of the predicate device, and that it reliably performs its intended function of video integration and display. This is verified through objective bench testing.

8. The Sample Size for the Training Set

• Not Applicable: This device is not an AI/ML algorithm that requires a "training set" of data for learning. Its functionality is hard-coded or configured based on engineering design.

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

• Not Applicable: As there is no AI/ML training set, the concept of establishing ground truth for it does not apply.

In summary, the provided document describes a 510(k) submission for a non-AI/diagnostic medical device (a display and video integration system). The "acceptance criteria" and "studies" are framed within the context of demonstrating substantial equivalence to a predicate device through engineering bench testing rather than clinical performance studies using patient data or AI/ML algorithms.

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