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The VitalFlow Console controls the speed of the VitalFlow Centrifugal blood pump during extracorporeal cardiopulmonary life support for adult patients with acute respiratory failure or acute cardiopulmonary failure, where other available treatment options have failed, and continued clinical deterioration is expected or the risk of death is imminent. The VitalFlow Centrifugal pump is driven by the VitalFlow Motor Drive or the VitalFlow Emergency Handcrank.

The VitalFlow Console provides control of blood pumping through an extracorporeal circuit during extracorporeal membrane oxygenation (ECMO) procedures. The console powers the VitalFlow motor drive unit which provides rotation of the VitalFlow Centrifugal pump. Pump motor speed (RPM) can be adjusted by the user and flow and bubble detection is provided by an ultrasonic flow probe and displayed on the touchscreen. The touchscreen display allows users to set alarm limits for all measured parameters. The device will alarm visually and audibly when limits are exceeded. Status indicators, power/battery life and secondary RPM indicator are provided. Data download and data streaming from the console is available for ECMO circuit data only; no patient data is stored for output.

The provided FDA 510(k) clearance letter for the VitalFlow Console (K250199) generally describes the device and its indications for use, and makes a case for substantial equivalence to a predicate device. However, it does not provide specific details about acceptance criteria or the study that proves the device meets those criteria, particularly regarding AI or algorithm performance.

The core of this submission is about a software upgrade to an existing hardware device (VitalFlow Console) which enables software upgrades via USB. The FDA clearance is based on demonstrating that this change does not raise new questions of safety or effectiveness and maintains the existing performance characteristics. Therefore, the information typically requested in your prompt (e.g., sample size for test sets, number of experts for ground truth, MRMC studies, AI effect size, etc.) would primarily be relevant if the upgrade introduced a new AI/algorithmic component or significantly altered a measurement or diagnostic function that required such validation.

Based on the document, here's what can be extracted and what cannot:

Analysis of the Provided Document Regarding Acceptance Criteria and Performance Study:

The document explicitly states:
"Software verification (which included Cybersecurity) was used to verify the performance characteristics of the subject device with the upgrade over USB change."
And:
"Substantial equivalence of the performance characteristics is demonstrated through regression testing. The VitalFlow Console (Upgrade Over USB) continues to meet international standards for safety and has demonstrated effectiveness at maintaining the device performance."

This indicates that the performance study focused on regression testing to ensure the new software upgrade feature (upgrade over USB) did not negatively impact the existing performance of the device or introduce new risks, especially related to cybersecurity. It was not a de novo study to establish new performance metrics for an AI or algorithmic component, as the device's fundamental function (controlling a blood pump) remains unchanged, and the cleared modification is about the method of software updating.

Therefore, most of the specific questions about AI/algorithm performance studies (e.g., number of experts, ground truth type, MRMC study) are not applicable to the scope of this particular 510(k) submission.

Extracted Information:

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

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

• Sample Size: Not explicitly stated. The testing mentioned is "Software verification" and "regression testing." For software changes, the "test set" would typically refer to the test cases used in the verification and validation (V&V) activities. The document does not provide the number of test cases run or the duration/number of runs for regression testing.
• Data Provenance: Not explicitly stated. Given that it's regression testing for a software update on an existing device, it would likely involve internal testing data and potentially real-world data from the existing predicate device's operational environment. It's implicitly a combination of retrospective (based on existing device performance) and prospective (new tests for the updated software) testing, but the document doesn't specify. Country of origin not mentioned.

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

• Not Applicable / Not Stated. This type of information is typically required for studies establishing diagnostic accuracy or clinical effectiveness, often involving human readers/interpreters. This submission focuses on a software update for a control console, where "ground truth" would relate to the correct functioning of the software and hardware rather than clinical interpretation by experts. "Software verification" and "regression testing" would involve engineering and software quality assurance expertise to establish whether the device performed as specified.

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

• Not Applicable / Not Stated. Adjudication methods are relevant for subjective interpretations (e.g., medical images). For software verification and regression testing, failures are objective (e.g., a test case passes or fails, a function behaves as expected or not).

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. An MRMC study was not conducted as this submission is not about a diagnostic AI/CAD device or a device that assists human interpretation in a new way. It's about a software update for a control console.

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

• Not Applicable / Not Stated in this context. The "algorithm" here is the control software for the blood pump. Its "standalone" performance would be measured by its ability to precisely control the pump's RPM, detect flow, and manage alarms, which is what "software verification" and "regression testing" would assess. The document confirms these existing performance characteristics are maintained.

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

• Not Applicable in the traditional sense. For software verification and regression testing, the "ground truth" is the established functional and performance requirements of the device. This is confirmed through testing against design specifications, system requirements, and pre-existing performance benchmarks of the predicate device.

8. The sample size for the training set:

• Not Applicable. This is not an AI/Machine Learning device where a "training set" is relevant. The "software update" refers to a traditional software engineering change, not an AI model retraining.

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

• Not Applicable. As above, no training set for an AI model.

Summary of Device and Changes:

• Device: VitalFlow Console, controls a centrifugal blood pump for extracorporeal cardiopulmonary life support.
• Predicate: VitalFlow Console (K230364).
• Change in K250199: Enables software upgrades to be uploaded through the USB port (previously required Medtronic service personnel to open the console and connect laptops to individual boards).
• Regulatory Focus: Ensuring the new USB update feature does not compromise the safety or effectiveness of the device, particularly regarding cybersecurity and maintaining existing performance.
• Performance Study Type: Software verification and regression testing, indicating an engineering-focused validation rather than a clinical outcome or AI performance study.

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The VitalFlow™ Console controls the VitalFlow™ Centrifugal blood pump during extracorporeal cardiopulmonary life support for adult patients with acute respiratory failure or acute cardiopulmonary failure, where other available treatment options have failed, and continued clinical deterioration is expected or the risk of death is imminent. The VitalFlow Centrifugal pump is driven by the VitalFlow Motor Drive or the VitalFlow Emergency Handcrank.

The VitalFlow Console provides control of blood pumping through an extracorporeal circuit during extracorporeal life support (ECLS) procedures. The console powers the VitalFlow motor drive unit which provides rotation of the VitalFlow Centrifugal pump. Pump motor speed (RPM) can be adjusted by the user and flow and bubble detection is provided by an ultrasonic flow probe and displayed on the touchscreen. The touchscreen display allows users to set alarm limits for all measured parameters. The device will alarm visually and audibly when limits are exceeded. Status indicators, power / battery life and secondary RPM indicator is provided. Data download and data streaming from the console is available for ECLS circuit data only; no patient data are stored or exported.

The VitalFlow Emergency Handcrank (drive unit) can be used in emergency situations to manually drive the centrifugal pump in the case of console and or motor drive failure.

The provided text describes the VitalFlow™ Console, an extracorporeal circuit component, and its FDA 510(k) clearance (K230364). The document outlines the device's indications for use, description, and the performance evaluations conducted to demonstrate substantial equivalence to a predicate device.

However, the document does not contain the detailed information necessary to answer all parts of your request, specifically regarding acceptance criteria for an AI/ML model, sample sizes for test sets in an AI context, expert ground truth establishment, MRMC studies, or training set details for an AI model. This is likely because the VitalFlow™ Console is a mechanical/electronic medical device, not an AI/ML-driven diagnostic or assistive device that would typically undergo such evaluations.

The "Bench Performance Evaluations" section lists various studies (Hydraulic performance, Reliability, EMC, Electrical Safety, Software Validation, Usability Studies), but these are standard engineering and regulatory tests for hardware and software functionality, not AI model performance.

Therefore, I cannot provide a table of acceptance criteria for an AI model or details about AI-specific testing from this document.

If you are looking for information regarding an AI/ML-driven medical device, this document does not seem to pertain to such a device.

From the provided text, I can infer the following about the device and its testing:

• Device Type: The VitalFlow™ Console is an extracorporeal circuit and accessories for long-term respiratory/cardiopulmonary failure (Product Code: QNR, Regulation Number: 21 CFR 870.4100). It controls features like pump motor speed (RPM), flow, and bubble detection.
• Purpose of Studies: The studies were conducted to demonstrate "substantial equivalence" of the VitalFlow™ Console to a predicate device (TandemHeart Escort Controller, K202751) and to meet the "Special Controls" described in 21 CFR 870.4100.
• Nature of Studies: The studies were primarily "Bench Performance Evaluations" and included aspects like hydraulic performance, reliability, EMC, electrical safety, software validation, and usability studies. These are typical engineering and functional performance tests for a physical device.
• Ground Truth: For a physical device like this, "ground truth" would be established through calibrated instruments and established engineering principles for parameters like flow rate, RPM, pressure, and electrical safety. It's not based on expert consensus on image interpretation or pathology.
• No AI Component: There is no mention of an AI/ML component or algorithm within the VitalFlow™ Console. Therefore, concepts like training sets, test sets for AI models, expert adjudication, or MRMC studies are not applicable to the information provided.

In summary, the provided document does not contain the information requested about AI/ML device acceptance criteria and study details because the VitalFlow™ Console appears to be a physical medical device, not an AI-driven one.

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