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The Stöckert SS System is intended to be used during cardiopulmonary bypass for procedures lasting six (6) hours or less.

The S5 System is configurable to user needs with different system components. The main configurable and optional system components consist of: Console, System Panel, Pumps, Bubble detector, Level control, Temperature monitor, Pressure control, Cardioplegia Control, Electronic gas blender, Serial Data interface, Venous line clamps (manual), Electrical venous occlude (EVO).

The provided text is a 510(k) summary for the Stöckert S5 System, a Class II medical device (Heart-Lung Machine). It describes the device, its intended use, and the non-clinical testing performed to demonstrate substantial equivalence to a predicate device.

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

No specific acceptance criteria (numerical thresholds for performance metrics) or a study proving that the device meets those criteria is explicitly provided in the typical format one would expect for an AI/ML device.

The document is a 510(k) Pre-market Notification, which focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving a device meets specific performance acceptance criteria against a clinical endpoint with defined sensitivity/specificity/accuracy thresholds. The studies described are primarily verification and validation (V&V) testing to ensure the device performs as intended and is safe and effective compared to its predicate.

Therefore, the interpretation below will focus on understanding the "acceptance criteria" through the lens of substantial equivalence and the "study" as the non-clinical testing performed for this purpose.

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document doesn't provide a typical table of acceptance criteria with numerical targets (e.g., sensitivity, specificity, accuracy). Instead, the "acceptance criteria" are implied by compliance with recognized standards and successful completion of various V&V tests, demonstrating that the device functions correctly and safely. The "reported device performance" is the successful completion of these tests.

Regarding the remaining requested information, it's important to understand that this document describes a hardware/software medical device (a Heart-Lung Machine) and not an AI/ML diagnostic or prognostic algorithm. Therefore, many of the requested fields are not applicable or cannot be answered from the provided text because they relate specifically to AI/ML performance evaluation.

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

• Not Applicable / Not Provided: The nature of the testing (electrical safety, EMC, mechanical, software V&V) does not typically involve "test sets" of patient data or data provenance in the way an AI/ML diagnostic device would. These tests are performed on the device itself or its components. The document does not specify sample sizes for these engineering tests (e.g., how many units were tested for electrical safety).

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: This type of medical device (Heart-Lung Machine) does not involve establishing ground truth from expert interpretation in the context of diagnostic or prognostic tasks. The "ground truth" for its performance is determined by meeting engineering specifications and safety standards.

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

• Not Applicable: Adjudication methods are relevant for resolving discrepancies in expert interpretations of data, typically in AI/ML performance studies. This is not described for the V&V testing of a Heart-Lung Machine.

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, a MRMC study was not done. This is a non-AI/ML device. The "multi reader multi case" paradigm and "human readers improve with AI" concepts are not applicable to the S5 System as described.

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

• Not Applicable / Not Relevant in the AI/ML sense: The device is a standalone hardware/software system designed to perform its functions. The V&V testing assesses its performance in this standalone capacity. However, this is not equivalent to a "standalone" AI algorithm performance evaluation, as it's an operational medical device with human interaction as part of its intended use.

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

• Engineering Specifications and Recognized Standards: The "ground truth" for the device's performance is compliance with established engineering specifications, design requirements, and recognized international standards (e.g., IEC 60601 series, IEC 62366-1, IEC 62304, ISO 14971) for safety and effectiveness.

8. The sample size for the training set

• Not Applicable: This device is not an AI/ML system that uses a "training set" of data.

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

• Not Applicable: This device is not an AI/ML system that uses a "training set" of data.

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The Stöckert Gas Blenders (2L, 5L, and 10L) are intended to enable qualified personnel to set monitor and control gas flows of medical grade gases (air/02/CO2) during cardiopulmonary bypass. The Stockert Gas Blenders are used as components of or optional accessories to the Stöckert S5 System (or any compatible Sorin system using the S5 firmware versions of 3.0 or greater) for periods of six hours or less.

The Stöckert Gas Blender Base Unit is 200 mm wide, 185 mm high, and 270 mm deep and it weighs 2.5 kg. It can be used either from a suitable horizontal surface such as a table or cart or it can be mounted on one of the masts (using the mast holder). It is attached to HLM console with the supplied 24V/CAN connector. This connection provides both power and communication between the Stöckert Gas Blender and the System Display Panel of the HLM. The Stöckert Gas Blenders are designed to provide a maximum mixed gas flow rate of 2L/min, 5L/min or 10 L/min depending on the model used to allow the perfusionist to purge the oxygenator with gas during the priming process. The front panel is the user interface and includes the on/off key, displays and controls. The rear panel houses the three inlet gas connections, the gas mix outlet, and the 24V/CAN connector. Gas line connectors are fast release connectors, consisting of a male connector (installed on the tubing) and a coupler socket (on the housing of the Gas Blender). Both sets (male and female) of connectors are labeled with the respective gas for which they are intended to be used. The Stöckert Gas Blenders enable the qualified perfusionists who are managing the cardiopulmonary bypass circuit using a Stöckert S5 or Sorin C5 System to precisely set, monitor and control the gas flows required for the oxygenation of the patient's blood during extracorporeal circulation. The supply sources for air, O2 and CO2 are connected to the rear inlets of the Stöckert Gas Blender and the gas mixture outlet is connected to the oxygenator (not supplied). The Stöckert Gas Blenders are optional accessories to and designed to be operated with the Stöckert S5 System/Sorin C5 System. They cannot be operated independently from the heart lung machine console. The values for total gas flow (air + O2), FiO2 and CO2 can be adjusted independently without affecting the remaining two values. Gas flow is displayed at both the Stöckert Gas Blender base unit and the remote display module situated in the Stöckert S5 System/Sorin C5 System "control desk" or "System Panel." Set values and actual values are continuously monitored and any discrepancy between them causes both optical and acoustic alarms. If desired, the perfusionist may set alarm limits at the remote display module to monitor the blood flow/gas flow ratio for the arterial pump.

The provided text describes the Stöckert Gas Blender and its FDA 510(k) submission. However, it does not include detailed information about acceptance criteria or a study that specifically proves the device meets those criteria in the format requested.

The document is a 510(k) summary for a medical device (Stöckert Gas Blender). For this type of submission, substantial equivalence to a predicate device is the key, not necessarily a detailed clinical study demonstrating specific performance metrics against pre-defined acceptance criteria in the way a new drug or a novel AI diagnostic might.

Based only on the provided text, here's what can be extracted and what is missing:

1. Table of acceptance criteria and reported device performance:

This information is not provided in the document. The document states that "Design verification and validation testing presented in the 510(k) included electrical/safety testing (IEC60601-1), EMI/EMC testing (IEC60601-1-2), and Functional Acceptance testing, and design validation testing (internal and external)." However, it does not list the specific acceptance criteria for these tests (e.g., accuracy of gas flow, precision of mixture) or the numerical performance results of the device against those criteria.

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

This information is not provided. The document mentions general "testing" but no specific test sets or their characteristics.

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/not provided. This device is a gas blender for heart-lung machines, not an AI diagnostic that requires expert interpretation for ground truth. Its performance would be validated through engineering and functional testing.

4. Adjudication method for the test set:

This information is not applicable/not provided. (See point 3).

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

No. The document explicitly states: "No formal clinical testing was provided as the basis for substantial equivalence or is required." Therefore, no MRMC study was conducted or reported here.

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

This concept is not applicable to this device. The Stöckert Gas Blender is a physical medical device that blends gases, not an algorithm. Its "standalone performance" would be its functional performance, which is generally verified through engineering tests, but specific details are not provided in this summary.

7. The type of ground truth used:

This information is not explicitly stated or applicable in the traditional sense of clinical ground truth (e.g., pathology). For a physical device like a gas blender, "ground truth" would be established by reference standards, calibrated measurement equipment, and compliance with engineering specifications. The document mentions "prospectively defined design and performance specifications," which would form this basis.

8. The sample size for the training set:

This information is not applicable/not provided. As this is a physical device and not an AI/machine learning algorithm, there is no "training set."

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

This information is not applicable/not provided (see point 8).

In summary:

The provided document is a 510(k) summary for a medical device focused on demonstrating substantial equivalence to a predicate device. It confirms that engineering, safety, and functional testing were performed but does not delve into the specific acceptance criteria or the detailed results of those tests, nor does it involve clinical studies or AI-related performance metrics.

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The modified Stöckert S5 System is intended to be used for cardiopulmonary bypass for periods of six (6) hours or less.

The Stöckert Electrical Venous Occluder is an optional accessory to and designed to be operated with the Stöckert S5 System. It cannot be operated independently from the S5 heart lung machine console. It consists of an Occluder Unit and Control Unit to provide

The provided text states that "Design verification and validation information provided in this Special 510(k): Device Modification demonstrates that the product meets prospectively defined design and performance specifications." However, it does not explicitly list the acceptance criteria or the specific studies conducted to prove that the device meets these criteria. Therefore, I cannot generate the requested table or answer most of the questions directly.

Based on the available information, here's what can be provided:

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

This information is not explicitly provided in the document. The filing states that the device "meets prospectively defined design and performance specifications" but does not detail what those specifications or the corresponding performance outcomes were.

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

This information is not explicitly provided in the document.

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

This information is not explicitly provided in the document.

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

This information is not explicitly provided in the document.

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 as the device is a "Heart Lung Machine" and an "Electrical Venous Occluder," not an AI-assisted diagnostic tool for human readers.

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

This information is not applicable as the device is a "Heart Lung Machine" and an "Electrical Venous Occluder," not an algorithm.

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

This information is not explicitly provided in the document. Given the nature of medical device verification and validation for a heart-lung machine accessory, the "ground truth" would likely be based on engineering specifications, physiological parameters, and safety standards rather than expert consensus on image interpretation or pathology.

8. The sample size for the training set:

This information is not explicitly provided in the document.

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

This information is not explicitly provided in the document.

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The modified Stöckert S5 System is intended to be used during cardiopulmonary bypass for procedures lasting six (6) hours or less.

The Stöckert Gas Blender is an optional accessory to and designed to be operated with the Stöckert S5 System. It cannot be operated independently from the heart lung machine console.

The provided text describes a 510(k) summary for a medical device (Stöckert S5 System Gas Blender) and a letter from the FDA regarding its substantial equivalence. This document focuses on regulatory approval based on demonstrating equivalence to an existing device, rather than presenting a performance study with detailed acceptance criteria and results as typically seen for novel AI/ML medical devices.

Therefore, many of the requested sections (e.g., sample size for test set, number of experts for ground truth, MRMC study, training set details) are not applicable or not available in this type of regulatory submission. The information provided is primarily related to design verification and validation, ensuring the device meets its own specifications and is safe and effective when compared to a predicate device.

Here's an attempt to answer the questions based only on the provided text, acknowledging the limitations:

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

The document states that "Design verification and validation information provided in this Special 510(k): Device Modification demonstrates that the product meets prospectively defined design and performance specifications." However, no specific quantitative acceptance criteria or their corresponding performance results are detailed in the provided text. The statement is a general declaration of compliance.

2. Sample sized 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 submission mentions "testing" but does not elaborate on the sample sizes or provenance of any data used for testing.

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 in the context of this device's regulatory pathway. The Stöckert S5 System Gas Blender is an accessory to a heart-lung machine, and its performance evaluation would likely involve engineering and functional testing against specifications, rather than expert-adjudicated ground truth data sets in the way an AI diagnostic device would.

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

This information is not applicable for the reasons stated in question 3.

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. The device is not an AI/ML diagnostic or assistive tool, but rather a physical component (gas blender) for a heart-lung machine. Therefore, an MRMC study comparing human reader performance with and without AI assistance is irrelevant.

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

This information is not applicable. The device is a physical hardware component and does not operate as a standalone algorithm.

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

This information is not applicable. For a device like a gas blender, ground truth would typically relate to known physical properties, engineering specifications, and validated measurement techniques, rather than expert-derived medical diagnoses or outcomes data. Its functionality would be verified based on its ability to accurately blend gases according to set parameters.

8. The sample size for the training set

This information is 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

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

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The Stöckert S5 System is intended to be used during cardiopulmonary bypass surgery lasting six (6) hours or less.

The Stöckert S5 System, like the parent S3 System, is a modular system consisting of a console, various pumps, monitors, displays, controls, and user interfaces.

This document describes the Stöckert S5 System, a heart-lung machine intended for use during cardiopulmonary bypass surgery. However, the provided text does not contain the detailed acceptance criteria or a study proving the device meets specific performance metrics in the format requested.

The document states: "Testing of the Stöckert S5 System demonstrated that the System fulfills prospectively defined performance criteria and included electrical safety, electromagnetic compatibility, software validation, and functional testing." This is a general statement of testing, but it does not provide the specific criteria or the results of those tests in a quantifiable manner.

Therefore, I cannot populate the table or answer most of the questions as the information is not present in the provided text.

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

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). The text only mentions "prospectively defined performance criteria" but doesn't detail the data used for the testing.

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

• Not applicable. This device is a heart-lung machine, not an imaging or diagnostic device requiring expert interpretation for ground truth. The "ground truth" for such devices typically relates to engineering specifications and functional safety.

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

• Not applicable. See point 3.

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. This is not an AI-assisted diagnostic or imaging device. It's a medical device for cardiopulmonary bypass.

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

• Not applicable. This question is usually for diagnostic algorithms. For a mechanical device, performance is inherently "standalone" in its function, but the question's context implies AI. The device itself is a standalone system without an "algorithm only" performance that would be separated from human interaction in the way diagnostic AI is typically evaluated.

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

• Implied Engineering and Safety Standards: For a heart-lung machine, the "ground truth" for performance testing would be adherence to established engineering specifications, electrical safety standards (e.g., IEC 60601-1), electromagnetic compatibility standards (e.g., IEC 60601-1-2), and verified software functionality according to design specifications. The document mentions "electrical safety, electromagnetic compatibility, software validation, and functional testing," which align with this type of ground truth.

8. The sample size for the training set:

• Not applicable. This device is a physical medical device, not a machine learning model that requires a "training set."

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

• Not applicable. See point 8.

In summary, the provided 510(k) summary focuses on establishing substantial equivalence to a predicate device (Stöckert S3 System) based on intended use, indications for use, operational characteristics, and fundamental technological characteristics. While it mentions performance testing, it does not elaborate on the specific acceptance criteria or detailed results of those tests in the way requested for a performance study.

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