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The intended use of Compressor Mini is to provide a supply of dry, filtered, compressed air for a medical respiratory ventilator or anaesthesia machine that meets the specifications of the Compressor Mini. Its capacity is approximately 30 Vmin at 50-64 psi (350 - 450 kPa, 3.5 - 4.5 bar). Compressor Mini is intended to be operated by healthcare providers, physicians, nurses and technicians. Compressor Mini is intended to be used for bedside application in a hospital environment. Compressor Mini is not intended to be used during in-hospital transportation or during ambulance or air transportation.

Compressor Mini will provide a supply of dry and filtered compressed air for a medical respiratory ventilator or anaesthesia machine that meets the specifications of the Compressor Mini. Its capacity is approximately 30 Vimin at 50-64 psi (350 - 450 kPa). The compressor is of light weight and compact design. It runs quietly, which makes it suitable for bedside use. It may be employed as a primary or secondary source of compressed air. The Standby function assures that if the central gas supply fails the Compressor Mini will start to deliver compressed air. The Compressor Mini is fitted with two alarm parameters, temperature and pressure. The Compressor Mini is designed with the similar materials and technology as the predicate device ALRECO 40400.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Compressor Mini:

The provided document describes a 510(k) submission for the Compressor Mini, a device intended to provide dry and filtered compressed air for medical respiratory ventilators or anesthesia machines. The submission claims substantial equivalence to a predicate device, the ALRECO 40400.

Crucially, the provided text DOES NOT contain a detailed study report with specific acceptance criteria, sample sizes, ground truth establishment methods, or statistical performance metrics (like sensitivity, specificity, AUC). This document is a 510(k) summary, which focuses on device description, intended use, and substantial equivalence to a predicate device, rather than a standalone performance study.

Therefore, many of the requested items cannot be answered directly from this document. However, I can infer some aspects based on the nature of a 510(k) submission and the information provided:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document does not present a formal table of acceptance criteria with specific performance metrics. The "performance" described is largely functional equivalence to the predicate device and the new device's specifications.

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

• Sample Size for Test Set: Not specified in the provided document. As this is a 510(k) summary for a medical device (a compressor, not an imaging algorithm), the "test set" would likely refer to engineering verification and validation testing, not a diagnostic accuracy study with a "test set" of patient data.
• Data Provenance: Not specified. Based on the regulatory filing, the testing would generally be internal engineering and performance testing conducted by the manufacturer (Siemens), likely at their facilities in Sweden or another location where R&D/manufacturing occurs. It would be retrospective in the sense that the device was designed and then tested to meet specifications.

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

• Not Applicable / Not specified. For a device like a medical air compressor, "ground truth" is established through engineering specifications, validated test methods (e.g., pressure transducers, flow meters, air quality sensors), and compliance with international standards (e.g., for medical electrical equipment, compressed air quality). It would not typically involve human expert adjudication in the way diagnostic imaging studies do.

4. Adjudication Method for the Test Set

• Not Applicable / Not specified. Adjudication methods like 2+1 or 3+1 are used for human expert consensus in diagnostic studies. For a physical device, testing involves objective measurements against predefined engineering specifications.

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

• No, an MRMC comparative effectiveness study was not done. This type of study is relevant for diagnostic algorithms or imaging systems where human readers interpret medical images or data. The Compressor Mini is a physical medical device (an air compressor) that supplies air; it does not involve human interpretation of output data in a diagnostic context.

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

• Yes, a standalone performance assessment (device only) would have been performed. This is implicit in the device's development and regulatory submission. The device is designed to operate autonomously to provide compressed air. Its performance (flow, pressure, air quality, alarm functions, standby function) would have been validated independently through engineering tests without human intervention as part of its core functionality. While the document doesn't detail these tests, they are a fundamental part of a medical device submission.

7. The Type of Ground Truth Used

• Engineering Specifications and International Standards. The "ground truth" for the Compressor Mini would be defined by its design specifications, performance requirements (e.g., specific flow rates, pressure ranges, air quality parameters as per medical gas standards), and compliance with relevant healthcare device standards (e.g., IEC 60601 for electrical safety, specific standards for medical air compressors if applicable). These are objective, measurable criteria.

8. The Sample Size for the Training Set

• Not Applicable / Not specified. This device is a physical product, not a machine learning algorithm. Therefore, there is no "training set" in the computational sense. The device's design and parameters are determined by engineering principles and iterative development, not by training on a dataset.

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

• Not Applicable. As there is no training set, this question is not relevant. The device's "ground truth" (its optimal performance and validated parameters) would be established through a combination of engineering design calculations, component testing, sub-system testing, and full-device verification and validation against its specifications and relevant regulatory standards.

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Use of the Servo Ventilator 300A is indicated for adult, pediatric or neonatal patient populations in an environment where patient care is provided by Healthcare Professionals (Physician, Nurse, Technician), when the professional determines that a device is required to assist the breathing of the patient. The device can be used both for controlling the entire ventilation for patients without any ability to breathe, as well as for supporting patients with reduced ability.

The Servo Ventilator 300A is a modification of the Servo Ventilator 300 which was found Substantially Equivalent on October 25, 1996 (Premarket Notification K960010). The physical differences between the Servo Ventilator 300 and the Servo Ventilator 300A consist of a software change and adding of a new printed circuit board as well as a new switch and two LED's on the front panel.

In the Servo Ventilator 300A a new functionality called Automode has been added, which is a method that, by using functionality from existing breathing modes, allows the patient to better interact with the ventilator. Each controlled mode has a corresponding supported mode. This gives the possibility for the ventilator to react on patient effort - triggering, and lack of effort - apnea. Essentially the ventilator can be set in two states, support or control. Which of these states that are active is determined by a pre-defined algorithm.

This document describes the Siemens Servo Ventilator 300A, a modification of the Servo Ventilator 300. The primary change is the addition of "Automode" functionality, which allows the ventilator to automatically switch between controlled and supported breathing modes based on patient effort.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not contain a specific table of acceptance criteria with quantitative targets (e.g., a specific percentage of accurate mode switches or a defined range for response times). Instead, the acceptance criteria are described qualitatively as ensuring the "Automode" functionality improves patient adaptation and ease of use without adversely affecting safety.

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

The document mentions a "clinical test has also been performed to evaluate the automatic switching between controlled and supported ventilation" (Page 2). However, it does not specify the sample size used for this clinical test (number of patients, number of events, etc.).

The data provenance (country of origin, retrospective/prospective) is not explicitly stated for this clinical test. Given the submitter's address (Sweden), it's possible the test was conducted there, but this is not confirmed. The document only mentions the intended use in the U.S. market.

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

This information is not provided in the document. There is no mention of experts establishing ground truth for the clinical test data.

4. Adjudication Method for the Test Set

This information is not provided in the document.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, What was the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

No MRMC comparative effectiveness study is mentioned. This device is not an AI-assisted diagnostic tool, but rather an automated feature within a medical device (ventilator). The "Automode" functionality is a direct automation of a clinical decision, not an aid for human interpretation. Therefore, the concept of "human readers improve with AI vs. without AI assistance" does not directly apply here.

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

The "Automode" functionality is inherently a standalone algorithmic feature. It automatically switches between modes based on pre-defined algorithms and patient effort, without continuous human intervention for each switch. The clinical test evaluated this automated switching.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

The document does not explicitly state how "ground truth" was established for the clinical test of automatic switching. It's implied that the success of the automatic switching was evaluated based on its clinical appropriateness and effect on patient ventilation, likely against established physiological responses or clinical standards, but the specific method (e.g., expert review of ventilator logs, patient outcomes, direct observation) is not detailed. Crucially, it speaks to whether the system "improves the adaptation of the ventilator to the patient needs," suggesting clinical observation or physiological measurements as the basis for evaluation.

8. The Sample Size for the Training Set

The document refers to the "Automode" as a "software change and adding of a new printed circuit board" based on "functionality from existing breathing modes" (Page 1). This implies that the underlying algorithms and logic were primarily designed based on existing knowledge of ventilator operation and clinical practice, rather than trained on a large dataset in the sense of machine learning. Therefore, a "training set" in the context of deep learning or statistical model training is not applicable or mentioned. The "design" of the device was validated, not "trained."

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

As noted in point 8, a "training set" in the modern AI sense is not explicitly used or described. The "ground truth" for the device's design implicitly comes from established medical and engineering principles of ventilation, rather than a data-driven training process with established ground truth labels. The "design" itself reflects clinicians' understanding of optimal ventilation strategies.

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The Siemens Servo Ventilator 300 is intended for general and critical ventilatory care for use with neonatal, infant, pediatric, and adult patients The unit is designed to be used at the bedside and for in-hospital transport. It is not intended for transport use in ambulances or helicopters in the U.S. market.

The intended use of the Computer Interface Board Version 2 is the same as for the Computer Interface Board Version 1. The CI board stores and transmits information about the ventilator to external digital devices via optically isolated serial interfaces.

The Servo Ventilator 300 and Computer Interface Version 2 is a modification of the Servo Ventilator 300 and Computer Interface Version 1 which was found Substantially Equivalent on June 26, 1991 (Premarket Notification K902859). These modifications are being made to update the hardware design and to make additional software features available, while retaining the original functionality.

The Servo Ventilator 300 Alarm and Monitoring Module has been modified to eliminate false or otherwise unnecessary alarms by eliminating the "Leakage Alarm" feature, and miscellaneous minor improvements to other alarm functions. This improves ease of use, and has the additional benefit of improving user vigilance when real alarms occur.

The Computer Interface, CI, is an accessory circuit board that interfaces the ventilator to an external information-gathering system, such as a personal computer, via asynchronous serial lines. Information, such as trend data, real time parameter values, and technical information, is transferred to the external system via different commands. The modifications in the Version 2 hardware improve reliability and manufacturing efficiency. The modifications in the Version 2 software allow the user to select from a wider variety of data channels and add the transmission of checksums to ensure data integrity.

This 510(k) summary describes a ventilator and computer interface, not an AI/ML device, therefore, the requested information elements related to AI/ML device performance (e.g., sample size for test set, number of experts, MRMC studies, standalone performance, training set details) are not applicable.

Here's an analysis based on the provided text, focusing on the acceptance criteria and the study proving the device meets them for this traditional medical device:

1. Table of Acceptance Criteria and Reported Device Performance

2. Reduced risk of ventilator shutdown due to component failures on the Computer Interface Board. | 1. The two circumstances triggering the predicate device's leakage alarm (gross leaks, malfunctioning flow transducer) will also trigger the expired minute volume alarm, thus "no reduction in patient safety from removing this alarm function."
3. Hardware improvements to the Computer Interface Board "affect the safety and effectiveness of the Servo Ventilator 300 by reducing the risk of ventilator shutdown as a result of component failures on the Computer Interface Board." |
   | Effectiveness/Functionality | 1. Retain original functionality despite hardware/software modifications.
4. Improve ease of use (related to alarm logic).
5. Computer Interface to store and transmit information about the ventilator to external digital devices.
6. Computer Interface to provide an expanded list of data items. | 1. Modifications made "while retaining the original functionality."
7. Alarm logic modifications "improves ease of use."
8. Computer Interface (CI) "stores and transmits information about the ventilator to external digital devices via optically isolated serial interfaces."
9. Software modifications "introduce new functions which provide the external data gathering system with an expanded list of data items that can be queried from the Servo Ventilator." |
   | Performance (Technical) | 1. Reliability improvements for both Servo Ventilator 300 and Computer Interface.
10. Manufacturing efficiency improvements.
11. Immunity to interference (for CI).
12. Data integrity (for CI, via checksums).
13. All alarm conditions simulated and output channels tested; tests passed according to criteria equal to or more stringent than predicate device. | 1. Servo Ventilator 300 hardware modifications "improve reliability." CI hardware design changes "improve reliability."
14. CI hardware design changes "simplify manufacturing."
15. CI hardware design changes "increase immunity to interference."
16. CI software modifications "add the transmission of checksums to ensure data integrity."
17. "All alarm conditions were simulated and all output channels were tested... All tests were passed according to criteria that are equal or more stringent than the test criteria which were applied to the predicate device." |
    | Substantial Equivalence | Device is "as safe and effective, and performs as well as or better than the predicate device." | "Analysis and testing have shown that... the modified device is as safe and effective, and performs as well as or better than the predicate device." |

Study Proving Device Meets Acceptance Criteria

The study described is a non-clinical verification and validation study of the modified device, performed at the unit, integration, and system levels.

1. Sample size used for the test set and the data provenance: Not applicable in the context of an AI/ML device. For this traditional device, the "test set" consisted of various operational states and parameters of the ventilator itself. The data provenance refers to the simulated conditions and outputs generated within a controlled test environment.

   • Data Provenance: The tests involved simulating "all alarm conditions" and a "range of ventilator operating states." This indicates a controlled, artificial generation of conditions within a lab setting to test the device's responses.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. The "ground truth" for a mechanical/electronic device like a ventilator is its designed functionality and expected operational responses per engineering specifications, alarm thresholds, and data transmission protocols. These are established by engineering design and regulatory standards, not by expert consensus in the diagnostic sense.

3. Adjudication method for the test set: Not applicable. The testing described is objective and based on comparison of actual device outputs/behavior against predefined engineering specifications and the predicate device's performance.

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: Not applicable. This is a traditional medical device, not an AI-assisted diagnostic tool.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. The device is a ventilator, a physical system interacting with a patient, with software components. "Standalone performance" in the AI sense is not relevant. The "standalone" performance here refers to the device's inherent functionality without external systems, which was tested.

6. The type of ground truth used:

   • Engineering Specifications/Design Requirements: The primary ground truth was the device's design specifications for functionality (e.g., alarm logic, data transmission), safety (e.g., no reduction in patient safety), and performance (e.g., reliability, immunity to interference, data integrity via checksums).
   • Predicate Device Performance: The predicate device served as a baseline for "as safe and effective, and performs as well as or better than." The criteria for passing new tests were "equal or more stringent than the test criteria which were applied to the predicate device."

7. The sample size for the training set: Not applicable. This is a traditional medical device, not an AI/ML device that requires a training set.

8. How the ground truth for the training set was established: Not applicable.

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The intended use of the Servo Screen 390 is to provide a central bedside location for the display of all parameter information from the ventilator in order to aid the clinician in quickly and accurately reviewing the operating state of the ventilator and the ventilatory status of the patient.

The Servo Screen 390 is a real time ventilatory monitor system that continuously reads and makes calculations from parameters measured or calculated by Servo Ventilators. It displays this information in a clear and logically organized manner. Parameters are listed in a numerical format and/or as waveforms and loops. All displayed parameters are trended in 3, 6, 12, or 24 hour intervals. The device consists of a small, compact computer unit suitable for use in the clinical environment designed specifically for integration with existing ventilators Servo Ventilator 300 and Servo Ventilator 900. The flat screen display unit measures 252 x 330 x 88 mm (not including control knob) and a flexible support arm. Weight including the support arm is 4.3 kg. When used with the Servo Ventilator 900, the Servo Computer Module 990 is required to interface the two devices. This interface mounts under the ventilator, adding 2.7 kg to the weight and 4.0 cm to the height.

This device (Servo Screen 390 and Servo Computer Module 990) is a ventilator monitoring system designed to display ventilatory data from existing Siemens Servo Ventilators. The provided text outlines a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices, rather than a detailed study proving performance against specific acceptance criteria for a new clinical claim. Therefore, much of the requested information (like sample size, ground truth, expert qualifications, MRMC study, training set details) is not applicable or not available in this type of submission.

Here's an analysis based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a quantitative, measurable format with specific thresholds. Instead, it focuses on demonstrating agreement with the ventilator display and meeting regulatory requirements.

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

• Sample Size: Not specified. The document states "ventilator operation is simulated over the operating range of key ventilator parameters." This suggests a functional testing approach without a defined "sample size" in the context of patient data.
• Data Provenance: Not applicable. The testing described is non-clinical, involving simulated ventilator operation, not patient data.

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

• Number of Experts: Not applicable. This was a non-clinical, engineering-focused validation.
• Qualifications of Experts: Not applicable.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not applicable. The testing involved comparing the device display to the ventilator display, which is a direct comparison rather than an adjudication process typically used with human interpretation.

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:

• MRMC Study: No, an MRMC comparative effectiveness study was not done. The device is a display system, not an AI-powered diagnostic tool, and the submission is a 510(k) for substantial equivalence to existing ventilator monitors, not a clinical effectiveness trial for a new therapeutic or diagnostic claim.
• Effect Size: Not applicable.

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

• Standalone Performance: Yes, in an implicit sense. The "tests demonstrated that the data displayed by the servo screen is in agreement with the ventilator display" would represent a standalone functional test of the device's ability to accurately retrieve and display data from the ventilator without human intervention influencing the display accuracy itself.

7. The Type of Ground Truth Used:

• Ground Truth Type: The "ground truth" for the non-clinical tests was the direct output/display of the connected ventilator during simulated operation. The Servo Screen's display was compared directly against what the primary ventilator was reporting/displaying.

8. The Sample Size for the Training Set:

• Sample Size for Training Set: Not applicable. The device is a display system, not an AI/ML algorithm that requires a "training set."

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

• Ground Truth for Training Set Establishment: Not applicable.

In summary, the provided document is a 510(k) summary for a ventilator monitoring system. The "study" described is a non-clinical validation focused on demonstrating functional accuracy of data display and electromagnetic compatibility against established standards and the output of the source ventilator. It does not involve patient data, expert interpretations, or AI/ML components requiring training sets or clinical effectiveness studies in the manner typically associated with diagnostic AI tools. The "acceptance criteria" are inferred from the demonstrated agreement with ventilator displays and compliance with regulatory standards.

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