Search Results

The A8, A9 Anesthesia System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The A8, A9 is intended for use by licensed clinicians in the administration of general anesthesia, for patients requiring anesthesia within a health care facility, and can be used in adult, pediatric and neonate populations.

High Flow Nasal Cannula (HFNC) is indicated for delivery of nasal high flow oxygen to spontaneously breathing adult patients. It can be used for pre-oxygenation and short-term supplemental oxygenation (up to 10 minutes) during intubation in operating rooms. It is not intended for apneic ventilation. HFNC is indicated for use in adults only.

The A8, A9 Anesthesia System is a continuous flow inhalation gas anesthesia system that delivers anesthetic vapor and provides for automatic and manual modes of ventilation. The A8, A9 Anesthesia System incorporates O2, CO2, N2O and Agent concentration monitoring (Desflurane, Isoflurane, Halothane, and Sevoflurane). The A8, A9 Anesthesia System is a modified version the previously cleared Mindray A7 Anesthesia System cleared in K171292.

The provided text describes the 510(k) premarket notification for the Mindray A8, A9 Anesthesia System, focusing on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria based on studies involving human readers or AI performance metrics.

Therefore, most of the information requested in your prompt (acceptance criteria table with performance, sample size for test set, data provenance, number of experts for ground truth, adjudication method, MRMC study, standalone performance, training set size, and ground truth establishment for training set) is not available in this document.

The document details engineering tests and conformance to standards, which are different from clinical performance studies for AI/radiology devices.

Here's a breakdown of what is available and what is not:

Information Found in the Document:

• Device Name: A8, A9 Anesthesia System
• Predicate Devices: K171292 (A7 Anesthesia System), K192972 (BeneVision N Series Patient Monitors). Reference devices also listed.
• Technological Differences from Predicate:
  • Change the Vaporizer Type and the addition of Electronic Vaporizers (A9)
  • Change certain parameters of the ventilator modes
  • Addition of the High Flow Nasal Cannula Oxygen (HFNC)
  • Change the Anesthetic Gas Module and Accessories
  • Addition of the Sealed Lead Acid Battery
• Performance Data (Type of Studies Conducted):
  • Functional and System Level Testing (bench testing) to validate performance and ensure specifications are met.
  • Biocompatibility Testing (conformance to ISO standards: 10993-1, -5, -10, -18, 18562-1, -2, -3)
  • Software Verification and Validation Testing (following FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices")
  • Electromagnetic Compatibility and Electrical Safety (conformance to IEC and ANSI/AAMI standards: ES60601-1, IEC 60601-1-6, -1-8, ISO 80601-2-13, -2-55, IEC 60601-1-2)
  • Bench Testing (conformance to ASTM and ISO standards: F1101-90, IEC 60601-1-6, -1-8, ISO 5360, 10079-3, 80601-2-13, -2-55)

Information NOT Found in the Document (and why):

This document is for an Anesthesia System, which is a hardware medical device with integrated software for control and monitoring. It is not an AI-driven image analysis or diagnostic device that would typically involve acceptance criteria related to human reader performance, expert ground truth, or MRMC studies. The "performance data" section focuses on testing the device's functional specifications, safety, and compliance with general medical device standards.

1. A table of acceptance criteria and the reported device performance: Not provided in the format of performance metrics against specific acceptance thresholds for diagnostic accuracy, sensitivity, specificity, etc. The document generally states that "the devices continue to meet specifications and the performance of the device is equivalent to the predicate" based on functional and system-level testing, and compliance with standards. Key technical characteristics are compared in a large table, but this is a comparison to the predicate, not a list of acceptance criteria with measured performance against them.
2. Sample sized used for the test set and the data provenance: Not applicable in the context of this type of device submission. The "test set" here refers to the actual physical devices undergoing bench and functional testing, not a dataset of patient images or clinical cases.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth in this context would be engineering specifications and validated measurement techniques, not expert clinical interpretation.
4. Adjudication method: Not applicable.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No. This type of study is for evaluating diagnostic performance, typically for imaging devices or AI algorithms assisting human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This device is an anesthesia system, not a standalone AI algorithm for diagnosis.
7. The type of ground truth used: For this device, ground truth is established by engineering design specifications, international and national consensus standards (e.g., ISO, IEC, ASTM), and validated measurement instruments.
8. The sample size for the training set: Not applicable for this type of device. There is no "training set" in the machine learning sense described. Software validation ensures the embedded software performs as designed and specified for controlling the anesthesia system.
9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document describes a regulatory submission for an anesthesia system, which relies on demonstrating safety and efficacy through engineering testing and adherence to established performance standards for medical devices, rather than AI model validation studies common for diagnostic algorithms.

Ask a specific question about this device

The A5 Anesthesia System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The A5 is intended for use by licensed clinicians, for patients requiring anesthesia within a health care facility, and can be used in adult and pediatric (including neonate, infant, child and adolescent) populations.

The A5 Anesthesia System is a continuous flow inhalation gas anesthesia system that delivers anesthetic vapor and provides for automatic and manual modes of ventilation. The A5 incorporates 02, CO2, N2O and Agent concentration monitoring (Desflurane, Isoflurane, Enflurane, Sevoflurane and Halothane).

This document is a 510(k) summary for the A5 Anesthesia System. It describes the device, its intended use, and compares it to a predicate device and several reference devices to demonstrate substantial equivalence.

Here's a breakdown of the requested information based on the provided text:

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

The document does not explicitly present acceptance criteria in a table format with specific quantitative thresholds. Instead, it lists various "Nonclinical testing and Performance" areas that were tested and states that "The functional and system level testing showed that the device continues to meet specifications and the performance of the device is equivalent to the predicate."

However, the "Device Comparison Table" on pages 7 and 8 provides a comparison of technical characteristics between the subject device (A5 Anesthesia System) and the predicate device (A5 Anesthesia Delivery System (K123211)). This table implies that the performance of the subject device in these listed characteristics meets or is equivalent to the predicate device, which can be interpreted as the acceptance criteria being parity with the predicate.

Here's an attempt to construct a table based on the provided information, interpreting the predicate device's specifications as the acceptance criteria for the subject device:

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

The document lists "Nonclinical testing and Performance" areas like:

• Software (Unit testing, Integration testing, System testing)
• Performance
• Power Supply
• Thermal
• Cleaning and Disinfection
• Fresh Flow Optimizer
• AG Module
• Heating Module
• Waveform Comparison
• Biocompatibility (Volatile Organic Compounds, Particulate Testing, Cytotoxicity, Sensitization, Irritation / intracutaneous reactivity, Extractables and leachables (E&L) testing, Inorganic gases testing)
• Human Factors Validation Testing
• Testing as per consensus standards (AAMI/ANSI ES60601-1, IEC 60601-1-2, ISO 80601-2-13, ISO 80601-2-55, ASTM F1101-90, AIM 7351731)

However, the document does not specify:

• The sample size used for any of these tests.
• The data provenance (e.g., country of origin, retrospective or prospective nature of the data if any clinical data was implied).

The testing appears to be mostly focused on hardware and software functionality, performance, and safety against specified standards, rather than clinical trial data involving patient samples or expert interpretations.

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 provided in the document. The testing described is primarily non-clinical (engineering, software, biocompatibility, standards compliance). There is no mention of "ground truth" being established by experts in the context of clinical interpretation, as might be found in studies for diagnostic AI devices.

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

This information is not provided in the document. Given the nature of the tests (non-clinical performance, safety, and standards compliance), an adjudication method as typically used for clinical endpoints or image interpretation is not applicable or described.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not conducted or described. This type of study is relevant for AI-powered diagnostic or decision support tools that assist human readers, which is not the primary function of this anesthesia system.

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

The device is an "Anesthesia System" which delivers anesthetic vapor and provides ventilation. It includes an "Anesthetic Gas Module" for monitoring, but it is not an "algorithm only" device operating without human-in-the-loop. Its performance, as described by the parameters in the comparison table, would be standalone in terms of its ability to measure and deliver gases accurately, but it functions as part of a system used by clinicians. There is no mention of a standalone algorithm's performance in isolation from the hardware components.

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

For the non-clinical tests (software, performance, electrical safety, biocompatibility, etc.), the "ground truth" would be defined by engineering specifications, international consensus standards, and predicate device performance. For example, the accuracy of gas measurement (e.g., CO2 accuracy 0 to 1%: +/-.1%) would be tested against calibrated references, where the reference measurement itself serves as the ground truth. There is no mention of clinical ground truth types like pathology or outcomes data.

8. The sample size for the training set

This information is not applicable and not provided. The A5 Anesthesia System is a traditional medical device (hardware and software for life support functions and monitoring), not an AI/Machine Learning model that requires a "training set" in the computational sense. The software testing mentioned (Unit, Integration, System) refers to traditional software development and verification, not machine learning model training.

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

This information is not applicable and not provided for the same reasons as point 8.

Ask a specific question about this device

The A7 Anesthesia System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The A7 is intended for use by licensed clinicians, for patients requiring anesthesia within a health care facility, and can be used in adult and pediatric (including neonate, infant, child and adolescent) populations.

The A7 Anesthesia System is a continuous flow inhalation gas anesthesia system that delivers anesthetic vapor and provides for automatic and manual modes of ventilation. The A7 incorporates 02, CO2, N2O and Agent concentration monitoring (Desflurane, Isoflurane, Enflurane, Sevoflurane and Halothane).

This document refers to the Mindray A7 Anesthesia System (K171292). It outlines its indications for use, device description, and a comparison to a predicate device (K151954), along with reference devices, to demonstrate substantial equivalence.

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

Summary of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Mindray A7 Anesthesia System are implicitly derived from its comparison to a predicate device (also an A7 Anesthesia System, K151954) and several reference devices. The core principle is "substantial equivalence," meaning the new device performs at least as well as, or comparably to, the established devices.

The table below summarizes the key technical characteristics and their reported performance/specifications for both the subject device and its direct predicate. Since the document states "The functional and system level testing showed that the device continues to meet specifications and the performance of the device is equivalent to the predicate," the performance of the subject device is presented as matching the predicate.

Ask a specific question about this device

The HAMILTON-C3 ventilator is intended to provide positive pressure ventilatory support to adults, pediatrics, infants, and neonates.

Intended areas of use:

• In the intensive care ward or in the recovery room.
• During transfer of ventilated patients within the hospital.

The HAMILTON-C3 ventilator is a medical device intended for use by qualified, trained personnel under the direction of a physician and within the limits of its stated technical specifications.

The HAMILTON-C3 has been designed to ventilate adult and pediatric patients in the critical care environment. With optional support, the HAMILTON-C3 is also able to ventilate infants and neonates. The HAMILTON-C3 ventilator uses the same graphical user interface (GUI) used by the predicate HAMILTON-C2, which features a touchscreen "Ventilation Cockpit". This provides the exact information that the user needs and helps focus on what is important. In addition, the HAMILTON-C3 includes the ASV ventilation-mode, which automatically applies lung-protective strategies, reduces the risk of operator error, and promotes early weaning.
The HAMILTON-C3 has been designed with built-in batteries and a turbine thereby giving the user maximum independence and flexibility to accompany a patient everywhere. The HAMIL-TON-C3 offers the same ventilation modes as the HAMILTON-C2, which provides for both full and partial ventilatory support.

1. The HAMILTON-C3 offers all the conventional modes, as well as advanced invasive and non-invasive ventilation modes: ASV, (S)CMV+, SIMV+, PCV+, SPONT, APRV, DuoPAP, NIV, NIV-ST, nCPAP-PS, PSIMV+, and PSIMV+ with IntelliSync.
2. All 41 monitoring parameters can be trended over 1, 6, 12, 24, and 72 hours.
3. The ability to turn off the Apnea alarm in the nCPAP-PS mode.
4. The HAMILTON-C3 includes a 12.1" wide-screen monitor.

The Hamilton-C3 is a continuous ventilator. The provided text outlines its characteristics, intended use, and a comparison with a predicate device and other FDA-cleared ventilators. However, it does not include detailed acceptance criteria or a study that specifically "proves the device meets the acceptance criteria" in the way one might expect for a diagnostic or AI-driven device with performance metrics like sensitivity, specificity, or F1-score.

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving novel performance. The "acceptance criteria" discussed are primarily related to safety, effectiveness, and adherence to recognized standards, rather than specific performance metrics for a novel diagnostic function.

Here's a breakdown based on the information provided, and where information is not present:

1. Table of acceptance criteria and the reported device performance

Based on the document, "acceptance criteria" are inferred from the demonstration of substantial equivalence and adherence to recognized standards. The "reported device performance" is largely presented as meeting the same functional capabilities as the predicate device (HAMILTON-C2) and adhering to various medical device standards.

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 in the traditional sense for this submission type. This is a 510(k) submission based on substantial equivalence, not a clinical trial with a "test set" of patient data to evaluate a diagnostic algorithm's performance. The "testing" referred to is non-clinical verification and validation of the device's functionality and adherence to standards. There is no mention 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. As per point 2, there is no "test set" with ground truth in the context of diagnostic performance presented in this document.

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 document describes a continuous ventilator, not an AI-assisted diagnostic device. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant or mentioned.

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

• No. This is a medical device (ventilator), not a standalone algorithm. While it contains software, the evaluation in this document focuses on the integrated system's safety and effectiveness and its equivalence to a predicate device, not on the performance of a standalone algorithm or AI.

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

• Not Applicable. As per point 2, there is no "test set" requiring ground truth for diagnostic performance evaluation. The "ground truth" for the device's functionality is its design specifications and compliance with recognized standards.

8. The sample size for the training set

• Not Applicable. This document does not describe the development or evaluation of an AI/ML algorithm that would require a "training set" of data.

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

• Not Applicable. See point 8.

Ask a specific question about this device