Search Results

The VEO Multigas Monitor for Pocket PC, model 400601 is intended to provide monitoring of CO2, N2O, anesthetic agents, anesthetic agent identification and respiratory rate during anesthesia, recovery and respiratory care. It may be used in the operating suite, intensive care unit, patient room and emergency medicine settings for adult, pediatric and infant patients.

The VEO Multigas Monitor for Pocket PC, model 400601 is a miniature mainstream infrared gas analysis bench. The complete multigas analyzer is contained within a transducer that is attached to the breathing circuit via an airway adapter.

The provided text is a 510(k) summary for the VEO Multigas Monitor for Pocket PC, model 400601. It describes the device, its intended use, and its substantial equivalence to predicate devices, but it does not contain the detailed information required to answer all your questions about acceptance criteria and the specific study proving the device meets those criteria.

Here's what can be extracted and what information is missing:

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

Missing Information: Specific numerical acceptance criteria (e.g., accuracy, precision ranges for gas measurements, response time) and the detailed results demonstrating how the device met these criteria.

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

Missing Information: The document states "Testing in direct comparison to predicates throughout the operating range was conducted using calibrated gas samples and legally marketed anesthesia and ventilation devices," but it does not specify the sample size of these tests or the data provenance. It implies prospective testing in a controlled lab environment with calibrated gases and existing devices.

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)

Missing Information: This document does not describe the use of human experts to establish ground truth for the device's performance. The ground truth for gas measurement devices typically comes from reference standards (calibrated gas samples) and other validated measurement instruments, not human expert interpretation in the way it would for imaging diagnostics.

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

Missing Information: Not applicable as the testing involves comparison to calibrated references and predicate devices, not subjective expert assessment 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

Missing Information: This is not an AI-assisted diagnostic device, but a multigas monitor. Therefore, an MRMC study or assessment of human reader improvement with AI assistance is not relevant or described.

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

The device is a "Multigas Monitor," implying standalone measurement. The testing described ("Testing in direct comparison to predicates throughout the operating range was conducted using calibrated gas samples and legally marketed anesthesia and ventilation devices") indicates a standalone performance evaluation against quantifiable standards. The device itself is not an "algorithm" in the typical sense of AI, but rather hardware and software that measures gas concentrations.

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

The ground truth for this type of device would likely be:

• Calibrated gas samples: Known concentrations of CO2, N2O, and anesthetic agents used as a reference.
• Measurements from legally marketed anesthesia and ventilation devices (predicates): These would serve as established, validated reference values for comparison.

Missing Information: While implied, the document doesn't explicitly state the exact "type" of ground truth used beyond "calibrated gas samples."

8. The sample size for the training set

Missing Information: As this document does not describe a machine learning or AI algorithm in the context of a "training set," this information is not provided and likely not applicable in the way you're conceptualizing it for an AI device. The device's underlying technology relies on established infrared gas analysis principles and sensor calibration, not a data-driven training process in the AI sense.

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

Missing Information: Not applicable for the reasons stated in point 8.

Ask a specific question about this device

The VEO Multigas Monitor for Pocket PC is intended to provide monitoring of carbon dioxide and oxygen during anesthesia, recovery and respiratory care. It may be used in the operating suite, intensive care unit, patient room and emergency medicine settings for adult and pediatric patients.

The VEO Multigas Monitor for Pocket PC combines a miniature mainstream infrared gas analysis bench with an ultra-fast response oxygen fuel cell. The complete multigas analyzer is contained within a transducer that is attached to the breathing circuit via an airway adapter.

Here's an analysis of the provided text to extract the acceptance criteria and study information for the K051857 device, the VEO Multigas Monitor for Pocket PC:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state specific quantitative acceptance criteria (e.g., "accuracy must be within ±X%"). Instead, it broadly compares the device's performance to its predicate devices in several key areas. The acceptance criteria are implicitly defined as being "equivalent or superior" to the predicate devices.

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

• Sample Size for Test Set: Not specified. The document states "Non-clinical testing in direct comparison to predicates throughout the operating range was conducted." It doesn't provide specific numbers of tests or gas samples used.
• Data Provenance: Not specified regarding country of origin or whether it was retrospective or prospective. It was a "non-clinical" study, implying laboratory testing rather than patient data.

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

• Number of Experts: Not applicable. The ground truth for the non-clinical testing was established using "calibrated gas samples and legally marketed anesthesia and ventilation devices," not human experts.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. Since the testing involved calibrated gas samples and established devices, there would be no need for expert adjudication.

5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study

• MRMC Study Done: No. The study was a non-clinical comparison against predicate devices using calibrated gas samples and other medical equipment, not a human-reader study. Therefore, there's no reported effect size regarding human reader improvement with or without AI assistance.

6. Standalone Performance Study (i.e., algorithm only without human-in-the-loop performance)

• Standalone Study Done: Yes. The described "Non-clinical testing in direct comparison to predicates throughout the operating range" is a standalone performance study. The device's performance (response time, accuracy, precision, reliability) was directly measured and compared against established standards (calibrated gas samples) and predicate devices.

7. Type of Ground Truth Used

• Type of Ground Truth: "Calibrated gas samples and legally marketed anesthesia and ventilation devices." This means the ground truth was based on precisely known concentrations of gases and the output from reference medical equipment.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This device is a hardware multigas monitor, not an AI/ML algorithm that requires a training set in the conventional sense. Its "training" would involve calibration and engineering design, not data-driven machine learning.

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

• Ground Truth for Training Set: Not applicable, as it's not an AI/ML device that uses a "training set" in the context of data. The device's accuracy and performance are established through calibration and engineering validation using known physical standards.

Ask a specific question about this device