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The NeoNatalie Resuscitator is a self-inflating, manual resuscitator intended for newborns and infants up to 5 kg body mass who require respiratory support.

The NeoNatalie Resuscitator is a self-inflating, manual resuscitator intended for newborns and infants up to 5 kg body mass who require respiratory support. A manual resuscitator is a resuscitation device in which ventilation of the lungs is produced by the operator compressing the compressible unit of the device, the ventilation bag. The ventilation bag is self-inflating: the compressed bag will refill with ambient air via the bag inlet valve. The resuscitator provides positive pressure ventilation of the lungs (when used with a face mask). The resuscitator can be used to provide supplemental oxygen when used with the oxygen kit, the NeoNatalie Resuscitator Oxygen Kit. When a resuscitator fitted with an oxygen reservoir is used to provide supplemental oxygen, the ventilation bag will refill with oxygen from the oxygen reservoir. The NeoNatalie Resuscitator is made of polysulfone, silicone rubber and stainless steel. The NeoNatalie Resuscitator is reusable resuscitator which may be sterilized by autoclaving.

The provided text describes the regulatory clearance for the NeoNatalie Resuscitator and includes a comparison to a predicate device, as well as general information about testing performed. However, it does not contain the detailed clinical study information typically provided when discussing "acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of an AI/ML device.

The NeoNatalie Resuscitator is a mechanical medical device (manual resuscitator), not an AI/ML device, and therefore the specific questions regarding test sets, training sets, ground truth, expert adjudication, and MRMC studies are not directly applicable in the way they would be for an AI/ML diagnostic or predictive tool.

Based on the provided text, here's an attempt to answer the questions, highlighting where the information is not directly available or applicable:

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

The document frames the "acceptance criteria" and "device performance" in terms of compliance with international standards and functional requirements for a manual resuscitator, rather than traditional clinical performance metrics like sensitivity or specificity for an AI algorithm.

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The CPRmeter is used as a guide in administering cardiopulmonary resuscitation (CPR) to a suspected sudden cardiac arrest (SCA) victim at least 8 years old.

The CPRmeter™ device is small, lightweight (approximately the size and weight of a cell phone), and powered by a replaceable battery. The CPRmeter device is intended for use by responders who have been trained in CPR and use of the CPRmeter device. If in doubt about the appropriateness for use or ability to use. CPR is to be performed without using the CPRmeter device.

When attached to the bare chest of a suspected SCA victim, the CPRmeter device provides real-time feedback on CPR compressions in accordance with current CPR guidelines. It displays CPR feedback indicators for depth, rate, and release of chest compressions. It also counts the number of compressions in a series and provides notification of lack of expected CPR activity.

The provided text focuses on the device description, indications for use, predicate devices, and general testing criteria for substantial equivalence of the CPRmeter™ CPR Feedback Device. However, it does not contain specific acceptance criteria or detailed study information in the format required for the requested output.

Specifically, the document mentions:

• "Design verification and design validation testing demonstrates that the CPRmeter device meets its functional requirements and performance specifications."
• "Compression depth detection and accuracy have been verified under a wide variety of use or pre-use conditions (e.g., drop testing, temperature and humidity stress, after repeated cleaning)."

But it does not provide:

• A table of specific acceptance criteria (e.g., "accuracy > 90% for depth measurement") and reported performance values.
• Sample sizes for test sets, data provenance, number or qualifications of experts, or adjudication methods for ground truth.
• Information about MRMC studies, standalone algorithm performance, or how ground truth was established for training/test sets.

Therefore,Based on the provided text, I cannot complete the requested information, which requires specific acceptance criteria and detailed study parameters. The document states that the device's "functional requirements and performance specifications" and "compression depth detection and accuracy have been verified," but it does not specify what those requirements or accuracy thresholds are, nor does it describe the studies (e.g., sample sizes, ground truth methods, expert involvement) used to verify them.

Missing Information:

1. Table of acceptance criteria and reported device performance: The document only states that the device "meets its functional requirements and performance specifications" and that "compression depth detection and accuracy have been verified." It does not provide specific numerical criteria or reported performance values.
2. Sample size used for the test set and data provenance: Not mentioned.
3. Number of experts used to establish the ground truth for the test set and their qualifications: Not mentioned.
4. Adjudication method for the test set: Not mentioned.
5. Multi-reader multi-case (MRMC) comparative effectiveness study information: Not mentioned. The device is a feedback device for CPR, not an AI diagnostic tool, so an MRMC study in the typical sense wouldn't apply here.
6. Standalone (algorithm-only) performance: Not mentioned.
7. Type of ground truth used: Not explicitly stated how "compression depth detection and accuracy" were verified, so the type of ground truth is unclear (e.g., mechanical test rig, expert measurement).
8. Sample size for the training set: Not mentioned.
9. How the ground truth for the training set was established: Not mentioned.

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Indications for use are identical for the predicate products cited, and also more than 20 others sold in the US market, i.e., to remove obstructing secretions, blood or vomits from a patient's airway, to keep air passages to lungs open to allow ventilation.

The 1999 device (LSU4000) is a new more modern, unique solid design witch added benefits of more user-friendly controls, a batery level indicator, and a built in AC adapter. LSU performance is virtually the same as for its predicate designs and configurations.

Components: The subject LSU consists of a high-speed piston pump powered directly by an electric motor, an internal 12 V sealed lead-acid rechargeable battery, an electronic circuit board with controls and signalling light-emitting diodes. All components are assembled into a custom plastic cabinet. The 12 V battery and 8,4 V motor make it possible to both operate and charge the unit from any 12 - 28 VDC, 4 Amp source. Charging may also take place utilizing the internal 110-230 VAC power adapter.

Here's an analysis of the provided text to fulfill your request, broken down by each point. It's important to note that this document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a full clinical study with detailed performance metrics. As such, some of your requested information (especially on MRMC studies, specific expert qualifications, and detailed ground truth generation for training data) will not be present.

Acceptance Criteria and Device Performance Study

The Laerdal Suction Unit (LSU4000) aims to be substantially equivalent to its predicate devices concerning construction, indications for use, operating characteristics, and performance. The document outlines key performance criteria based on industry standards.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify a "test set" in the context of a dataset for an AI/ML device. Instead, it refers to internal product testing against industry standards. Therefore:

• Sample Size for Test Set: Not applicable in the context of a dataset. Tests are performed on units of the device itself. The number of physical units tested is not specified.
• Data Provenance: Not applicable in the context of a dataset. The testing is described as internal validation against industry standards.

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

Not applicable. This is not an AI/ML device that relies on expert-labeled data for "ground truth" establishment in a dataset. The "ground truth" for performance is defined by established industry standards and specifications (e.g., ISO, IEC, ASTM, Federal Specifications).

4. Adjudication Method for the Test Set

Not applicable. There is no expert review or adjudication process described for the performance testing. The product's compliance is assessed against predefined numerical and functional standards.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No. This document describes a medical device (portable suction unit), not an AI/ML-based diagnostic or assistive technology that would typically undergo an MRMC study.

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

No. This is a physical medical device. The "performance" is inherently standalone, as it refers to the device's functional capabilities (vacuum, airflow, battery life) independent of human assistance for its operation.

7. The Type of Ground Truth Used

The "ground truth" for the device's performance is established by industry standard protocols and performance criteria for medical suction equipment. These include:

• ISO 10079-1: Medical Suction Equipment - Part 1 Electrically powered suction...
• IEC 601-1: Medical Electrical Equipment – Part 1 Gen. requirements for safety
• General Services Administration KKK-A-1822C, Federal Specification for Ambulance, para. 3.12.4
• ASTM F-29 Standard F 960, Medical and Surgical Suction and Drainage Systems, paras 5.8.1 and 5.8.2

The device claims to meet these criteria, implying adherence to their specified requirements for vacuum, airflow, and battery operation time.

8. The Sample Size for the Training Set

Not applicable. This is not an AI/ML device that requires a "training set" of data. The device's design is based on engineering principles and improvements over predicate devices, not machine learning from a dataset.

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

Not applicable, as there is no "training set." The design and engineering process for this device would rely on established scientific principles, previous device designs, user feedback, and industrial standards.

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