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The Neptune S Waste Management System is indicated for use in procedures where collecting and disposing of fluid waste and capturing suctioned tissue specimens is desired. It is intended to be used in operating rooms, pathology, surgical centers, procedure rooms, and for endoscopic procedures, such as colonoscopies, esophagogastroduodenoscopies (EGDs), and bronchoscopies.

The Neptune S Waste Management System includes subject devices Neptune S Rover and V2 Manifolds, previously 510k cleared Neptune 2 Docker, and supporting accessories.

Neptune S Rover is a mobile device, plugged into a standard 15A hospital-grade AC power outlet and is used to suction and collect waste fluids during surgical or nonsurgical procedures. It can also be used to collect tissue specimens suctioned during these procedures. The Rover provides fluid waste management, fluid volume measurement and contains features designed to specifically aid with tissue specimen collection, including an array of lighting features and a flat worksurface. It was designed with a smaller footprint to accommodate smaller operating rooms or procedure rooms, particularly endoscopy suites.

The manifolds are non-sterile, single-use disposables, that act as an interface between the Rover canister and standard medical grade suction tubing. They facilitate the collection of surgical tissue and the removal of fluid waste.

The provided text describes the non-clinical and clinical tests performed for the Neptune S Waste Management System to establish its safety and effectiveness.

Here's a breakdown of the requested information:

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

The document does not explicitly present a table of acceptance criteria for specific performance metrics with corresponding reported device performance values for non-clinical tests. Instead, it generally states that "All requirements were met and no new issues of safety or effectiveness were raised" for various non-clinical test items like intended use/user needs, safety, general requirements and performance, software, electrical/mechanical/thermal safety, electromagnetic compatibility, and shipping.

However, for the clinical test (PMCF survey), specific performance rates are provided:

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

• Sample size for the clinical test set: Not explicitly stated as a number of patients or procedures. It refers to "retrospective clinical data collected through Post Market Clinical Follow-up (PMCF) survey of predicate device." Without further detail, the exact sample size cannot be determined from this document.
• Data provenance:
  • Country of origin: Not specified.
  • Retrospective or prospective: Retrospective ("retrospective clinical data collected through Post Market Clinical Follow-up (PMCF) survey").

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

This information is not provided in the document. The clinical data used was a PMCF survey of a predicate device, which implies real-world usage rather than a controlled study with expert ground truth establishment for a test set in the context of AI/ML performance evaluation.

4. Adjudication method for the test set

This information is not applicable as the clinical data was a PMCF survey, not a study involving human readers and AI for ground truth 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

There is no indication that an MRMC comparative effectiveness study was performed or that the device involves AI assistance for human readers. The device is a waste management system, not an imaging or diagnostic AI tool.

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

This is not applicable as the device is a physical medical device (waste management system) and not an algorithm or AI product.

7. The type of ground truth used

For the non-clinical tests, the ground truth was based on design specifications, risk analysis, and conformance to standards (e.g., IEC 60601-1, IEC 62304).

For the clinical data (PMCF survey), the "ground truth" was based on observed operational performance of the predicate device in real-world clinical procedures, specifically looking at waste fluid and debris removal success rates and the occurrence of adverse events. It is outcome-based, related to the device's functional performance in a clinical setting.

8. The sample size for the training set

This is not applicable. The device is a physical medical device, not an AI/ML model that undergoes training with a dataset.

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

This is not applicable as there is no training set for an AI/ML model mentioned.

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The device is designed to perform radiographic x-ray examinations on pediatric and adult patient treatment areas.

The Rover product concept was developed under a contract from the Australian Department of Defense to fulfil a need for a full performance digital medical x-ray imager, light enough to be used in deployed medical facilities. Key Design Features: Full trauma imaging capability 40-110kV, 0.2-20mAs; Ultra-light weight at 105 kg; Ground Clearance allows for 75mm step up; Operation on uneven ground; Spare battery tray swap out in under a minute; The unit uses FDA cleared digital image capture panels and software made by FujiFilm OR Varex.

The provided document is a 510(k) summary for a mobile x-ray system (ROVER) and does not describe acceptance criteria for an AI/ML device or detailed studies proving such a device meets those criteria. The document focuses on establishing substantial equivalence for a hardware medical device to previously cleared devices.

Therefore, many of the requested items (e.g., sample size for test set, data provenance, number of experts, adjudication method, MRMC comparative effectiveness, ground truth type, training set size and ground truth establishment methods) are not applicable or cannot be extracted from this document as it pertains to an X-ray system, not an AI/ML diagnostic aid.

Here's the information that can be extracted or inferred:

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

The document does not specify quantitative acceptance criteria in terms of diagnostic performance metrics for an AI/ML device. Instead, it relies on regulatory standards and the equivalence to predicate devices. The "reported device performance" is essentially that it operates properly and produces diagnostic quality images.

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. The document states "Clinical testing was not required to establish substantial equivalence because all digital x-ray receptor panels have had previous FDA clearance." The testing described is bench testing and verification of system operation, not a clinical study with a test set 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 no clinical test set requiring expert ground truth was used.

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

Not applicable.

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

Not applicable. This device is an X-ray system, not an AI diagnostic aid requiring MRMC studies to assess reader improvement.

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

Not applicable. This is an X-ray system, not an AI algorithm.

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

Not applicable, as no clinical test set requiring ground truth was used. The focus was on engineering verification and compliance with standards.

8. The sample size for the training set

Not applicable. This is not an AI/ML device, so there is no training set mentioned.

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

Not applicable.

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The device is designed to perform radiographic x-ray examinations on pediatric and adult patients treatment areas.

The Rover product concept was developed under a contract from the Australian Department of Defense to fulfil a need for a full performance digital medical x-ray imager, light enough to be used in deployed medical facilities. Key Design Features:

• Full trauma imaging capability 40-110kV, 0.2-20mAs;
• Ultra-light weight at 105 kg;
• Ground Clearance allows for 75mm step up;
• Operation on uneven ground;
• Spare battery tray swap out in under a minute;
  The unit uses FDA cleared digital image capture panels and software made by FujiFilm.

The provided document is a 510(k) Premarket Notification for the "Rover" mobile x-ray system. It details the device's technical specifications and compares it to a legally marketed predicate device (DRX-Revolution Nano Mobile X-ray System). The acceptance criteria and testing described are focused on demonstrating substantial equivalence to an existing device, rather than proving performance against specific acceptance criteria for an AI/ML-based device.

Therefore, the document does not contain the information requested regarding acceptance criteria related to AI/ML device performance, ground truth establishment, expert adjudication, MRMC studies, or standalone algorithm performance.

Here's why the document doesn't provide the requested information and what it does provide:

• Device Type: The Rover is a mobile x-ray system, a physical medical device for capturing x-ray images. It uses FDA-cleared digital image capture panels and software (specifically, Fujifilm and Fuji FDX Console Advance DR-ID 300CL Software) which are themselves "previously cleared." This submission is about the system integrating these components, not about a novel AI/ML algorithm for image analysis or diagnosis.
• Basis for Clearance: The basis for clearance is "substantial equivalence" to a predicate device, focusing on functional, technical, and safety equivalence of the hardware and integrated pre-cleared software.
• Testing: The testing detailed is primarily non-clinical bench testing to confirm proper system operation and safety standards compliance (e.g., IEC standards, radiation performance, cybersecurity, wireless technology).
• Clinical Testing: The document explicitly states: "Clinical testing was not required to establish substantial equivalence because all digital x-ray receptor panels have had previous FDA clearance." This means no new clinical data (and thus no associated ground truth, expert reads, or AI performance metrics) was generated for this specific 510(k) submission.

Summary of what is present in the document that somewhat relates to the request, but not in the context of AI/ML acceptance criteria:

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

   • Acceptance Criteria (Implicit): Substantial equivalence to the predicate device in terms of indications for use, configuration, generator specifications, panel interfaces, and meeting US performance standards (21 CFR 1020.30 and 21 CFR 1020.31). Also, compliance with various IEC standards (60601-1, 60601-1-2, 60601-1-3, 60601-1-6, 60601-2-28, 60601-2-54).
   • Reported Device Performance: The "Substantial Equivalence Chart" (page 5) compares the Rover to the predicate, showing "SAME" or "Equivalent" for most characteristics. "Bench testing indicate that the new devices are as safe and effective as the predicate devices. Proper system operation is fully verified upon installation. We verified that the modified combination of components worked properly and produced diagnostic quality images as good as our predicate generator/panel combination." (page 6)

2. Sample sized used for the test set and the data provenance: Not applicable in the context of AI/ML performance testing. Testing was system-level functional and safety verification.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for image interpretation was not established as part of this submission. The software components were previously cleared.

4. Adjudication method for the test set: Not applicable.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done: No, an MRMC study was not done.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No, as this is a mobile x-ray system, not a standalone AI algorithm. The imaging software used is a pre-cleared component.

7. The type of ground truth used: Not applicable for AI/ML performance. The "ground truth" for the device's acceptable performance was its compliance with safety standards and its ability to produce diagnostic quality images comparable to the predicate, as verified through bench testing.

8. The sample size for the training set: Not applicable. This device is not an AI/ML algorithm that requires a training set.

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

In conclusion, the document describes the clearance of a medical device (a mobile X-ray system) based on substantial equivalence to a predicate, not the performance claims of a novel AI/ML-based diagnostic or analytical tool. Therefore, it does not provide the specific information requested about acceptance criteria and studies for AI/ML device performance.

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The Theradyne Rover Express™ Powered Wheelchair is intended for medical purposes to provide mobility to persons restricted to a sitting position.

The Theradyne Rover Express™ Powered Wheelchair.

The provided text describes a 510(k) summary for the "Theradyne Rover Express™ Powered Wheelchair." This document focuses on establishing substantial equivalence to a predicate device rather than detailing specific clinical studies for performance metrics. As a result, much of the requested information regarding acceptance criteria, study design, and ground truth for an AI/device performance evaluation is not present in the provided text.

Here's an analysis based on the given document:

Acceptance Criteria and Reported Device Performance

The document states that the Rover Express™ Powered Wheelchair was examined and tested according to FDA's Guidance Documents, with successful results that "did not raise any issues of safety and effectiveness of the device." However, specific numerical acceptance criteria (e.g., minimum torque, battery life, turning radius) and detailed performance metrics are not explicitly reported in the format of a table within the provided text.

The conclusion reiterates that the device is "substantially equivalent to previously marketed powered wheelchairs as demonstrated by its conformance to FDA recognized consensus standards and FDA's Guidance Documents." This implies that the 'acceptance criteria' were adherence to these standards and guidance documents, which the device met.

Study Information (Based on a traditional medical device, not an AI/diagnostic device)

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

• The document does not describe a clinical "test set" in the context of an AI or diagnostic study. The testing performed was "functional & safety testing" of the physical wheelchair device.
• The sample size for this functional and safety testing is not specified.
• Data provenance is not applicable in the context of clinical data for an AI algorithm. The testing was of the manufactured device itself.

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 as the document describes a physical medical device (powered wheelchair) and its compliance testing, not a diagnostic or AI device that requires expert-established ground truth.

4. Adjudication method for the test set:

• This is not applicable for the type of device and testing 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:

• This is not applicable. The device is a powered wheelchair; it does not involve human readers or AI assistance in a diagnostic context.

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

• This is not applicable. The device is a powered wheelchair; it is not an algorithm.

7. The type of ground truth used:

• For a physical device like a powered wheelchair, "ground truth" would relate to its physical specifications and performance against engineering standards. The document implicitly states that the 'ground truth' was established by "FDA recognized consensus standards and FDA's Guidance Documents."

8. The sample size for the training set:

• This is not applicable. There is no mention of a training set for an AI algorithm as this is a physical medical device.

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

• This is not applicable as there is no training set for an AI algorithm.

Summary of Missing Information:

The provided 510(k) summary is for a physical medical device (Theradyne Rover Express™ Powered Wheelchair) and focuses on demonstrating substantial equivalence through compliance with recognized standards and guidance documents. It does not contain any information related to AI performance, clinical study results with human readers, ground truth for diagnostic classifications, or typical metrics associated with AI/diagnostic device evaluation. Therefore, most of the specific questions asked regarding AI evaluation criteria cannot be answered from this document.

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