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The Midas Flowmeter is intended for use as a continuous flow conscious sedation system to deliver a mixture of nitrous oxide and oxygen gases to a patient. When used with the electronic automatic vacuum switch (eAVS), the Midas Flowmeter is used to control the scavenging of waste analgesic gas.

The Midas Flowmeter is used within a healthcare environment during a nitrous oxide (N20)/oxygen (O2) conscious sedation system. The device is a digitally controlled, software driven, continuous flow device that precisely meters nitrous oxide and oxygen medical gases and delivers gas mixtures to a conscious, spontaneously breathing patient. The Midas Flowmeter uses electronic mechanisms to control the flowrate and mixture percentage of gases delivered to a patient. The optional eAVS is an accessory to the Midas Flowmeter. It is used within the nitrous oxide/oxygen conscious sedation system to allow removal of waste analgesic gases through a connected vacuum source. The eAVS connects the exhalation line of the patient's breathing circuit to vacuum tubing from the vacuum source and controls the vacuum flowrate (i.e., scavenging flowrate). The eAVS is specifically designed to work with the Midas Flowmeter and all functionality control of the eAVS has been integrated into the user interface of the Midas Flowmeter.

The provided document is a 510(k) Premarket Notification from the FDA, which focuses on demonstrating substantial equivalence of a new medical device to existing predicate devices. It primarily discusses comparisons of technical characteristics and compliance with recognized standards.

Crucially, this document does NOT contain explicit acceptance criteria tables or a detailed study report that proves the device meets specific performance metrics in a clinical or simulated environment with ground truth from experts, as would be expected for an AI/ML powered device.

The document states:

• "The characteristics of the Midas Flowmeter and optional eAVS do not require clinical investigation due to safety and efficacy being supported by non-clinical testing performed."
• "The verification and validation testing of the Midas Flowmeter and optional eAVS was found to be acceptable and supports the claims of substantial equivalence."

This indicates that the device's performance was evaluated through bench testing and compliance with recognized consensus standards, rather than a clinical study involving human readers or expert-established ground truth for a test set.

Therefore, I cannot provide the requested information about acceptance criteria, study details, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, or the type of ground truth used, because such a study (as described in the prompt) was not performed or detailed in this 510(k) submission.

The document focuses on:

• Comparison of Technical Characteristics: Showing the Midas Flowmeter and eAVS are similar in function, intended use, and technology to predicate devices (FlowStar Touch Digital Mixer Flowmeter and Nitronox Scavenger Plus).
• Compliance with Standards: Listing various IEC, ISO, AAMI, and ASTM standards that the device was tested against to demonstrate safety and performance (e.g., IEC 60601-1 for basic safety, ISO 11195 for gas mixers).
• Non-clinical Performance Data (Bench Testing): A bulleted list of "performance bench testing" categories like "Gas Flow Rate Adjustment," "Touchscreen Display," "Accuracies and Faults," and "Life Testing." However, it does not provide the specific acceptance criteria for these tests nor the detailed results in a quantitative manner as requested. It simply states that "The results of the testing demonstrated that the Midas Flowmeter and optional eAVS met all of the acceptance criteria for functional, operational, and performance characteristics..."

In summary, none of the specific elements required to answer your prompt (e.g., table of acceptance criteria with reported performance, sample sizes for test sets, expert ground truth establishment, MRMC studies, standalone performance, training set details) are present in this 510(k) document because the device is a hardware system (a gas mixer) and was cleared based on non-clinical bench testing and adherence to recognized standards, not clinical performance studies with AI/ML components involving human expert review or data-driven ground truth.

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The Nitronox® Scavenger Plus is intended to control the vacuum flowrate for scavenging of waste analgesic gas.

The Nitronox® Scavenger Plus (Scavenger Plus) is used within a nitrous oxide/oxygen conscious sedation system to allow removal of waste analgesic gases through a connected vacuum source. The device is used during a dental or medical procedure in a healthcare facility environment. The device is not intended to be used for general anesthesia. The Scavenger Plus is operated by a licensed healthcare professional during conscious sedation of a patient.

The Scavenger Plus connects the exhalation line of the patient's breathing circuit to vacuum tubing from the vacuum source and controls the vacuum flowrate (i.e., scavenging flowrate). The rate at which the gas is removed (i.e., scavenged) is specified by the Scavenger Plus, which uses a control knob and reservoir bag to limit the amount of vacuum applied to the exhalation line of the breathing circuit.

The document provided is a 510(k) Premarket Notification from the FDA for a medical device called the "Nitronox® Scavenger Plus." This type of submission aims to demonstrate that a new device is as safe and effective as a legally marketed predicate device, rather than proving its absolute safety and efficacy through extensive clinical trials.

The document discusses non-clinical performance data and states that clinical investigation was not required because safety and efficacy were supported by non-clinical testing. Therefore, the information typically associated with a study proving a device meets acceptance criteria for an AI/ML medical device (such as those involving test sets, training sets, expert adjudication, MRMC studies, and detailed ground truth establishment) is not present in this document.

The acceptance criteria described in the document relate to engineering and operational performance of a physical gas-scavenging apparatus, not an AI/ML algorithm.

Here's a breakdown of the relevant information provided, adapted to the questions where possible, and noting where information is absent due to the nature of the device and submission:

Acceptance Criteria and Reported Device Performance

The document describes performance criteria primarily related to pressure limits for gas scavenging.

Study Details (as inferable from the document, with many points being N/A for this type of device/submission)

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

   • Sample Size: Not applicable. This device is a physical gas scavenging apparatus, not an AI/ML algorithm that processes data. Testing was likely done on physical units of the device under controlled conditions. The number of samples/units tested is not specified but would be part of standard engineering verification.
   • Data Provenance: Not applicable in the context of "data" for an AI model. This refers to bench testing performed on the physical device. The location of testing is not specified, but it would be part of the manufacturer's quality system. The testing would be "prospective" in the sense that it was conducted specifically for this submission.

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

   • Not applicable. Ground truth for a physical device's performance usually relates to engineering specifications and physical measurements, rather than expert interpretation of complex data (like medical images). Compliance with standards like ISO 80601-2-13 is verified directly through measurement.

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

   • Not applicable. This is relevant for studies involving human interpretation or subjective assessments. Performance testing for this device is based on objective measurements against engineering specifications.

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:

   • No. This is not an AI-assisted device. MRMC studies are used to evaluate diagnostic imaging devices or AI algorithms that assist human readers.

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

   • Not applicable. This is not an algorithm-only device. It's a physical medical device.

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

   • Engineering specifications and objective physical measurements: The "ground truth" for this device's performance is whether its physical output (e.g., pressure) meets predefined engineering and regulatory standards (e.g., ISO 80601-2-13).

7. The sample size for the training set:

   • Not applicable. This is not an AI/ML device that requires a training set.

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

   • Not applicable. No training set for an AI/ML model.

In summary, the provided document details a 510(k) submission for a physical medical device. The "acceptance criteria" and "proof" primarily revolve around bench testing demonstrating compliance with established engineering standards and functional specifications, rather than complex data-driven analyses characteristic of AI/ML device evaluations. The FDA determined that the non-clinical testing was sufficient to establish substantial equivalence to a predicate device, thus no clinical trials were deemed necessary.

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The Indego® orthotically fits to the lower limbs and the trunk; the device is intended to enable individuals with spinal cord injury at levels T3 to L5 to perform ambulatory functions with supervision of a specially trained companion in accordance with the user assessment and training certification program. The device is also intended to enable individuals with spinal cord injury at levels C7 to L5 to perform ambulatory functions in rehabilitations in accordance with the user assessment and training certification program. The Indego is not intended for sports or stair climbing.

Parker Hannifin's Indego® device is a wearable powered exoskeleton that actively assists individuals to stand and walk; these are patients with walking impairments resulting from lower extremity weakness or paralysis. The Indego consists of snap-together components weighing 26 pounds total. The hip component houses a rechargeable battery pack, central processor, and Bluetooth radio, while each upper leg component houses two motors as well as embedded sensors and controllers. On-board microprocessors receive signals from integrated sensors which provide information on the user's posture and tilt. This allows the device to function in a manner similar to the Segway personal mobility device, which is controlled by the user's tilt. A user similarly controls of the Indego by means of postural changes (e.g., to walk forward, the user just leans forward). The technology of the design links the low weight and low profile to advanced battery technology (smaller size), motors (smaller and more powerful), and micro controllers (state-of-the art). Visual cues from the LED lights on the hip and vibratory feedback inform both the patient and therapist or trained support person of the status and mode of operation. The Indego controls are self-contained, with forearm crutches or a walker used solely for stability. Users can perform sit-to-stand and stand-to-sit transitions and walk along even or uneven terrain up to about five degree (5°) grades. Taller torso "wings" are provided to support users who may need additional trunk support while walking. A physical therapist can configure, operate, and monitor the device during therapy and training to make adjustments as needed. This is achieved through the support of a wireless application that will run on mobile/wifi connected smart devices such as an iPod or iPhone. The patient and physical therapist will be able to work in concert to achieve the actions of transitioning from sitting to standing, standing to walking, stop walking, and return from standing to sitting. The untethered, free roaming design of the device will allow it to be utilized in multiple indoor and outdoor locations in either a rehabilitation setting with a certified Indego Specialist or a personal setting with a trained support person.

The provided text describes the Indego® powered lower extremity exoskeleton, and its substantial equivalence submission (K171334) to the FDA. The submission emphasizes clinical and non-clinical data to support expanded indications for use.

Here's an analysis of the acceptance criteria and the study proving the device meets these criteria, based on the provided text:

Key Takeaway: The device's "acceptance criteria" are not explicitly defined as specific numerical thresholds for performance metrics to achieve FDA clearance in the same way clinical trials for pharmaceuticals might state primary endpoints. Instead, the "acceptance criteria" are implicitly demonstrated by showing the device's safety and effectiveness for its intended use, comparable performance to predicate devices, and successful completion of various non-clinical and clinical tests. The primary measure of clinical success appears to be enabling ambulatory functions with minimal assistance (FIM score of 4 or higher) and demonstrating walking speed.

1. Table of Acceptance Criteria and Reported Device Performance

Since explicit acceptance criteria with numerical targets are not stated, the table below will summarize the demonstrated performance and implicitly infer the "acceptance criteria" based on the successful clearance. The core "acceptance criterion" appears to be demonstrating safety and effectiveness for the stated Indications for Use, with performance comparable or better than predicate devices.

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

The terms "test set" and "training set" are typically used in machine learning. However, for medical device clinical studies, we can interpret the clinical data used for evaluation (which serves a similar purpose to a "test set" in demonstrating performance) as follows:

• Pilot Study: 16 subjects
• Multi-Site Clinical Trial: 45 subjects
• Postmarket Clinical Data (Rehabilitation Use): 37 subjects
• Personal Use Training Program Study: 4 subjects (user and support person teams)
• Postmarket Personal Use Data: 5 subjects (user and support person teams)

Total unique subjects across all reported clinical studies: While there might be some overlap, the text states "TOTAL SCI SUBJECTS: 107" for Indego data (comparing to Ekso's 56). This suggests the aggregate number of unique individuals whose data contributed to the clinical evaluation is 107.

Data Provenance:

• The text doesn't explicitly state the country of origin for all clinical data. However, the contact information for the sponsor (Parker Hannifin) is in USA (Ohio), and the submission correspondent (Emergo) is in USA (Texas). The postmarket personal use data mentions "5 trained Indego personal users worldwide. One of these individuals is a European user... The other 4 are from the United States." This indicates a mix of US and implied international data for personal use.
• The studies were prospective in nature, as they describe "Pilot Study", "Multi-Site Clinical Trial", and "Personal Use Training Program Study" which were IRB-approved and conducted according to GCP. Postmarket data would be collected retrospectively from existing users.

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

This device is not an AI/ML diagnostic tool, so the concept of "ground truth" derived from expert consensus (e.g., radiologists interpreting images) isn't directly applicable in the same way.

Instead, for this exoskeleton device, "ground truth" or clinical outcomes are established through:

• Direct observation and measurement by trained clinical personnel (e.g., physical therapists, study coordinators) conducting the tests (10 MWT, 6 MWT, TUG, WISCI, FIM scores).
• Patient self-report/feedback for aspects like ease of use and satisfaction with training.
• Physician assessment for patient suitability and injury levels.

The qualifications of the personnel involved are implied by references to "specially trained companion", "physical therapist," "Indego Specialist," and "clinicians." The training and certification programs for both clinicians and personal users (with support persons) indicate a standardized approach to application and performance assessment. No specific number of "experts" for a consensus ground truth committee is mentioned, as the data collection methods are objective measurement and direct clinical observation.

4. Adjudication Method for the Test Set

Given that this is a physical therapy/mobility device study, and not an image-based diagnostic study where multiple readers might interpret findings, a formal "adjudication method" like "2+1" or "3+1" (common for reconciling discordant image interpretations) is not applicable here. Performance measures (e.g., walking speed, distance, FIM scores) are objective and directly measured or scored by the trained clinical staff involved in the studies. Adverse events are recorded and investigated as part of standard clinical trial procedures.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done.

MRMC studies are typically used to evaluate the impact of a diagnostic tool (like an AI algorithm) on human reader performance, usually in radiology, by comparing diagnostic accuracy or efficiency with and without AI assistance across multiple cases and readers. This exoskeleton is a treatment/assistive device, not a diagnostic one.

The studies focused on the device's standalone performance and safety in enabling mobility for individuals with SCI, rather than assisting human readers in interpreting data. The "assistance" refers to the device assisting the patient, not AI assisting a human healthcare provider's diagnostic process.

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

Again, this is not an AI/ML algorithm in the diagnostic sense, but a powered mechanical device. Therefore, a "standalone algorithm only" performance is not relevant.

However, the "performance" of the device itself (independent of human diagnostic input, but certainly dependent on human operation and supervision) is extensively reported through:

• Non-Clinical Performance Data: Maximum Torque Testing, Cleaning Chemical Compatibility Testing, Component Life Cycle Testing, Durability Testing, Battery Life Cycle Testing, Storage and Transport Testing, Software verification and validation testing, Electrical safety testing, Electromagnetic compatibility testing – all reported as "PASS."
• Clinical Performance Data: The various functional mobility tests (10 MWT, 6 MWT, TUG, WISCI, FIM). These tests assess the patient's ability to ambulate with the device, which is essentially the device's functional performance in combination with the user. The device acts in conjunction with the user's postural changes and the supervision of a trained human.

7. The Type of Ground Truth Used

As discussed in #3, for this type of medical device, "ground truth" is established through:

• Objective Clinical Measurements: Quantifiable outcomes from standardized mobility tests (e.g., 10 Meter Walk Test, 6 Minute Walk Test, Timed Up and Go Test).
• Functional Independence Measures (FIM) and Walking Index for Spinal Cord Injury (WISCI) Scores: These are standardized clinical rating scales, where scores are determined by trained clinicians (Indego Specialists) based on observation of the patient's performance and the level of assistance required.
• Adverse Event Reporting: Direct observation and documentation of any adverse events during use.
• Patient and Trainer Feedback: Qualitative feedback on training adequacy and real-world use.

Unlike diagnostic tools, there isn't a "true positive/negative" outcome like disease presence confirmed by biopsy. Instead, the "ground truth" is the demonstrated functional ability and safety of the patient using the device as measured by validated clinical instruments.

8. The Sample Size for the Training Set

The text refers to "training" in two contexts:

1. Device training for users: Subjects undergo extensive training to learn how to use the Indego. This is training for the human user, not training data for an AI algorithm.
2. Training data for the device itself: If the device contained adaptable AI/ML components that learn from data, this section would be relevant. However, the description of the Indego's "on-board microprocessors receive signals from integrated sensors which provide information on the user's posture and tilt" and "user similarly controls of the Indego by means of postural changes" suggests deterministic or rule-based control rather than a deep learning model that would require a large "training set" of performance data. There is no mention of a machine learning "training set" size for the device's internal algorithms.

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

As there's no explicitly described "training set" for an AI/ML algorithm within the device. Thus, the method for establishing "ground truth for the training set" is not applicable in the context of an AI/ML model for this device. The device's control mechanisms appear to be based on pre-programmed logic derived from engineering principles and biomechanics, rather than learned from a large dataset.

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The Indego® orthotically fits to the lower limbs and the trunk; the device is intended to enable individuals with spinal cord injury at levels T7 to L5 to perform ambulatory functions with supervision of a specially trained companion in accordance with the user assessment and training certification program. The device is also intended to enable individuals with spinal cord injury at levels T4 to T6 to perform ambulatory functions in rehabilitation institutions in accordance with the user assessment and training certification program. The Indego is not intended for sports or stair climbing.

Parker Hannifin's Indego® device is a wearable powered exoskeleton that actively assists individuals to stand and walk. Unique in design, the Indego consists of five (5) snap-together components (the lumbar/hip section, right and left upper leg sections, and right and left lower leg sections) weighing 26 pounds total. The hip component houses a rechargeable battery pack, central processor, and Bluetooth module, while each upper leg component houses two motors as well as embedded sensors and controllers.

On-board microprocessors receive signals from integrated sensors which provide information on the user's posture and tilt. This allows the device to function in a manner similar to the Segway personal mobility device, which is controlled by the user's tilt. A user similarly controls the Indego by means of postural changes (e.g., to walk forward, the user just leans forward). The technology of the design links the low weight and low profile to battery technology (smaller size), motors (smaller and more powerful), and micro controllers. Visual cues from LED lights on the hip unit and vibratory feedback inform both the therapist and patient of the status and mode of operation.

The Indego controls are self-contained, with crutches or a walker used solely for stability. Users can perform sit-to-stand and stand-to-sit transitions and walk along even or uneven terrain up to about five degree (5°) grades. Taller torso "wings" are provided to support users who may need additional trunk support while walking. A physical therapist can configure, operate, and monitor the device during therapy and training to make adjustments as needed. This is achieved through the support of a wireless application that will run on mobile/wifi connected smart devices such as an iPod or iPhone. Through the use of a Bluetooth connection, the Indego device's mode of operational parameters such as gait speed and step length/height, will be able to be changed or modified in real time. The device can be utilized in multiple indoor and outdoor locations within a rehabilitation setting or personal setting.

The provided text describes the regulatory submission for the Indego exoskeleton device. It details the device, its intended use, and a comparison to a predicate device (ReWalk). The filing includes non-clinical and clinical performance data to demonstrate safety and effectiveness.

Here's the breakdown of the acceptance criteria and study information:

Acceptance Criteria and Device Performance

Study Details:

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

   • Sample Size: 40 subjects.
   • Data Provenance: The document does not explicitly state the country of origin but implies a prospective clinical study ("The study was performed in compliance with Good Clinical Practices (GCP) with subjects enrolled in an IRB approved study that were consented for participation according to the intended use of the device..."). This suggests a controlled clinical trial setting.

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

   • The document does not mention the use of experts to establish a "ground truth" for the test set in the traditional sense of medical image analysis or diagnostic studies. Instead, the clinical study involved subjects using the device and their performance (walking speed, TUG test, level of assistance, perceived exertion) being measured directly. The "truth" here is the objective measurement of their functional abilities while using the device.

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

   • Not applicable in the context of this functional performance study. The data collected (e.g., walking speed, FIM scores, TUG test results) are objective measurements or standardized assessment scores, not subjective interpretations requiring adjudication.

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:

   • No, a multi-reader multi-case (MRMC) comparative effectiveness study was not performed. This type of study typically applies to diagnostic or screening devices where human readers interpret medical images or data. The Indego is a powered exoskeleton for ambulation; its effectiveness is measured directly through functional performance metrics of the user.

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

   • The device, by its nature, inherently involves a human-in-the-loop. It is a wearable exoskeleton designed to assist individuals with spinal cord injury in performing ambulatory functions. Therefore, a standalone (algorithm only) performance assessment, independent of human interaction, is not relevant or applicable for this device. The software verification and validation were performed as part of non-clinical testing, but this is distinct from measuring "standalone performance" of a diagnostic algorithm.

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

   • The "ground truth" in this context is based on objective functional performance data using standardized clinical assessment tools (e.g., Ten Meter Walk Test, Timed Up-and-Go Test, WISC-II, FIM scores, Borg Rating of Perceived Exertion) and direct observation of subjects' abilities while using the device in a controlled clinical study environment.

7. The sample size for the training set:

   • The document does not explicitly mention a "training set" in the machine learning sense for the device's algorithms. The device's internal microprocessors receive signals from integrated sensors and function based on the user's posture and tilt (similar to a Segway). The development of these control algorithms would have involved engineering design, testing, and potentially iterative refinement, but the document does not specify a distinct "training set" of data in the manner of AI/ML models.

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

   • Given that a "training set" (as understood in machine learning) is not explicitly described, the method for establishing its "ground truth" is not detailed. The device likely relies on engineering principles and biomechanical models for its control algorithms, validated through non-clinical and clinical testing, rather than an AI model trained on a large dataset with established ground truth labels common in areas like image recognition. The "ground truth" for its functional performance is established through the clinical study mentioned above.

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