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ShuntCheck is an aid to the detection of flow in implanted cerebrospinal fluid (CSF) shunts. ShuntCheck includes Micro-Pumper, a component which may be used to temporarily increase CSF flow in suspected non-flowing, patent shunts during the ShuntCheck test. ShuntCheck cannot alone diagnose CSF shunt function or malfunction. The clinical diagnosis of CSF shunt function or malfunction, incorporating the flow information from ShuntCheck, should be made only by a qualified neurosurgeon.

ShuntCheck is a non-invasive device which detects flow in a CSF shunt via transcutaneous thermal dilution. The device consists of a single use disposable thermosensor array patch which is connected to a data acquisition unit (a DAQ) which is connected to a laptop or tablet computer. The device also includes a Micro-Pumper which vibrates the shunt valve during the test procedure to generate a temporary increase in flow in patent but temporarily non-flowing shunts. The shunt is cooled transcutaneously by placing an instant ice pack over the shunt cephalic to the thermosensor. The thermosensor array patch, which is placed on the skin over the shunt "downstream" of the ice, reads the change in skin temperature over the shunt as cooled fluid flows downstream and also at a two nearby control locations. Data is transferred through the DAQ and captured in the computer. If the device detects a characteristic downstream transcutaneous temperature dip, the computer reports "flow confirmed" and presents a time-temperature graph of test data. If no temperature dip is detected, the unit reports "flow not confirmed" and presents a time-temperature graph.

The ShuntCheck III device is intended as an aid to detect flow in implanted cerebrospinal fluid (CSF) shunts. The study presented is a 510(k) submission for substantial equivalence to a predicate device, ShuntCheck v2.2. The evaluation includes both bench testing and limited clinical testing.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Bench Testing (without Micro-Pumper): The sample size for simulating flow detection (10 ml/hr and 0 ml/hr, threshold, and misalignment) is not explicitly stated as a number of tests or runs. It mentions "bench testing was conducted" and "the ShuntCheck test is conducted normally."
• Bench Testing (with Micro-Pumper):
  • Shunt flow generation and impact on valve function: Testing of eight shunt valves and 5 programmable valves.
  • ShuntCheck's ability to detect flow generated by Micro-Pumper: Not explicitly stated as a number of tests or runs.
• Clinical Testing:
  • 38 asymptomatic patients for testing ShuntCheck without Micro-Pumper.
  • 12 patients (asymptomatic or confirmed functioning shunts) for testing with the proposed Micro-Pumper.
  • 4 patients with confirmed obstructed shunts for testing with the proposed Micro-Pumper.
• Data Provenance: The clinical testing was conducted at Boston Children's Hospital, indicating US-based, prospective clinical data collection for the clinical testing aspects. The bench testing would be lab-based.

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

• For the clinical testing, the ground truth for "shunt function or malfunction" was confirmed via MRI imaging.
• The clinical diagnosis of CSF shunt function or malfunction, incorporating flow information, "should be made only by a qualified neurosurgeon." This implies that a qualified neurosurgeon ultimately interpreted the MRI imaging to establish the ground truth for shunt status in the clinical study. The exact number of neurosurgeons involved in ground truth establishment is not specified. Their qualifications are stated as "qualified neurosurgeon."

4. Adjudication Method for the Test Set

• The document does not explicitly describe an adjudication method for the ground truth (e.g., 2+1 reads, consensus panel). It states that shunt function or malfunction was "confirmed via MRI imaging," implying a standard diagnostic process overseen by medical professionals (qualified neurosurgeons).

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 MRMC comparative effectiveness study was done. This device is not an AI-assisted diagnostic tool for human readers; it's a standalone device outputting a "Flow Confirmed" or "Flow Not Confirmed" result. The clinical study evaluated the device's accuracy in detecting flow in relation to a patient's shunt status and the impact of the Micro-Pumper component.

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

• Yes, a standalone performance evaluation was done through both bench testing and clinical testing. The device's software algorithm provides the "Flow Confirmed" or "Flow Not Confirmed" result based on its sensor data, independent of human interpretation of raw data. The "Accuracy without Micro-Pumper" and "Accuracy with proposed Micro-Pumper" in the clinical study table directly reflect the standalone performance against the ground truth.

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

• Bench Testing: The ground truth was established by precisely controlled flow rates (e.g., 10 ml/hr, 0 ml/hr) in a simulated environment using infusion pumps and drop counters.
• Clinical Testing: The ground truth for shunt function or malfunction was established by MRI imaging.

8. The Sample Size for the Training Set

• The document describes a 510(k) submission for a device, not an AI/ML algorithm that typically requires a large, explicitly defined "training set." The device relies on a validated algorithm for detecting flow. The development of this algorithm would have involved various bench and potentially early clinical data, but a specific "training set size" in the context of an AI/ML model is not provided or applicable in the way it's usually understood for deep learning. The testing described serves as validation and verification of the final algorithm's performance.

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

• Given that this is not an AI/ML device with an explicit training set in the modern sense, the concept of "ground truth for the training set" as it relates to machine learning is not directly applicable. The algorithm's development (and thus its "training" or optimization) would have been based on principles of thermal dilution and fluid dynamics, likely informed by experimental data from bench models. The document refers to the algorithm as a "validated algorithm."

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ShuntCheck is an aid to the detection of flow in implanted cerebrospinal fluid (CSF) shunts. ShuntCheck cannot alone diagnose CSF shunt function or malfunction. The clinical diagnosis of CSF shunt function or malfunction, incorporating the flow information from ShuntCheck, should be made only by a qualified neurosurgeon.

ShuntCheck is a non-invasive device which detects flow in a CSF shunt via transcutaneous thermal convection. The device consists of a single use disposable thermosensor array patch which is connected to a handheld "biodisplay" unit. The shunt is cooled transcutaneously by placing ice over the shunt cephalic to the thermosensor. The thermosensor array patch, which is placed on the skin over the shunt "downstream" of the ice, reads the change in skin temperature over the shunt as cooled fluid flows downstream and also at a nearby control location. Data is captured in the biodisplay unit. If the device detects a characteristic downstream transcutaneous temperature dip, the biodisplay reports "flow confirmed". If no temperature dip is detected, the unit reports "flow not confirmed"

The provided text does not contain specific acceptance criteria or a detailed study description with performance metrics to fully answer all aspects of the request. The document is primarily a 510(k) summary and an FDA clearance letter for the ShuntCheck device, which is an aid to detect flow in CSF shunts. It mentions functional and safety testing, and animal studies, but does not provide quantitative acceptance criteria or detailed results of a clinical study that would demonstrate performance.

However, based on the available information, here's what can be extracted and inferred:

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

The document does not explicitly state quantitative acceptance criteria for device performance (e.g., sensitivity, specificity, accuracy thresholds) or provide reported device performance metrics in a clinical context. It mentions:

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

• Test Set Sample Size: Not explicitly stated for any clinical or animal studies. The text mentions "three animal studies" and "a representative sample of the device" for safety testing. This suggests multiple animals were used, but the exact number is not provided.
• Data Provenance:
  • Type: Primarily animal studies and bench testing. There is no mention of human clinical trial data for performance evaluation in this summary.
  • Country of origin: Not specified.
  • Retrospective or Prospective: Animal studies and bench testing are generally prospective in nature.

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. The primary performance evaluation described was in animal studies and bench testing; there's no indication of human expert involvement for ground truth establishment in a test set. The intended use states that "The clinical diagnosis of CSF shunt function or malfunction, incorporating the flow information from ShuntCheck, should be made only by a qualified neurosurgeon," implying neurosurgeons are the experts who would ultimately interpret the device's output in a clinical setting, but not for establishing ground truth in a validation study described here.

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

Not applicable. As no human expert-adjudicated test set is described, there is no mention of an adjudication method.

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 performed. The ShuntCheck is a non-invasive thermal convection device, not an AI-assisted diagnostic tool. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant to this device. The device provides "flow confirmed" or "flow not confirmed" output.

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

This is not a software-only "algorithm." It is a physical device (thermosensor array patch connected to a handheld "biodisplay" unit) that detects temperature changes to infer flow. The device itself provides a "flow confirmed" or "flow not confirmed" output, which can be considered its "standalone" output. The studies mentioned (bench testing, animal studies) assessed this device's ability to accurately provide this output. However, it's explicitly stated that "ShuntCheck cannot alone diagnose CSF shunt function or malfunction," and its output must be interpreted by a neurosurgeon.

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

The document implies that the ground truth for the animal studies was based on known flow rates (ml/hr) or the functional state of the shunt (e.g., presence or absence of flow) as established by experimental conditions in the animal models. For bench testing, the ground truth was related to known electrical resistances and expected temperature outputs.

8. The sample size for the training set

The device is not AI/machine learning based and therefore does not have a "training set" in the conventional sense of data used to train an algorithm. The development cycle would involve engineering design, prototyping, and testing, but not and algorithmic training set.

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

Not applicable, as there is no training set for an AI algorithm.

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ShuntCheck is indicated for the detection of flow in implanted silastic cerebrospinal Shuid (CSF) shunts. ShuntCheck must be used by a qualified neurosurgeon according to instructions. ShuntCheck cannot, alone diagnose CSF shunt function or malfunction. The clinical diagnosis of CSF shunt function or malfunction, incorporating the flow information from ShuntCheck, should be made only by a qualified neurosurgeon.

Not Found

I am sorry, but the provided text does not contain the detailed information needed to describe the acceptance criteria and the study that proves the device meets them. The document is a 510(k) clearance letter for a device called "ShuntCheck," indicating that the FDA has determined it is substantially equivalent to legally marketed predicate devices.

While it mentions the indications for use, it does not include:

• A table of acceptance criteria and reported device performance.
• Details about sample sizes, data provenance, or the number and qualifications of experts for ground truth.
• Information on adjudication methods.
• Any mention of multi-reader multi-case (MRMC) studies or standalone algorithm performance.
• The type of ground truth used in studies.
• Sample size and ground truth establishment for a training set (which would be relevant for devices involving AI/ML, but less so for a 2004 device like ShuntCheck which likely relies on physical principles).

Therefore, I cannot fulfill your request based on the given text.

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