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For nasogastric/oralgastric enteral feeding, incorporating safety connectors which reduce the risk of misconnections between feeding tubes and intravenous connectors.

The Nutrisafe 2 silicone feeding tubes are utilizing a different formulation of silicone material I he Nutrisate 2 silicone feeding tubes. The feeding tubes. The feeding tubes are available in several than the catisting Nutrioure is a unique connection does not incorporate a luer, reducing SIZCS, and the leveling system connains an IV administration set or other medical delivery the fisk of madvertenity connection reduces the risk of involuntary disconnection; voluntary systems: "The is achieved by simply unscrewing the hub connections.

The Nutrisafe 2 Feeding Tube's acceptance criteria and the study proving its performance are detailed below. It's important to note that this device is a medical device, and the provided text from a 510(k) submission primarily focuses on demonstrating substantial equivalence to predicate devices through non-clinical bench testing, rather than a clinical study with human subjects, AI systems, or extensive statistical performance metrics typically found for diagnostic or AI-driven medical devices.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: The document does not specify a distinct "test set" sample size in terms of the number of feeding tubes or components used for the non-clinical bench testing. It broadly states that "Non-Clinical verification... was conducted through in-vitro bench testing."
• Data Provenance: The data is from in-vitro bench testing, meaning it was conducted in a laboratory setting. The country of origin for the data is not explicitly stated, but the submission is to the US FDA, implying the tests were conducted either by the applicant (Vygon Corp. in Montgomeryville, PA, USA) or a certified laboratory for the purpose of US market approval. The testing is retrospective in the sense that it evaluates the performance of the manufactured device in a controlled environment as part of the market submission process, rather than a prospective clinical trial.

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

This section is not applicable as the provided information describes non-clinical bench testing of a physical medical device, not a diagnostic or AI-driven system requiring expert-established ground truth from images or clinical data. The "ground truth" here is determined by engineering specifications and physical measurements, and the conformity to these is assessed by technical personnel.

4. Adjudication Method for the Test Set

This section is not applicable for the same reasons as point 3. Adjudication methods like 2+1 or 3+1 are used for resolving disagreements among multiple human readers on a diagnosis or interpretation, which is not relevant to the described non-clinical bench testing.

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 section is not applicable. The provided document is for a physical medical device (feeding tube) and does not involve AI assistance, human readers, or image interpretation. Therefore, an MRMC study or effect size calculation with AI assistance is not relevant.

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

This section is not applicable. The device is a physical feeding tube, not an algorithm or software. Therefore, standalone algorithm performance is not relevant.

7. The Type of Ground Truth Used

The ground truth used for this premarket notification is based on engineering specifications and intended use requirements. The non-clinical bench testing verified that the Nutrisafe 2 Feeding Tube, despite a change in silicone material formulation, continued to meet these established specifications and its intended use, which includes safety features related to connection types.

8. The Sample Size for the Training Set

This section is not applicable. There is no "training set" in the context of this device's non-clinical verification. Training sets are relevant for machine learning algorithms, which are not part of this submission. Device manufacturers conduct design and development activities where prototypes might be tested, but this is distinct from an "AI training set."

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

This section is not applicable as there is no training set for this device.

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For nasogastric/oralgastric enteral feeding, incorporating safety connectors which reduce the risk of misconnections between feeding tubes and intravenous connectors.

The Nutrisafe 2 feeding tubes are a product line extension to the existing Nutrisafe 2 feeding tubes. The subject feeding tubes are available in several sizes and in two materials. The feeding system contains a unique connection that does not incorporate a luer, reducing the risk of inadvertently connecting to intravenous connectors. The locking connection reduces the risk of involuntary disconnection; voluntary disconnection is achieved by simply unscrewing the hub connections.

The provided text describes a 510(k) premarket notification for a medical device, the Nutrisafe 2 Feeding Tube, and its substantial equivalence to a predicate device. However, it does not involve an AI/ML device or a study that evaluates its performance against acceptance criteria in the way described in your request. The document focuses on the regulatory clearance process for a common medical device (feeding tube) based on its technological characteristics and intended use, rather than an AI algorithm's diagnostic or predictive capabilities.

Therefore, many of the requested categories in your prompt are not applicable to the provided information. I will, however, outline what can be extracted from the text in relation to your request.

Summary of Provided Information Regarding Acceptance Criteria and Study:

The provided document, K100163, is a 510(k) summary for a medical device (Nutrisafe 2 Feeding Tube), which is a product line extension. This type of regulatory submission assesses substantial equivalence to a predicate device, rather than proving performance against specific quantitative acceptance criteria of an AI model.

The "study" mentioned is a non-clinical verification through in-vitro bench testing. This type of testing ensures the device meets its specifications and intended use, primarily focusing on physical and functional characteristics suitable for a feeding tube, such as the integrity of the connection system, material compatibility, and flow rates. It does not involve human subjects, comparison to AI, or expert adjudication of outcomes.

Here's how the information aligns (or doesn't align) with your request categories:

1. Table of Acceptance Criteria and Reported Device Performance: This kind of quantitative data (e.g., sensitivity, specificity, AUC) is not present because this is a physical medical device, not an AI/ML diagnostic tool. The general "acceptance criteria" for this device would be its ability to meet specifications for a feeding tube as determined by the in-vitro bench testing.

   Metric/Criteria	Reported Device Performance
   All Specifications and Intended Use	"meets all specifications and intended use."

2. Sample size used for the test set and the data provenance: Not applicable in the context of an AI/ML device. The "test set" here would refer to the physical samples of the Nutrisafe 2 Feeding Tubes used in the in-vitro bench testing. The document does not specify the number of tubes tested, nor their manufacturing origin beyond "Vygon, Montgomeryville, PA 18936." This was in-vitro testing, not human data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth, in the context of an AI/ML model, refers to validated diagnoses or outcomes. For a physical feeding tube, "ground truth" is established by engineering standards and functional tests. No external experts or adjudication were involved in establishing "ground truth" for the device's functional performance beyond the internal verification process.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. This method is used for resolving discrepancies in expert labeling or diagnoses for AI/ML validation datasets.

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 study type is for evaluating the impact of AI assistance on human diagnostic performance. The Nutrisafe 2 Feeding Tube is a standalone medical device, not an AI component.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. This refers to the performance of an AI algorithm alone.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For this device, the "ground truth" for its performance is derived from engineering specifications and in-vitro functional and safety testing results. For example, the "locking connection reduces the risk of involuntary disconnection" is a functional characteristic tested against design requirements.

8. The sample size for the training set: Not applicable. This device does not have a "training set" in the context of AI/ML.

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

In summary, the provided document describes a traditional medical device (feeding tube) and its regulatory clearance based on substantial equivalence and non-clinical bench testing, not an AI/ML device. Therefore, most of the detailed questions regarding AI/ML study methodologies are not relevant to this specific premarket notification.

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