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Enteral Feeding Sets are intended to dispense liquid nutrition (feeding solutions) at a user controlled rate. These enteral feeding sets interface with a patient's feeding tube and may use gravity or an enteral feeding pump to dispense feeding solution. The enteral feeding sets include a bag to contain the feeding solution and/or a EnPlus spike to connect to a pre-filled container. The device is used for infants, children, adolescents and adults.

The Enteral Feeding Sets have four models, Enteral Feeding Pump bag Set, Enteral Feeding Pump EnPlus spike Set, Enteral Feeding Gravity bag Set, Enteral Feeding Gravity EnPlus spike Set. The Enteral Feeding Pump bag Set and Enteral Feeding Gravity bag Set both have a structure of a formula bag with 100ml graduations and a protective closure cap. The difference is the former is droved by pump and the latter is droved by gravity. Enteral Feeding Pump EnPlus spike Set and Enteral Feeding Gravity EnPlus spike Set feature a piercing spike (with or without screw cap) used to connect the set to a prefilled container of enteral feeding solution. It is a single-use, non-sterile device.

The provided document is a 510(k) Premarket Notification from the FDA for "Enteral Feeding Sets." It describes the device, its intended use, and a comparison to a predicate device, along with a summary of non-clinical testing.

However, the document does not contain any information about a study that proves the device meets the acceptance criteria for an AI/ML powered medical device. The acceptance criteria and testing detailed in the document are for traditional medical device performance, such as dimensions, material properties, leakage, tensile strength, and biocompatibility. There is no mention of AI models, test sets for AI performance, expert ground truth establishment, or MRMC studies.

Therefore, I cannot fulfill your request for information regarding AI/ML acceptance criteria and study details based on the provided text.

If you are looking for an example of how such a request would be answered for an AI/ML device, please provide a document that discusses an AI/ML powered medical device and its validation.

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Enteral feeding set is intended to use gravity to deliver liquid nutritional formulas or water to patient's enteral access device (feeding tube). The device includes a bag to contain the feeding solution.

The proposed device is provided non-sterile and single use. It has two types (Type A and Type B). Type A has more component (ENFit transition adapter) than Type B.

The Type A includes 5 models, ENF02G-1000R-T, ENF02G-1000B-T, ENF02G-1200R-T, ENF02G-1200B-T and ENF02-500R-T, which have the same components including 1) filling port, 2) bag, 3) tube, 4) drip chamber, 5) roller clamp, 6) ENFit female connector, 7) ENFit transition adaptor and 8) protecting cover. The difference between the 5 models is the specification of bag and tube.

The Type B includes 5 models, ENF02G-1000R, ENF02G-1000B, ENF02G-1200R, ENF02G-1200B and ENF02-500R, which have the same components including 1) filling port, 2) bag, 3) tube, 4) drip chamber, 5) roller clamp, 6) ENFit female connector and 7) protecting cover. The difference between the 5 models is the specification of bag and tube.

The provided document is a 510(k) summary for an Enteral Feeding Set. It describes non-clinical tests performed to demonstrate substantial equivalence to a predicate device, but it does not include any information about clinical studies or the performance of a device based on expert reviewer adjudication for a test set. This device is an "Enteral Feeding Set" which is a physical medical device, not a software-driven diagnostic or AI-powered system that would typically involve acceptance criteria related to diagnostic accuracy, sensitivity, or specificity, adjudicated by experts.

Therefore, many of the requested items (2, 3, 4, 5, 6, 7, 8, 9) are not applicable to the content of this document.

Here's an analysis of the provided information based on the request:

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

The document provides a summary of non-clinical test conclusions and a comparison to a predicate device (K142539). These demonstrate compliance with relevant standards rather than specific performance metrics against pre-defined acceptance criteria in the typical sense of a diagnostic device.

Study that proves the device meets the acceptance criteria:

The device meets its "acceptance criteria" through a series of non-clinical tests and compliance with recognized international standards for medical devices. The document explicitly states: "Non clinical tests were conducted to verify that the proposed device met all design specifications as was same/similar to the predicate device. The test results demonstrated that the proposed device complies with the following standards." The study described is essentially a compilation of these non-clinical tests.

The following information is not available in the provided text as it pertains to clinical studies often associated with diagnostic or AI-powered devices, which is not the nature of this enteral feeding set's submission.

2. Sample size used for the test set and the data provenance: Not applicable; no clinical test set for diagnostic performance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable; no clinical test set requiring expert ground truth.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable; no clinical test set requiring 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: Not applicable; this is a physical medical device, not an AI software.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable; this is a physical medical device, not an AI algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable; the "ground truth" for this device is compliance with engineering specifications, material biocompatibility, and functional performance as per standards, rather than a diagnostic 'truth'.
8. The sample size for the training set: Not applicable; no training set mentioned, as this is a physical device, not a machine learning model.
9. How the ground truth for the training set was established: Not applicable.

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