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The Disposable Enteral Feeding Sets is intended to be used for enteral feeding nutrition into gastrointestinal tract by connecting with nasogastric tube by gravity. The devices may include a bag to contain the feeding nutrition and/or a spike to connect to a prefilled container. The device is used for children, adolescents, and adults.

The proposed device, Disposable Enteral Feeding Sets, is intended to be used for enteral feeding nutrition into gastrointestinal tract by connecting with nasogastric tube by gravity. The devices may include a bag to contain the feeding nutrition and/or a spike to connect to a prefilled container. The device is used for children, adolescents and adults. The proposed device is provided sterile or non-sterile and single use.

The provided text is a 510(k) Pre-market Notification for a medical device called "Disposable Enteral Feeding Sets". The document largely focuses on demonstrating substantial equivalence to a predicate device and does not contain the detailed information typically found in a study description for evaluation of AI/algorithm performance.

Therefore, I cannot provide the requested information about acceptance criteria and a study proving a device meets acceptance criteria in an AI/algorithm context. The document describes standard performance testing for a physical medical device, not a software or AI device.

Here's why and what information can be extracted regarding general acceptance criteria and testing:

Why the requested information cannot be fully provided from this document:

• No AI/Algorithm Focus: The device is a "Disposable Enteral Feeding Set," which is a physical medical device for enteral nutrition. There is no indication that it incorporates any AI, machine learning, or complex algorithms that would require the kind of performance studies (e.g., standalone performance, MRMC studies, ground truth establishment by experts) typically associated with AI/ML-driven medical devices.
• "Acceptance Criteria" for Physical Device: The term "acceptance criteria" here refers to the performance specifications and safety standards for a physical product (e.g., leak integrity, flow rate, material biocompatibility, sterility), not diagnostic accuracy or clinical utility of an AI.
• "Study" for Physical Device: The "study" mentioned refers to non-clinical performance tests (chemical, physical, sterility, ENfit physical performance) conducted on the physical device to ensure it meets established safety and performance standards relevant to its physical function. It is not a clinical efficacy study or an AI performance study.

Information that can be extracted relevant to general performance and acceptance (for a non-AI physical device):

1. A table of acceptance criteria and the reported device performance:
   The document does not provide a specific table of acceptance criteria paired with reported device performance values. It states that "the test result showed that the aged proposed device met the acceptance criteria" for various tests. The specific acceptance criteria are implicitly those defined by the referenced international standards and internal procedures.

   Acceptance Criteria (Implied)	Reported Device Performance
   Compliance with ISO 20695:2020 Annex C & D	Met
   Compliance with ISO 8536-4:2022 Annex B	Met
   Compliance with ISO 8536-4:2020 Annex B.4	Met
   Compliance with ISO 80369-20:2015	Met
   Compliance with USP Sterility Tests	Met
   Final product inspection procedure specifications	Met
   Performance of bag part (e.g., integrity, capacity)	Met
   Performance of spike part (e.g., connection integrity)	Met
   Biocompatibility (for patient-contact material)	Met (no adverse effects)

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):
   The document does not specify the sample sizes for the non-clinical performance tests. The data provenance is implied to be from the manufacturer's testing in China, as the applicant is "Beijing L&Z Medical Technology Development Co., Ltd." These are non-clinical lab tests, not human 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. "Ground truth" in the context of expert consensus is relevant for AI/ML diagnostic or prognostic devices. For a physical device like an enteral feeding set, performance is evaluated against engineering specifications and international standards, not against expert human interpretations of data.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
   Not applicable for a physical device's non-clinical performance 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:
   Not applicable. The device is a physical feeding set, not an AI or imaging device that would involve human readers or AI assistance.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
   Not applicable. There is no algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
   Not applicable. Performance is measured against physical and chemical standards and specifications (e.g., sterility, dimensional accuracy, material integrity, flow rates), not a ground truth derived from expert consensus, pathology, or outcomes data.

8. The sample size for the training set:
   Not applicable. This is a physical medical device, not an AI/ML model that requires training data.

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

In summary, the provided FDA 510(k) clearance letter and summary pertain to a physical medical device and therefore do not contain the information requested regarding AI/algorithm performance studies.

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