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Model # ECD03-A:
The EnsoETM is a thermal regulating device, intended to:
• connect to a Gaymar Medi-Therm III Conductive Hyper/Hypothermia System or Stryker Altrix Precision Temperature Management System to control patient temperature,
• allow enteral administration of fluids,
• and provide gastric decompression and suctioning.

Model # ECD04-A:
The EnsoETM is a thermal regulating device, intended to:
• connect to a Cincinnati Sub-Zero Blanketrol II or Blanketrol III Hyper-Hypothermia System to control patient temperature,
• allow enteral administration of fluids,
• and provide gastric decompression and suctioning.

The EnsoETM is a multi-lumen silicone tube that is placed in the esophagus in a similar manner to a standard orogastric tube to control a patient's temperature, while simultaneously maintaining access to the stomach to allow gastric decompression, drainage, and enteral administration of fluids, thereby maintaining the functionality of the standard orogastric tube. Modulation and control of patient temperature is achieved by connecting the device to an external heat exchanger. Two lumens connect to the external heat exchanger. A third central lumen connects to wall suction to allow standard gastric decompression or an enteral administration system to allow enteral administration of fluids. The device is made of standard medical-grade silicone. It is a single-use, disposable, non-implantable device with an intended duration of use of 36 hours or less.

The provided text is a U.S. FDA 510(k) Premarket Notification document for the EnsoETM device. This document describes a medical device, its intended use, and the non-clinical performance testing conducted to demonstrate its substantial equivalence to previously cleared predicate devices.

The request asks for information relevant to the acceptance criteria and study that proves a device meets the acceptance criteria, specifically in the context of an AI/human-in-the-loop study. However, the provided document does not describe an AI/human-in-the-loop study, nor does it discuss ground truth establishment for such a study, nor does it mention AI or machine learning in any context.

The "acceptance criteria" and "device performance" described in the document relate to non-clinical performance testing of a physical medical device (EnsoETM), an esophageal thermal regulation device with capabilities for enteral fluid administration and gastric decompression. The testing described focuses on the device's physical properties, such as tensile strength, burst strength, leakage, flow rate, resistance to vacuum, and connector properties, to ensure it functions as intended and safely when used with external temperature management systems.

Therefore, I cannot fulfill the request as it pertains to AI-related acceptance criteria, MRMC studies, ground truth establishment, or training set details because this information is not present in the provided text.

I can, however, extract the acceptance criteria and demonstrated performance for the physical device's non-clinical testing as described in the document:

Based on the provided FDA 510(k) document for the EnsoETM device, here is a summary of the non-clinical performance testing and conclusions related to meeting acceptance criteria:

The "acceptance criteria" in this context are the successful demonstration that the subject devices (EnsoETM models ECD03-A and ECD04-A) meet performance requirements and are substantially equivalent to the predicate devices (EnsoETM models ECD01-A and ECD02-A), particularly with the new ENFit connector for enteral administration. The "study" refers to a series of non-clinical, laboratory-based performance tests.

1. Table of Acceptance Criteria and Reported Device Performance:

The document states that "Non-clinical performance testing was conducted to demonstrate the technological characteristics of the subject devices are substantially equivalent to the predicate devices." For each test, the "Discussion" implicitly serves as the reported device performance demonstrating that the acceptance criteria (i.e., suitability, ability to withstand pressures, prevention of leakage, capability for flow, etc.) were met.

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

The document concerns non-clinical, laboratory-based performance testing of a physical device. It does not provide specific sample sizes (e.g., number of devices tested for each parameter) or data provenance in terms of country of origin or retrospective/prospective nature, as these are typically not reported for such engineering tests in a 510(k) summary. The tests are implicit to be conducted under controlled laboratory conditions to demonstrate device performance characteristics.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

This information is not applicable as the document describes non-clinical engineering performance testing of a physical device, not an AI or diagnostic imaging study requiring expert labeling or ground truth establishment.

4. Adjudication Method for the Test Set:

This information is not applicable as the document describes non-clinical engineering performance testing.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done:

No, an MRMC comparative effectiveness study was not done. This type of study is relevant for diagnostic devices that involve human interpretation of medical images, often with AI assistance, which is not the subject of this 510(k).

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

No, a standalone algorithm performance study was not done. This device is a physical medical device, not an algorithm.

7. The type of Ground Truth used:

This information is not applicable as the document describes non-clinical engineering performance testing. The "ground truth" for these tests would be the established engineering standards, specifications, and physical laws against which the device's performance is measured (e.g., the specified burst pressure a lumen must withstand, or the dimensional requirements of an ISO standard).

8. The sample size for the training set:

This information is not applicable as the document describes non-clinical engineering performance testing, not an AI or machine learning study.

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

This information is not applicable as the document describes non-clinical engineering performance testing, not an AI or machine learning study.

Conclusion from the document regarding meeting acceptance criteria:

The "Conclusion" section of the document states: "The results of all performance testing demonstrate the subject devices have technological characteristics that do not raise new questions of safety or effectiveness when compared to the predicate devices. Therefore, the subject devices are substantially equivalent to the predicate devices." This directly indicates that the device met the non-clinical acceptance criteria established for substantial equivalence.

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