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For temporary (less than six months) subcutaneous or submuscular implantation to develop surgical flaps and additional tissue coverage required in a wide variety of applications, particularly to aid in reconstruction following mastectomy, to aid in the treatment of underdeveloped breasts and to aid treatment of soft tissue deformities.

The Motiva Flora® SmoothSilk® Tissue Expander (MFSTE) is intended for temporary subcutaneous or submuscular implantation to develop surgical flaps and additional tissue coverage and is not intended for use beyond six (6) months. All MFSTEs require periodic, incremental inflation with sterile saline for injection until the desired tissue expansion is developed. Once the desired tissue expansion is achieved, the tissue expander is surgically removed and a breast implant is inserted within the breast pocket.

The MFSTE is constructed of successive cross- linked layers of silicone elastomer with an Integrated Injection Port system for incremental expander filling. The Tissue Expanders are available in a range of sizes to meet diverse patient needs and achieve individualized aesthetic results. The MFSTE has a controlled surface architecture produced by the mandrel imprinting technique with an average roughness of 4 microns. Each MFSTE is supplied sterile.

The injection port is dome-shaped with a radiofrequency identification (RFID) coil embedded in the needle stop. This needle stop prevents inadvertent puncture through the base of the injection port during the filling process.

The injection port is found with the Motiva Flora® Port Locator accessory through the air wound coil's RFID signal placed inside the needle stop. The RFID wireless system comprises two components: the tag and the reader. The Motiva Flora® Port Locator (the reader) has antennas that emit radio waves and receive the signal coming from the RFID coil tag in the needle stop to locate the center of the injection port. The user interface of the Motiva Flora® Port Locator includes LED lights to guide the user during the process. This is an innovative technology not available in other tissue expanders on the market.

In addition, the RFID transponder functions as a traceability feature, which provides a unique electronic serial number (ESN) for each expander. The ESN allows access to a database with the device information (serial and lot number, size, projection, model, manufacturing date, etc.).

Additional design features of the MFSTE include a non-ferromagnetic component design that allows the device to be used with Magnetic Resonance (MR) and Computerized Tomography (CT) under certain conditions.

The MFSTE includes six (6) fixation tabs (TrueFixation®) to provide the surgeon options to suture the tissue expander in the breast pocket. Another design feature is its reinforced base.

The provided text describes a medical device, the Motiva Flora® SmoothSilk® Tissue Expander, and the studies conducted to demonstrate its substantial equivalence to a predicate device for FDA 510(k) clearance.

However, the information provided does not pertain to an AI/ML-enabled medical device. The document describes tests related to:

• Biocompatibility: Testing to ensure the materials are safe for use in the human body.
• Animal Testing: To compare tissue reaction to the device and a predicate.
• Mechanical Testing: To evaluate physical properties like elongation, tensile strength, and injection port integrity.
• Sterilization Testing: To confirm the device can be properly sterilized.
• Port Locator Electrical Safety & EMC Testing: For the accessory device that helps locate the injection port (this is not an AI/ML component; it relies on RFID and basic electrical/electromagnetic principles).
• Port Locator Software Testing: Standard software validation, not specifically AI/ML performance.
• MR Conditional Testing: To ensure compatibility with MRI environments.

Therefore, many of the specific questions about AI/ML device acceptance criteria and study design (e.g., sample size for test/training sets, ground truth establishment, expert adjudication, MRMC studies, standalone performance, effect size) are not applicable to this document. The document focuses on demonstrating physical, chemical, and biological equivalence, along with the safety and basic functionality of the device and its non-AI accessory.

To answer your request based on the provided text, I can only address aspects relevant to a non-AI medical device.

Since the provided document does not describe an AI/ML-enabled medical device, many of the requested categories related to AI/ML specific studies (e.g., sample sizes for training/test sets, expert adjudication methods, MRMC studies, standalone performance) are not applicable.

However, I can extract the general acceptance criteria and the performance studies conducted for this non-AI medical device.

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

The document describes "pre-determined acceptance criteria" for various tests, and states that "All testing met the requirements" or "All testing results met their pre-determined acceptance criteria," or "demonstrate[d] that the MFSTE is substantially equivalent." Specific numerical acceptance thresholds are not explicitly listed in the summary, but the types of criteria are implied by the tests performed.

2. Sample sizes used for the test set and the data provenance:

• Sample Size: Not specified in the provided summary for any of the tests (e.g., number of devices tested for mechanical properties, number of animals in the GLP study).
• Data Provenance:
  • Country of Origin: Not specified.
  • Retrospective or Prospective: Unclear from the summary. Animal studies are typically prospective. Mechanical, sterilization, and electrical/EMC tests are conducted under controlled, often laboratory, conditions.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable for a non-AI physical device validation. "Ground truth" in this context refers to established standards (e.g., ASTM standards for mechanical properties, ISO standards for biocompatibility) or direct measurements. For the animal study, histopathologic evaluation would be performed by qualified pathologists, but the number of experts is not specified.

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

• Not applicable for a non-AI physical device validation. Adjudication methods are typically for subjective assessments, like image interpretation in AI studies. The tests described are objective (e.g., measurement of tensile strength, electrical output, presence/absence of foreign body reaction by pathologists).

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 device is a physical tissue expander; it does not involve human readers interpreting images, nor does it have an AI component to assist them.

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

• Not applicable. This is a physical device, not an algorithm. The "Port Locator" is an accessory device that functions using RFID, not an AI algorithm.

7. The type of ground truth used:

• For Mechanical, Sterilization, Electrical/EMC, and MR Conditional Testing: Ground truth is defined by established engineering and medical device standards (e.g., ASTM, ISO, IEC, FCC) which specify acceptable performance ranges and test methodologies.
• For Biocompatibility: Ground truth is established by ISO 10993 standards and FDA guidance, relying on recognized biological reactions and toxicology principles.
• For Animal Testing: Ground truth is based on histopathological evaluation by qualified experts (pathologists) determining typical vs. adverse tissue responses.

8. The sample size for the training set:

• Not applicable. This is a physical medical device, not an AI algorithm that requires a training set.

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

• Not applicable. As above, no training set for an AI algorithm is involved.

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