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The Rubicor EnCapsule™ Breast Biopsy Device is intended for diagnostic sampling of breast tissue during breast biopsy procedures. It is to be used for diagnostic purposes only and is not intended for therapeutic uses.

The Rubicor EnCapsule™ Breast Biopsy Device is indicated to provide breast tissue samples for diagnostic sampling of breast abnormalities. It is designed to provide breast tissue for histologic examination with a partial or complete removal of the imaged abnormality.

The extent of histological abnormality cannot be reliably determined from its mammographic appearance. Therefore, the extent of removal of the imaged evidence of an abnormality does not predict the extent of removal of a histologic abnormality (e.g. malignancy). When the sampled abnormality is not histologically benign, it is essential that the tissue margins be examined for completeness of removal using standard surgical procedures.

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The provided text describes a 510(k) summary for the Rubicor EnCapsule™ Breast Biopsy Device. However, the document does not contain a detailed study proving the device meets specific acceptance criteria for performance metrics analogous to those found in AI/ML device submissions (e.g., sensitivity, specificity, AUC).

Instead, this submission is for a physical medical device (an electrosurgical device for breast biopsies) and its substantial equivalence determination is based on a comparison to a predicate device and "in-vitro testing." The typical acceptance criteria for such a device would relate to its safety, effectiveness in sampling tissue, and functionality, rather than diagnostic accuracy metrics.

Here's a breakdown of the information that is available based on your request, highlighting what is missing due to the nature of the device and submission:

Acceptance Criteria and Reported Device Performance

Given the nature of this physical biopsy device, the "acceptance criteria" discussed are largely related to the device's functional performance and equivalence to a predicate, rather than diagnostic accuracy.

Study Details (as far as applicable to this type of device submission)

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not specified. The submission refers to "in-vitro testing." For a physical device, this would typically involve testing the device's mechanical, electrical, and functional attributes in a controlled environment, often not involving human patient data or a "test set" in the diagnostic AI sense.
   • Data Provenance: The testing was "in-vitro," meaning in a controlled lab environment. No information on country of origin or whether it was retrospective/prospective is provided, as it's not applicable to in-vitro testing of a physical device.

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

   • Not applicable. As this is not an AI/ML diagnostic algorithm, there is no "ground truth" established by experts in the context of diagnostic accuracy for a test set. In-vitro testing for a physical device might involve engineers or technicians evaluating device function against specifications.

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

   • Not applicable. There is no diagnostic "test set" or adjudication process described as would be for an AI/ML algorithm.

4. 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 biopsy device, not an AI diagnostic tool designed to assist human readers.

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

   • Not applicable. This is a physical biopsy device, not a standalone AI algorithm. It is used by a human practitioner to obtain tissue samples.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Not applicable. For in-vitro testing of a physical device, the "ground truth" would relate to engineering specifications and functional output (e.g., successful tissue capture, integrity of the captured sample), not clinical diagnostic outcomes like pathology.

7. The sample size for the training set:

   • Not applicable. This is a physical device; there is no "training set" in the context of machine learning.

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

   • Not applicable, as there is no training set.

Summary regarding the absence of detailed metrics common for AI/ML devices:

This 510(k) summary is for a physical medical device (a breast biopsy device) whose primary function is to collect tissue samples. The "test results" and "performance" section specifically refer to "in-vitro testing" demonstrating the device is "safe and effective for their intended function" and "substantially equivalent" to a predicate device.

The type of data and criteria you've asked for (e.g., sensitivity, specificity, expert ground truth, MRMC studies) are characteristic of diagnostic software or Artificial Intelligence/Machine Learning (AI/ML) devices. For a physical device like the Rubicor EnCapsule™, the "performance" typically refers to mechanical, electrical, and functional safety and efficacy, often assessed through bench testing and comparison to existing, legally marketed devices. The document does not provide any clinical study results demonstrating diagnostic accuracy metrics because the device itself is a tool for tissue acquisition, not a diagnostic algorithm. The diagnosis is made by a pathologist examining the tissue obtained using the device.

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