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The CoreLite device is a cabinet X-ray system used to provide digital X-ray images of specimens from various anatomical regions in order to allow rapid verification that the correct tissue has been excised during the biopsy procedure.

Performing the verification in the same room as the procedure or nearby improves workflow, thus reducing the time the patient needs to be under examination.

The CoreLite Specimen Radiography System is a self-contained digital imaging system for verification of breast biopsy specimens at the point of care which enables the procedure to be completed faster. The system is comprised of the x-ray cabinet and the PC with DICOM compliant software which provides the user interface, means to enter patient details (either directly or from a DICOM Modality Worklist, if available) and the means to acquire, review and save or transmit DICOM images to the Picture Archiving and Communication System (PACS). The cabinet incorporates shielding and interlock circuits to meet requlatory requirements.

The provided FDA 510(k) summary for the Cirdan Imaging Limited CoreLite device indicates that it is a cabinet X-ray system for verifying breast biopsy specimens. It states that the device was benchmarked against a predicate device (Faxitron CoreVision Digital Specimen Radiography (DSR) System) in a clinical setting to demonstrate substantial equivalence. However, the document does not provide detailed acceptance criteria or the specifics of a study proving the device meets particular quantitative performance metrics with associated sample sizes, ground truth establishment, or expert involvement as typically requested.

Based on the provided text, here is the information that can be extracted and a clear indication of what is not present:

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

The document does not provide a table of explicit quantitative acceptance criteria for image quality or clinical performance, nor does it present specific numerical performance results. It only states a qualitative outcome for the comparison study.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Sample size for test set: Not specified. The document only mentions "a clinical setting" for benchmarking.
• Data provenance: Not specified.

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 specified. The document states "the results were judged to be equivalent to the predicate," implying expert judgment, but does not detail the number or qualifications of these experts.

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

Not specified.

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

A multi-reader, multi-case (MRMC) comparative effectiveness study was not explicitly mentioned. The study described was a benchmarking of the device (CoreLite) against a predicate device, focusing on image quality and clinical equivalence. There is no mention of AI assistance or its effect on human readers. The CoreLite system is described as a digital imaging system, not an AI-powered diagnostic aid.

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

The CoreLite is an X-ray system itself, not an algorithm. Therefore, a "standalone algorithm-only" performance study is not applicable in the typical sense of AI algorithms. The system's performance revolves around its ability to produce digital X-ray images for verification. "Non-Clinical Testing included image quality tests with accredited phantom test objects and High Contrast resolution targets" which could be considered a standalone technical performance assessment of image quality.

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

The document implies a qualitative assessment against the predicate device in a clinical setting. It states "the results were judged to be equivalent to the predicate." This suggests the ground truth was likely established by comparison to the established predicate device's performance, potentially through expert comparison of images or assessment of successful specimen verification. Specific details about the ground truth (e.g., pathology confirmation of tissue presence) are not provided.

8. The sample size for the training set

Not applicable. The CoreLite is an X-ray imaging system, not a machine learning algorithm that requires a training set.

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

Not applicable, as it's not an AI/ML device requiring a training set.

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