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510(k) Data Aggregation

K Number

K941505

Device Name

VENTANNA ANTI-CD3 PRIMARY ANTIBODY

Manufacturer

VENTANA MEDICAL SYSTEMS, INC.

Date Cleared

1996-05-30

(793 days)

Product Code

Regulation Number

Type

Panel

Reference & Predicate Devices

N/A

Predicate For

N/A

Intended Use

Not Found

Device Description

Ventana Medical Systems, Inc. developed Anti-CD3 (Clone UCHT-1) for use on the Ventana ES automated immunohistochemistry system.

AI/ML Overview

Here's an analysis of the provided text, extracting the requested information about acceptance criteria and the supporting study:

The provided text describes a study for Ventana's Anti-CD3 (Clone UCHT-1) for use on the Ventana ES automated immunohistochemistry system.

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

Acceptance Criteria	Reported Device Performance
Specificity: Appropriate staining of cells of lymphoid origin and no staining of cells of non-lymphoid origin.	Shown with appropriate staining of cells of lymphoid origin and no staining of cells of non-lymphoid origin. Agrees with published data.
Sensitivity: Consistent staining of T cell lymphomas and appropriate staining of normal lymphoid tissue.	Consistent staining of 8 of 9 T cell lymphomas, and appropriate staining of normal lymphoid tissue. Sensitivity is dependent on tissue processing and slide preparation.
Negative Control: Negative results for negative control tissues.	Negative control tissue was all negative. Negative control run with each tissue gave negative results.
Inter-run Reproducibility: Consistent staining intensity across different runs.	Mean staining intensity and standard deviation of 4.00 ± 0.00 across 10 different instrument runs for samples of the same tissue.
Intra-run Reproducibility: Consistent staining intensity within a single run.	Mean staining intensity and standard deviation of 4.00 ± 0.00 for 10 samples of the same tissue within one run.
Background Staining: No inappropriate background staining.	No inappropriate staining of the tissues in this study.
Staining Pattern: Staining in the plasma membrane of cells.	Staining occurred in the plasma membrane of cells from normal tonsil, thymus, and blood.

2. Sample sized used for the test set and the data provenance

Sample Size:
- Normal samples: Number not explicitly stated, but "normal tonsil, thymus and blood" are mentioned.
- Pathologic samples: "8 of 9 T cell lymphomas" are specifically mentioned. The total number of pathologic samples beyond T cell lymphomas is not specified.
- "10 different instrument runs" and "10 samples of the same tissue within one run" were used for reproducibility testing, implying at least 10 tissue samples were involved in these specific tests.
Data Provenance:
- Country of Origin: Not specified.
- Retrospective or Prospective: "Samples were obtained from excess tissues obtained for reasons other than the present study." This indicates a retrospective collection of tissue samples.

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

Number of Experts: Not explicitly stated, but "a qualified pathologist" (singular) is mentioned as evaluating the slides. This suggests one primary expert.
Qualifications of Experts: "A qualified pathologist." Further detail (e.g., years of experience, specific sub-specialty) is not provided.

4. Adjudication method for the test set

Adjudication Method: None explicitly stated. The evaluation was done by "a qualified pathologist," implying a single assessment without a multi-reader review or adjudication process.

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

No. This study is a performance evaluation of an immunohistochemistry reagent and automated system, not an AI-assisted diagnostic device. Therefore, an MRMC comparative effectiveness study involving human readers with/without AI assistance is not applicable and was not performed.

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

Yes, in essence. This study evaluates the standalone performance of the Anti-CD3 (Clone UCHT-1) reagent and the Ventana ES automated system when processed for evaluation by a pathologist. While a human (pathologist) interprets the results, the study's focus is on the device's ability to correctly stain the tissues, rather than the pathologist's diagnostic performance. The "algorithm" here would be the staining protocol and reagent performance, which is assessed directly.

7. The type of ground truth used

Expert Consensus/Pathological Diagnosis: The ground truth for the samples (e.g., T cell lymphomas, normal lymphoid tissue) appears to be established by prior pathological diagnosis or the inherent nature of the normal tissue. The pathologist then evaluates the staining against this established tissue identity. The published data by Reinherz, E. L., and S. F. Schlossman also serves as a reference for expected specificity.

8. The sample size for the training set

Not applicable. The provided text describes a verification/validation study for a medical device (an IHC reagent and automated system), not a machine learning algorithm. Therefore, there is no explicit "training set" in the context of AI/ML. The development of such a reagent would involve internal R&D and optimization, but these preclinical activities are not described as a "training set" in this summary.

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

Not applicable, as there is no training set as defined in AI/ML contexts. The reagent's development would be based on scientific understanding of antibody-antigen interaction and iterative testing by experts, but this is distinct from establishing ground truth for an AI training dataset.

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