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Durex latex condom is used for contraception and for prophylactic purposes (to help prevent pregnancy and the transmission of sexually transmitted disease).

This condom is made of a natural rubber colored latex sheath, which completely covers the penis with a closely fitted membrane. This device is a parallel sided, teat ended, flavored condom and is designed to conform to national and international voluntary standards, including ISO 4074, EN600 and ASTM D3492. The condom is offered in Chocolate, Chocolate/coconut. Chocolate/peppermint, and Chocolate/orange.

This 510(k) submission for the Durex Flavored Latex Condom does not contain the detailed study information typically found for AI/ML-based diagnostic devices. This is a premarket approval for a physical medical device (condom) and therefore, the acceptance criteria and study design are related to manufacturing standards and physical performance tests, rather than algorithmic performance.

However, I can extract the relevant information based on the typical requirements for such devices, even if it's not presented in the same format as for an AI study.

Here's a breakdown of the information based on your request, applied to the context of a latex condom:

1. Table of Acceptance Criteria and Reported Device Performance

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

This information is not provided in a typical AI/ML sense but rather refers to manufacturing quality control.

• Sample Size: Not explicitly stated as a single "test set" sample size. Condoms are produced in large batches, and statistical sampling methods (e.g., AQL - Acceptable Quality Level) are used for quality control testing of physical attributes from each batch. The actual number of condoms tested would depend on the batch size and the specific testing protocol (e.g., for burst tests, freedom from holes).
• Data Provenance: The testing is implied to be conducted by the manufacturer, SSL Americas, and its manufacturing partners (LS Rubber SDN and Thai Nippon Rubber Industry). It's a prospective internal quality control process during and after manufacturing.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

In this context, "experts" are not human readers interpreting images, but rather the recognized standards bodies and material scientists/engineers who established the test methods and performance specifications for condoms.

• Number of Experts: Not applicable in the sense of AI ground truth. The "ground truth" for condom quality is defined by the established national and international standards (ISO, EN, ASTM).
• Qualifications of Experts: These standards are developed by committees of material scientists, engineers, medical professionals, and regulatory experts.

4. Adjudication Method for the Test Set

Not applicable in the sense of AI human reader adjudication. Product conformance to specifications is determined directly by objective physical and chemical tests against predefined limits set by the standards.

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 as this is a physical medical device, not an AI/ML-based diagnostic or assistive technology. There are no "human readers" interpreting "cases" with or without "AI assistance."

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Not applicable. There is no algorithm being tested. The device is a standalone physical product.

7. The Type of Ground Truth Used

The ground truth is based on established national and international voluntary standards for latex condoms (e.g., ISO 4074, EN 600, ASTM D3492) regarding physical properties, material composition, and performance. In addition, FDA regulation 21 CFR 801.435 defines the ground truth for expiration dating.

8. The Sample Size for the Training Set

Not applicable. There is no concept of a "training set" for physical manufacturing of condoms. The manufacturing process itself is akin to a "training" process, where continuous monitoring, process controls, and adjustments are made to ensure consistent product quality, but it's not a dataset in the AI sense.

9. How the Ground Truth for the Training Set Was Established

Not applicable. As above, there's no "training set." The "ground truth" for manufacturing quality is established by adhering to Good Manufacturing Practices (GMP) and the quality control parameters derived from the aforementioned international standards and internal specifications.

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