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The Divas Dual Pleasure condom is used for contraception and for prophylactic purposes to help prevent pregnancy and the transmission of sexually transmitted diseases.

This condom is made of a natural rubber latex sheath, which closely fitted membrane. The device is shaped, teat ended, lubricated condom, which is lubricated with a water based warming lubricant. The device is designed to conform to national and international voluntary standards, including ISO 4074 and ASTM D3492, except where noted.

The provided document describes a 510(k) summary for the "Divas Dual Pleasure Male Latex Condom with Warming Lubricant." It details the device's description, intended use, technological characteristics, and its substantial equivalence to predicate devices based on regulatory standards. However, the document does not contain any information regarding acceptance criteria or a study that proves the device meets specific performance criteria in the context of typical medical device performance studies (e.g., sensitivity, specificity, accuracy for an AI/diagnostic device).

Instead, this submission is for a condom, and its acceptance is based on conformance to established voluntary standards for condoms and lubricants.

Therefore, I cannot provide the requested information. The document focuses on regulatory compliance and substantial equivalence to legally marketed predicate devices, rather than a clinical performance study with defined acceptance criteria for metrics like accuracy, sensitivity, or specificity.

To address the specific points you requested, based on the absence of such information in the provided text:

1. A table of acceptance criteria and the reported device performance: This information is not present in the provided document. The device's performance is gauged by its adherence to existing voluntary standards (ISO 4074 and ASTM D3492 for condoms, and conformity with predicate lubricant characteristics).
2. Sample size used for the test set and the data provenance: Not applicable/Not provided for a performance study. The document refers to manufacturing according to standards, implying testing of product batches in accordance with those standards, rather than a "test set" for a diagnostic algorithm.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable/Not provided. Ground truth is not established by experts in this context; rather, it's defined by established engineering and material standards for condoms and lubricants.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable/Not provided.
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 is not an AI/diagnostic device.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. This is not an AI/diagnostic device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For this type of device (condom), the "ground truth" would be the engineering specifications and performance requirements outlined in the voluntary standards (ISO 4074 and ASTM D3492) that the product is designed to meet (e.g., burst pressure, freedom from holes, dimensions, lubricant properties).
8. The sample size for the training set: Not applicable. This is not an AI/diagnostic device.
9. How the ground truth for the training set was established: Not applicable.

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

The condom is made of a natural rubber latex sheath, which completely covers the penis with a closely fitted membrane. The device is a shaped, teat ended, lubricated condom which is lubricated with a water based warming lubricant.

This document is a 510(k) premarket notification for a Durex Male Latex Condom with Warming Lubricant. It does not describe a study that proves the device meets acceptance criteria in the way a medical device AI/algorithm submission would. Instead, it demonstrates substantial equivalence to predicate devices based on conformity to existing standards and physical testing.

Here's an analysis of the provided text in the context of your request, highlighting what is and isn't present:

This submission is for a physical medical device (condom with lubricant), not an AI/algorithm. Therefore, many of the requested criteria regarding AI performance studies (e.g., sample size for test set, number of experts, adjudication method, MRMC studies, standalone performance) are not applicable or described in the provided text.

However, I can extract the relevant information regarding the device's conformance to standards and the basis for its safety and effectiveness.

Acceptance Criteria and Reported Device Performance (as much as can be inferred):

Explanation of the Study and Device Conformance (where applicable):

1. A table of acceptance criteria and the reported device performance
   See table above. The document states, "The device is designed to conform to national and international voluntary standards, including ISO 4074 and ASTM D3492, except where noted." And for the finished product, "Physical testing and release testing of the finished product revealed results in conformance with required specifications."

2. Sample size used for the test set and the data provenance
   Not explicitly stated in the provided text. For a physical device, testing involves material properties and functional tests, often using batches or statistical samples, which are not detailed here.

3. 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/algorithm submission requiring expert-established ground truth. Conformance to standards is the primary "ground truth" for this type of device.

4. Adjudication method for the test set
   Not applicable as this is not an AI/algorithm submission.

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 is a physical device, not an AI/algorithm.

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 AI/algorithm.

7. The type of ground truth used
   The "ground truth" for this device's acceptance is its conformance to established national and international voluntary standards (ISO 4074 and ASTM D3942) and its physical/release testing specifications.

8. The sample size for the training set
   Not applicable. There is no AI/algorithm training set for this physical device.

9. How the ground truth for the training set was established
   Not applicable. There is no AI/algorithm training set. The "ground truth" for the device's safety and effectiveness is established through its design, materials, manufacturing processes, and testing that demonstrate conformance to recognized engineering and product standards for condoms and lubricants. The substantial equivalence argument relies on comparing these characteristics to legally marketed predicate devices.

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Personal Warming Lubricant is designed to enhance the ease and comfort of intimate activity and is compatible with latex condoms

Personal Warming Lubricant is a non-sterile, clear, nonstaining, non-greasy, high viscosity liquid gel used as a personal lubricant. This product is highly lubricous and may be used with or without a latex condom during intimate sexual activity.

The provided document is a 510(k) Premarket Notification for a Personal Warming Lubricant. The information primarily focuses on establishing substantial equivalence to a predicate device rather than presenting detailed acceptance criteria and a study proving performance against those criteria in the way a medical diagnostic or AI device submission might.

Based on the provided text, here's a breakdown of the requested information, adapted to the nature of this submission:

1. Table of Acceptance Criteria and Reported Device Performance

For this type of device (personal lubricant), the "acceptance criteria" are primarily related to safety, physical/chemical properties, and performance relative to the predicate device, rather than diagnostic accuracy metrics.

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

• Test Set Sample Size: The document does not specify a numerical "sample size" for a traditional test set in the context of device performance testing for diagnostic accuracy. The studies mentioned (Stability, Preservative Effectiveness, Comparison to Predicate Device) would have involved samples of the product itself rather than human subjects or data sets in the way an AI/diagnostic device would. For the "Comparison with Predicate Device," the "test set" would implicitly be the new device and the predicate device being compared.
• Data Provenance: Not applicable in the traditional sense. The studies were non-clinical, involving the product itself.

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

• This information is not provided in the document. For non-clinical product testing like stability or preservative effectiveness, "experts" might be laboratory technicians or chemists, but their number and specific qualifications are not detailed here. For the "perceptual qualities" comparison, it's possible human evaluators were used, but no details are given.

4. Adjudication Method for the Test Set

• This information is not provided and is generally not applicable for this type of non-clinical product submission. Adjudication methods like 2+1 or 3+1 are typically used for establishing ground truth in clinical image or data interpretation studies.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not done. This type of study is relevant for diagnostic devices that involve human interpretation of cases, often with and without AI assistance, to measure changes in human reader performance. This personal lubricant submission does not involve such a scenario.

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

• No, a standalone study was not done. This concept is specific to AI algorithms. This submission is for a physical product, not an algorithm.

7. The Type of Ground Truth Used

• For Stability: The "ground truth" is adherence to predefined physical/chemical specifications over time.
• For Preservative Effectiveness: The "ground truth" is successful inhibition of microbial growth according to established challenge test criteria.
• For Comparison to Predicate Device: The "ground truth" for "acceptable comparison" would be a qualitative and quantitative assessment of physical, chemical, and perceptual attributes aligning closely with the predicate device, as determined by laboratory analysis and potentially human sensory evaluation according to an internal protocol.

8. The Sample Size for the Training Set

• This information is not applicable. There is no "training set" for this type of product submission, as it does not involve machine learning or AI algorithms. Product development might involve various formulation iterations and testing, but these are not considered "training sets" in the AI context.

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

• This information is not applicable as there is no training set mentioned or implied in the context of an AI/ML device.

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