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Erco-Vac ™ -C is used to create an erection in men with erectile dysfunction and for augmenting of male potency. Erco-Vac TM -C can be used for patients with following conditions: Diabetes Venous leakage Prostatectomy Hypertension Psychogenic condition Impotence due to radiation therapy Spinal cord injury

Erco-Vac ™ -C is based on the same scientific concept as known marketable vacuum constriction devices. Vacuum applied to penis causes blood inflow, engorgement and state of erection. Constriction device preliminary installed on the edge of vacuum chamber is forced to slip off on the root of the penis. By hindering blood outflow, constriction device sustains erection and makes intercourse possible after removal of vacuum chamber.

This document describes the ERCO-VAC™-C external penile rigidity system, which is a vacuum constriction device intended to create erections in men with erectile dysfunction. The submission claims substantial equivalence to several predicate devices.

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

The provided document does not contain specific acceptance criteria with numerical thresholds (e.g., minimum efficacy rate, maximum complication rate) or the results of a formal study evaluating these criteria for ERCO-VAC™-C. Instead, the submission focuses on establishing substantial equivalence based on intended use and technological characteristics compared to predicate devices.

The "reported device performance" is described qualitatively as "superior performance and safety" compared to predicate devices due to novel design elements.

• Transferable air tight penile seal for adjustable girths
• Constriction device with controllable inward radial pressure (Erco-Ribbon™-C)
• Cuff and quick release loop
• Automatic vacuum release
• Transferring device
• Vacuum chamber with a domed closed end, customized to user's penile anatomy |

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

The provided document does not contain information about a test set sample size or data provenance. The submission for K990006 is a 510(k) Pre-Market Notification, which primarily relies on substantial equivalence to legally marketed predicate devices, rather than new clinical study data for device performance.

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)

There is no mention of experts used to establish ground truth for a test set, as no specific test set data is presented.

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

No adjudication method is mentioned, as no test set data requiring adjudication is presented.

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

This device is not an AI-powered diagnostic or assistive tool for human readers. Therefore, an MRMC study comparing human readers with and 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

This device is a medical device (external penile rigidity system), not an algorithm. Therefore, a standalone algorithm performance study is not applicable. The device's function involves direct user interaction to achieve its intended purpose.

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

Since no clinical study data with a test set is provided, there is no mention of the type of ground truth used. The basis for marketing approval is substantial equivalence to predicate devices, meaning the intended use and technological characteristics are similar enough to existing devices.

8. The sample size for the training set

The document does not mention a training set sample size, as this is not a machine learning model.

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

Not applicable, as this is not a machine learning model.

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