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BD Surgiphor™ Antimicrobial Irrigation System is intended to mechanically loosen and remove debris, and foreign materials, including microorganisms, from wounds.

The subject BD Surgiphor™ Antimicrobial Irrigation System is a terminally sterilized 450 mL aqueous solution for irrigation and debridement of wounds. The device includes one bottle of Surgiphor™ solution (0.5% Povidone Iodine) which is used to loosen and remove wound debris. The mechanical action of fluid moving across the wound provides the mechanism of action and aids in the loosening and removal of debris, and foreign materials, including microorganisms, from wounds. The povidone iodine in the Surgiphor™ solution serves as a preservative to ensure that no unwanted microbial growth occurs in the solution after the bottle is open.

The provided text describes a 510(k) premarket notification for a medical device called the "BD Surgiphor™ Antimicrobial Irrigation System." This submission is based on demonstrating substantial equivalence to a previously cleared predicate device (K213616).

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance:

The core of this 510(k) submission is to demonstrate that a modified device is substantially equivalent to a predicate device, not to establish new performance criteria for a novel device. Therefore, the "acceptance criteria" here are primarily tied to demonstrating that the changes made do not negatively impact the safety or effectiveness of the device compared to the predicate.

The main change in the subject device (K221504) from its predicate (K213616) is the removal of the SurgiRinse™ solution bottle. The device still includes one bottle of Surgiphor™ solution. The manufacturer asserts that the fundamental "mechanism of action" (mechanical loosening and removal of debris and foreign materials, including microorganisms, from wounds through fluid pressure) remains unchanged. The povidone iodine in the Surgiphor™ solution continues to act as a preservative.

Given this context, the acceptance criteria are implicitly met by demonstrating that the modified device performs comparably to the predicate device, specifically by showing that removing the rinse bottle does not introduce new safety or efficacy concerns.

1. Table of Acceptance Criteria and Reported Device Performance:

Since this is a 510(k) for a modified device, the "acceptance criteria" are not reported as specific performance metrics like sensitivity, specificity, or accuracy (as one might see for an AI/ML device). Instead, the acceptance criteria are met by demonstrating that the changes do not degrade performance or safety.

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

The document does not describe a "test set" in the context of an AI/ML algorithm or a clinical trial with human subjects. This 510(k) relies on design verification and validation testing and standards compliance to demonstrate substantial equivalence to a predicate device, given a minor change (removal of one component from a kit).

Therefore, there is no mention of data provenance (country of origin, retrospective/prospective) because the studies are primarily engineering and quality control tests (sterilization, packaging, shelf-life) rather than clinical performance studies on patient data.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

This information is not applicable to this 510(k) submission. Ground truth establishment by experts (e.g., radiologists) is typically relevant for diagnostic AI/ML devices where a clinical reference standard is needed. This device is a physical irrigation system, and its "ground truth" for substantial equivalence is derived from a combination of:

• The established performance and safety of its predicate device.
• Laboratory testing (sterilization, packaging) against recognized standards.
• Engineering assessment that a structural change (removing a bottle) does not alter intended use or introduce new risks.

4. Adjudication Method for the Test Set:

This information is not applicable for the same reasons as #3. Clinical adjudication methods are not relevant for the type of testing described.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

This information is not applicable. An MRMC study is relevant for evaluating the performance of diagnostic aids, particularly AI/ML algorithms, in how they affect human reader performance. This device is an irrigation system, not a diagnostic tool, and the submission is focused on physical and chemical equivalence and safety, not on human interpretation of outputs.

6. Standalone (Algorithm Only) Performance:

This information is not applicable. This submission is for a physical medical device, not an algorithm.

7. Type of Ground Truth Used:

The "ground truth" in this context is established through:

• Engineering specifications and design verification: Ensuring the physical and chemical properties of the device (solution composition, packaging, sterility) remain consistent with safe and effective operation as defined by standards.
• Predicate device's established safety and effectiveness: The fundamental "truth" is that the predicate device was already deemed safe and effective for its intended use, and the current submission argues that the modified device maintains this "truth" despite the change.

8. Sample Size for the Training Set:

This information is not applicable. There is no "training set" as this device does not involve an AI/ML component.

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

This information is not applicable. Since there is no training set, there is no ground truth established for it.

In summary:

The provided FDA letter and 510(k) summary pertain to a physical medical device (an irrigation system) undergoing a minor modification. The "acceptance criteria" and "study" described are primarily related to engineering validation, quality control testing (e.g., sterility, shelf-life), and a comparison to a legally marketed predicate device to demonstrate substantial equivalence. It does not involve the types of studies (e.g., clinical trials, AI/ML performance evaluations) that would typically require the detailed information on test sets, expert readers, or ground truth methodologies for diagnostic or AI-powered devices. The crucial point of this submission is the statement: "The changes do not impact the safety or effectiveness of the subject device [compared to the predicate device]."

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BD Surgiphor™ Antimicrobial Irrigation System is intended to mechanically loosen and remove debris, and foreign materials, including microorganisms, from wounds.

The BD Surgiphor™ Antimicrobial Irrigation System is a 2-step system of aqueous solutions for irrigation and debridement of wounds. The 2-step process includes one bottle of Surgiphor™ Solution (0.5% Povidone Iodine) which is used first to loosen wound debris, and one bottle of SurgiRinse™ Solution (saline solution, USP 99.95%) which is used second to rinse the loosened debris from the wound. The mechanical action of fluid moving across the wound provides the mechanism of action and aids in the removal of debris, and foreign materials, including microorganisms, from wounds. The BD Surgiphor™ Antimicrobial Irrigation System is provided as a two-part terminally sterilized system with 450 mL of each solution. The povidone in the Surgiphor™ Solution serves as a preservative to ensure that no unwanted microbial growth occurs in the solution after the bottle is open.

The provided text describes a 510(k) premarket notification for the "BD Surgiphor Antimicrobial Irrigation System." It focuses on demonstrating substantial equivalence to predicate devices, particularly regarding the addition of "microorganisms" to the device's indications for use.

Based on the provided text, the device is a medical irrigation system, not an AI/ML device. Therefore, the questions about acceptance criteria, study design, and performance related to AI/ML (e.g., sample size, ground truth establishment, expert adjudication, MRMC studies, standalone performance, training sets) are not applicable to this submission.

The document primarily addresses the substantial equivalence of a physical medical device. The "performance testing" section refers to standard medical device testing (e.g., sterility, biocompatibility, packaging, stability, fluid pressure testing for mechanical action), not AI/ML model performance.

Here's a summary of what is provided regarding acceptance criteria and performance, as applicable to this medical device:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't present a formal table of quantitative acceptance criteria for performance in the way one would for an AI/ML device (e.g., accuracy, sensitivity, specificity thresholds). Instead, it states that "Substantial equivalence has been confirmed through performance testing." The performance testing described is primarily focused on demonstrating the physical and chemical properties and safety of the device, rather than a diagnostic or predictive performance metric.

2. Sample size used for the test set and the data provenance:

• Not applicable for an AI/ML context. The tests are for the physical device, not an algorithm processing data.

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 applicable. Ground truth in the context of an AI/ML study does not apply to this physical device.

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

• Not applicable. This relates to expert review of data for AI/ML model ground truth.

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 study type is for evaluating AI assistance in diagnostic tasks.

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

• Not applicable. There is no algorithm being evaluated in this submission.

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

• Not applicable in the AI/ML sense. The "ground truth" for this device's effectiveness relies on the mechanical action of fluid physically removing debris, which is demonstrated through fluid pressure testing and comparison to predicate devices, rather than a diagnostic 'truth'. The claim for "microorganism removal" is based on its mechanical action, not a specific antimicrobial kill claim.

8. The sample size for the training set:

• Not applicable. There is no training set for an AI/ML model.

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

• Not applicable. There is no training set for an AI/ML model.

In summary, the provided document is a 510(k) summary for a physical medical device, not an AI/ML device. Therefore, questions 1 (in the AI/ML context), 2, 3, 4, 5, 6, 7 (in the AI/ML context), 8, and 9 are not addressed in the text as they pertain to AI/ML model development and validation. The acceptance criteria and performance discussions are framed within the context of a medical device's physical, chemical, and sterility properties, and its intended mechanical action, demonstrating substantial equivalence to legally marketed predicates.

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