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Matrix Pre-Treatment Controller is designed to safely and effectively provide monitoring and control of the pre-treatment components for water purification for dialysis applications.

These pre-treatment components include media-tanks with controllable valves (for example, but not limited to: multimedia depth filters, filox media filters, water softeners, dealkalizers, organic scavengers, and carbon media filters), city booster pump, ultra-violet light dump valve, blend-valve, and the main water shut-off valve.

It monitors water pressures, water temperature, Delta-P pressure between two points), water flowrate, water pH, empty bed contact time for carbon media filters, and the status of the media-tanks' valve cycles. This operational data is displayed on a HMI-touch screen along with trends, alarms, and events.

It controls the regeneration and backwash cycles for media tanks, the city booster pump, the ultra-violet light dump valve for cooling purposes, the blend-valve to allow greater flow during backwash and regeneration, and the main water shut-off valve if a leak is detected.

Although it is designed to work with Better Water LLC equipment, it can be used with non-Better Water equipment provided certain components are added or replaced to ensure compatibility.

The Matrix Pre-Treatment Controller is a control box which provides control and monitoring of the pretreatment system. This includes the city water entering into the water treatment system, as well as the various pre-treatment components providing filtration and treatment.

The unique feature of this Controller is the digital touch-screen (HMI – human machine interface, OIT - operator interface terminal). This displays various system pressures, water temperature, alarms, and messages related to the pre-treatment system, and allows for touch-screen control and setup of the pre-treatment components.

This document describes the regulatory submission for the Matrix Pre-Treatment Controller, a device primarily for monitoring and controlling aspects of water purification systems used in hemodialysis. The information provided is a 510(k) Premarket Notification summary, which focuses on demonstrating substantial equivalence to legally marketed predicate devices, rather than an independent clinical study demonstrating a specific acceptance criterion for a medical device's performance (e.g., accuracy of a diagnostic tool).

Therefore, the prompt's request for "acceptance criteria and the study that proves the device meets the acceptance criteria," particularly regarding metrics like sensitivity, specificity, or reader performance, cannot be fully addressed using the provided text. This document is for a Class II device (water purification system component) and focuses on engineering and safety standards, and functional equivalence, not on clinical performance metrics typically associated with AI/diagnostic devices.

However, I can extract and interpret the available information relative to the spirit of the request, focusing on how the device's functional performance was "proven" for regulatory purposes.

Understanding "Acceptance Criteria" in this Context

For this device, "acceptance criteria" are primarily related to its functional specifications, safety features, and compliance with relevant standards and regulations, rather than diagnostic accuracy or human-AI performance. The "study" proving it meets these is the design validation and software validation testing.

1. Table of Acceptance Criteria (Functional/Safety) and Reported Device Performance

Study Proving Device Meets Acceptance Criteria:

The "study" in this context refers to the Design Validation and Software Validation testing performed by the manufacturer.

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

• Sample Size: The document does not specify a numerical "sample size" in the conventional sense of a clinical trial (e.g., number of patients/cases). Instead, it states that "Detailed Software Validation and Design Validation were performed with the Pre-Treatment Controller installed onto a Pre-Treatment Rack." This implies testing of one or more actual devices under various operational scenarios.
• Data Provenance: The testing was "in Better Water's water room." This indicates an in-house, controlled laboratory/simulation environment, likely in the USA (given the manufacturer's address in Smyrna, TN). The testing conditions were "under realistic conditions," "connected to various pre-treatment components and a Reverse Osmosis (RO) machine and operated as part of a complete water system, under power and connected to the incoming water supply." This would be best described as prospective, in-house functional and safety testing.

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

• This type of device validation (functional, safety, and software) typically does not involve medical "experts" (like radiologists) establishing ground truth in the way a diagnostic AI would. The "ground truth" here is the correct functional operation of the system according to its design specifications and compliance with engineering and safety standards.
• The validation would have been performed by qualified engineers, testers, and quality assurance personnel from Better Water LLC, ensuring the device met its design inputs. Their qualifications are implicit in the quality system (21 CFR Part 820) under which the device was developed. No specific number or qualifications are stated for "experts" establishing ground truth in the clinical sense.

4. Adjudication Method for the Test Set:

• Not applicable in the context of this functional/software validation. Adjudication methods like 2+1 or 3+1 are used for human reader agreement on clinical findings (e.g., lesion detection), which is not the nature of this device's testing. The "adjudication" is essentially the manufacturer's internal quality system sign-off that the test results confirm the device meets design specifications and relevant standards.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

• No. An MRMC study is relevant for diagnostic devices where human readers interpret patient data (e.g., medical images) with and without AI assistance. This device is a water purification system controller and does not involve human interpretation of clinical cases or AI assistance for such interpretation.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

• Yes, in spirit. The testing described ("Detailed Software Validation and Design Validation were performed...") evaluates the device's functional performance as a standalone automated controller. While a human touch-screen interface (HMI) is part of the device, the core "algorithm" (control logic) operates automatically based on sensor inputs and pre-programmed parameters. The validation confirms that this automated operation meets specifications. The concept of "standalone" for this device relates to its automated control and monitoring functions without human intervention in the continuous operational process, beyond initial setup and response to alarms.

7. The Type of Ground Truth Used:

• The "ground truth" was engineering specifications, functional requirements, and safety standards. The testing verified that the device's outputs (e.g., digital readings, control actions, alarms) matched the expected behavior based on calibrated inputs and defined specifications. For example, if a pressure sensor reading was 10 PSI, the "ground truth" was that the device should display 10 PSI and trigger an alarm if that value was outside a set threshold. Compliance with standards like IEC 60601-1 (medical electrical safety) and IEC 62304 (medical device software lifecycle) served as ground truth for safety and software quality.

8. The Sample Size for the Training Set:

• This device is not an AI/machine learning device in the sense that it requires a "training set" of data to learn patterns. It's a deterministic control system. Therefore, the concept of a "training set" as understood for AI models is not applicable. The device operates based on pre-programmed logic, parameters, and algorithms derived from engineering principles and standards, not from statistical learning on a dataset.

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

• As there is no "training set" for this deterministic control device, this question is not applicable. The "ground truth" for its development was established through engineering design, requirements definition, and adherence to relevant industry and regulatory standards.

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