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510(k) Data Aggregation
(120 days)
The Hysteroscopy Pump HM6 is intended to provide liquid distension of the uterus during diagnostic and operative hysteroscopy, and to monitor the volume differential between the irrigation fluid flowing into and out of the uterus.
The Hysteroscopy Pump HM6 is a microprocessor controlled device that consists of the following two main components: (1) an irrigation pump unit and (2) and fluid monitoring unit that are to be placed on a cart. The irrigation pump unit is a single roller pump that functions according to the peristaltic system and includes a manmachine interface (MMI) and a touch-screen display. The Hysteroscopy Pump HM6 is to be used with specially designed single use irrigation and outflow tube sets that are supplied sterile. The fluid monitoring unit of the Hysteroscopy Pump HM6 is a microprocessor controlled device, which monitors the amount of delivered irrigation solution and compares it with the volume of the secretions returned to the container. The monitoring enables the surgeon to observe the quantity of fluids left in the patient. The irrigation pump unit of the proposed device indicates any fluid deficit that exceeds the fluid deficit level pre-set by the surgeon. The Hysteroscopy Pump HM6 determines both the inflow and outflow volume by means of two separate scales.
This document describes a 510(k) premarket notification for the Hysteroscopy Pump HM6. The focus of the submission is to demonstrate substantial equivalence to predicate devices, rather than establishing de novo safety and effectiveness through extensive clinical studies with acceptance criteria. Therefore, the information provided is primarily focused on demonstrating the similarity of the new device to existing devices on the market, relying on bench testing for performance.
Here's an analysis of the provided information concerning acceptance criteria and studies:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly present a table of acceptance criteria with corresponding performance data in the traditional sense of a clinical trial proving a specific efficacy or accuracy metric. Instead, the "performance testing" section refers to bench tests and compliance with standards. The key "acceptance criterion" for this 510(k) submission is showing that the device is substantially equivalent to predicate devices and that the modifications do not raise new questions of safety and effectiveness.
The document highlights the following performance aspects and their comparison to the predicate Aquilex System:
| Acceptance Criterion (Implied) | Reported Device Performance (Hysteroscopy Pump HM6) |
|---|---|
| Accuracy of fluid deficit measurement | Improved to = 0% compared to = 10% for the predicate device (Aquilex System) due to a separate scale for inflow volume. |
| Maximum selectable pressure | Identical to the Aquilex System (150 mmHg). |
| Safety features (e.g., active pressure reduction, overpressure warnings, fluid deficit warnings) | Same or similar to predicate devices. |
| Software validation | Performed in accordance with FDA Guidance document "General Principles of Software Validation", with the software considered "Major Level of Concern". |
| Electrical Safety and EMC | Performed in accordance with IEC 60601-1 by an accredited test institute. |
| Design modifications (mechanics, hardware, software) | Results from internal design control process, including risk analysis. Validation and verification testing of system specifications performed as bench tests. |
| Tubing design and material | Identical to the Aquilex System, with minor length changes and addition of a clamp for the outflow tube set. |
| Fluid bag detection feature | New safety feature informs user if fluid in bag is <= 500 ml. |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document primarily describes bench testing and compliance with standards. It does not mention clinical studies involving human subjects or specific "test sets" in that context. Therefore, information regarding human sample size, data provenance (country of origin), or retrospective/prospective nature is not applicable here. The testing would have been conducted internally by the manufacturer or by accredited test institutes.
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)
This is not applicable as the submission relies on bench testing and adherence to standards, not expert-adjudicated clinical data to establish ground truth for a test set.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable, as there is no mention of a human-adjudicated test set.
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 device is a Hysteroscopy Pump, a medical equipment, not an AI-powered diagnostic or interpretive tool that would involve human readers or AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This device is a physical medical pump with integrated software, not a standalone algorithm. Its performance is evaluated through its physical function and software control, not as an isolated algorithm.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
For the performance claims made, the "ground truth" would be established by:
- Bench testing measurements: For fluid volume accuracy, pressure control, flow rates, etc., the ground truth would be precise measurements obtained through calibrated instruments in a controlled laboratory setting.
- Compliance with standards: For electrical safety and EMC, the ground truth is adherence to the specifications outlined in IEC 60601-1.
- Design control documentation: For software validation and mechanical/hardware design, the ground truth is adherence to internal design specifications and risk analyses.
8. The sample size for the training set
Not applicable, as this device's development does not involve machine learning or AI models that require a "training set" of data in the common sense. The software would have been developed through traditional software engineering processes, with testing conducted on the specific device and its components.
9. How the ground truth for the training set was established
Not applicable, as there is no "training set" as understood in machine learning contexts.
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