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510(k) Data Aggregation
(61 days)
Intended for irrigation and fluid warming in laparoscopic procedures, and distention, fluid warming, and volume/deficit measurements in endoscopic procedures within gynecology, and orthopedic disciplines.
FluidSmart® is intended for irrigation, distention, fluid warming, and fluid volume/deficit measurements in endoscopic procedures within gynecology, urology, and orthopedic disciplines. Fluids commonly used include 0.9% saline, lactated ringer's solution, 5% mannitol, 1.5% glycine, and sterile water.
The main unit is mounted on a rolling stand which houses the technology used to warm the fluids and the peristaltic pump used to pressurize the fluids. A touch-screen operated processor provides closed loop controls for fluid warming and pumping. The processor monitors temperature and pressure sensors to adjust warming power and pump speed as required. Load cells attached to the fluid bag hooks and suction canister ring allow the processor to monitor fluid levels pumped to and suctioned from the surgical site.
This document is a 510(k) summary for the FluidSmart device, which is a hysteroscopic insufflator. Such summaries typically focus on demonstrating substantial equivalence to a predicate device rather than providing detailed acceptance criteria and study results in the manner requested for AI/ML devices. Therefore, much of the requested information, which is common in regulatory submissions for AI/ML or novel devices, is not present here.
Here's a breakdown of the available information and what is not present:
1. Table of acceptance criteria and the reported device performance
The document provides a comparative table of technological characteristics to predicate devices, but it does not specify quantitative acceptance criteria for performance benchmarks, nor does it present device performance against such criteria directly in a table format as might be expected for software performance metrics.
Technological Characteristic | Acceptance Criteria (Implicit from Predicate/Study Purpose) | FluidSmart® Reported Performance |
---|---|---|
Fluid Warming Method | Same as Predicate (Infrared heating of fluid) | Infrared heating of fluid |
Pressure Generation Method | Same as Predicate (Peristaltic pump) | Peristaltic pump |
User Interface | Same as Predicate (Touch Screen) | Touch Screen |
Device Type | Same as Predicate (Electro-Mechanical) | Electro-Mechanical |
Maximum Pressure (ureteroscopy procedures) | 300 mmHg (matching secondary predicate at 300-500 mmHg) | 300 mmHg |
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
Not applicable. This device is a hardware device (insufflator, fluid warmer) with associated software for control. The testing involved non-clinical design verification, not analysis of a "test set" of patient data in the context of an AI/ML device.
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/ML device requiring expert ground truth for a test set.
4. Adjudication method for the test set
Not applicable, as this is not an AI/ML device requiring expert adjudication of a 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
No. This is not an AI/ML device and thus an MRMC study is not relevant.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
Not applicable, as this is not an AI/ML algorithm. The device itself (FluidSmart®) is a standalone system.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable, as this is not an AI/ML device. The "ground truth" for a device like this would involve engineering specifications, safety standards, and functional performance benchmarks (e.g., temperature accuracy, pressure regulation accuracy, leak tests), which are evaluated through non-clinical testing.
8. The sample size for the training set
Not applicable, as this is not an AI/ML device with a training set.
9. How the ground truth for the training set was established
Not applicable, as this is not an AI/ML device with a training set.
Summary of the Study Mentioned (Non-Clinical Testing):
The document states:
- Non-clinical testing performed including the following design verification testing:
- Software verification testing.
- Tubing set verification testing.
This indicates that Thermedx conducted internal testing to ensure the software functions as designed and the disposable tubing sets meet their specifications (e.g., integrity, pressure relief valve operation, biocompatibility, and expiration dating, as mentioned elsewhere). No clinical testing was conducted or submitted. The "study" here is essentially internal design verification and validation against engineering specifications and regulatory standards for safety and effectiveness for a medical device.
Key takeaway: This 510(k) submission primarily focuses on demonstrating substantial equivalence to existing predicate devices by comparing technological characteristics and asserting that the minor proposed changes (specifically, increasing maximum pressure for ureteroscopy procedures from 200 mmHg to 300 mmHg) do not raise new questions of safety or effectiveness. It is not an AI/ML device, and therefore, the typical AI/ML study parameters are not applicable or provided.
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