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510(k) Data Aggregation
(144 days)
The EkoSonic® Endovascular System is indicated for the:
- · Ultrasound facilitated, controlled and selective infusion of physician-specified fluids, including thrombolytics, into the vasculature for the treatment of pulmonary embolism.
- · Infusion of solutions into the pulmonary arteries.
- · Controlled and selective infusion of physician-specified fluids, into the peripheral vasculature.
The EkoSonic Endovascular System consists of an EkoSonic Device and the Control Unit 4.0 (CU 4.0). The EkoSonic Device consists of a disposable infusion catheter with removable ultrasound core. The infusion catheter contains multiple side holes distributed over the length of the treatment zone. The ultrasound core contains up to 30 ultrasound elements, evenly spaced over the treatment zone. Thermal sensors in the treatment zone monitor catheter temperature. The CU 4.0 generates and controls the delivery of radiofrequency energy to the ultrasound core while monitoring and controlling the temperature of the treatment zone.
The provided text describes a 510(k) premarket notification for the EkoSonic® Endovascular System with Control Unit 4.0. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with clinical endpoints and detailed acceptance criteria for an AI/ML-driven medical device. Therefore, much of the requested information regarding acceptance criteria, sample sizes for test/training sets, ground truth establishment, expert qualifications, and MRMC studies is not present in the provided text.
Based on the information provided, here's what can be extracted:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implied to be that the new Control Unit 4.0 (CU 4.0) functions as intended and is substantially equivalent to the predicate device, the EkoSonic Control Unit (PT-3B), without raising new questions of safety or effectiveness. The performance data presented consists of a series of hardware and software tests, all of which "Pass."
| Category | Specific Test | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|---|
| Hardware Testing | System Tip Test | Pass | Pass |
| Secondary Cell Battery Test | Pass | Pass | |
| Reliability Prediction | Pass | Pass | |
| Battery Life Prediction | Pass | Pass | |
| Electrical Safety | Pass | Pass | |
| Hardware Inspection | Pass | Pass | |
| EMC/EMI | Pass | Pass | |
| RF Board Tests | Pass | Pass | |
| System Integration | Pass | Pass | |
| CIC Hardware Tests | Pass | Pass | |
| Shipping Package Tests | Pass | Pass | |
| Design Verification Hardware Tests | Pass | Pass | |
| UI Board Hardware Tests | Pass | Pass | |
| Environmental Tests | Pass | Pass | |
| Power Management Module Tests | Pass | Pass | |
| Software Testing | Periodic Self-Test | Pass | Pass |
| User Interface | Pass | Pass | |
| Firmware Upgrade | Pass | Pass | |
| Event Logging | Pass | Pass | |
| System Connect Validation | Pass | Pass | |
| Single Channel RF | Pass | Pass | |
| Graphing | Pass | Pass | |
| Device Compatibility | Pass | Pass | |
| Dual Channel RF | Pass | Pass | |
| Therapy Support | Pass | Pass | |
| Workflow | Pass | Pass | |
| System Startup | Pass | Pass | |
| Processor Communication | Pass | Pass | |
| Constant Parameter | Pass | Pass | |
| Source Code Inspection | Pass | Pass | |
| AC and Battery Power | Pass | Pass |
2. Sample size used for the test set and the data provenance
The document does not specify a "test set" in the context of a typical AI/ML algorithm evaluation (e.g., a set of medical images or patient data). The evaluation appears to be based on engineering verification and validation testing of the hardware and software components of the device itself. Therefore, sample sizes for data (e.g., patient data) are not applicable or provided. The data provenance is internal testing performed by the manufacturer, EKOS Corporation, Inc.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. This submission is for a hardware/software device modification that does not involve diagnostic interpretation using AI/ML with a ground truth established by experts.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. The tests performed are engineering-based, not clinical evaluations requiring adjudication of expert opinions.
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. An MRMC study is not mentioned as this device is a continuous flush catheter system, not an AI-assisted diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This concept is not applicable. The device is a physical system (catheter, control unit) that performs a therapeutic function (ultrasound-facilitated infusion), not a standalone algorithm. The "performance" refers to the device's functional integrity as a system.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Not applicable. The "ground truth" for the engineering tests performed would be the design specifications and expected functional behavior of the hardware and software components.
8. The sample size for the training set
Not applicable. This document describes a device modification, not the development or training of an AI/ML algorithm.
9. How the ground truth for the training set was established
Not applicable. There is no mention of a "training set" in the context of AI/ML algorithm development.
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