Search Results

The Ultra ICE Plus - PI 9 MHz Peripheral Imaging Catheter is indicated for patients with vascular occlusive disease for which angioplasty, atherectomy, the placement of stents, or other intervention is contemplated.

Ultra ICE Plus - PI 9 MHz Peripheral Imaging Catheter is intended for use with the Boston Scientific iLab™ equipment and motor drive unit, MDU5 PLUS™. When used together, the catheter, motor drive unit (MDU), and iLab equipment form a complete imaging system that allows for ultrasonic visualization of peripheral intravascular structures.

The catheter consists of two main components: the catheter body and the imaging core.

The catheter body consists of three sections: the braided proximal shaft, single lumen mid-shaft, and the sonolucent distal tip. The catheter body comprises the usable length of the catheter (110 cm).

The braided proximal shaft provides the pushability to the catheter and serves as a lumen to the imaging core. The mid-shaft provides a flexible transition between the stiffer proximal shaft and the acoustically transparent distal tip. The distal tip serves as the imaging window and houses a septum situated between the inner lumen and the atraumatic rounded tip of the catheter. The self-sealing septum serves as the distal-flush entry point, as the catheter is flushed with water prior to use. This provides the acoustic coupling media required for ultrasonic imaging.
The imaging core consists of a proximal hub assembly, a rotating drive cable, and a radiopaque tip. The hub assembly provides an electro-mechanical interface between the catheter and the motor drive unit. The drive cable houses the low frequency piezoelectric (PZT) transducer at the distal imaging window.
The PZT transducer and the drive cable rotate independently from the sheath to provide 360° image resolution. The transducer converts electrical impulses sent by the motor drive into transmittable acoustic energy. Reflected ultrasound signals are converted back to electrical impulses, returned to the motor drive unit, and are ultimately processed by the iLab equipment for live visualization of intravascular structures.

The provided text is a 510(k) summary for the Ultra ICE Plus - PI 9 MHz Peripheral Imaging Catheter. It outlines the device's characteristics, intended use, and substantial equivalence to a predicate device based on non-clinical performance data. Since this is a submission for a medical device that relies on physical characteristics and performance rather than an AI/ML algorithm that predicts or classifies, many of the requested categories are not applicable.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't present specific numerical acceptance criteria in a table format, nor does it present specific numerical performance results against those criteria. Instead, it describes general performance categories tested. The primary "acceptance criterion" is demonstrating substantial equivalence to the predicate device.

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

The document describes non-clinical bench testing. It does not specify a "test set" in terms of patient data. The "samples" would be the catheters themselves. The exact number of catheters used for each type of bench test (e.g., how many were fatigue tested, how many were dimensionally checked) is not provided.

• Sample size for test set: Not specified (refers to physical devices, not patient data).
• Data provenance: Not applicable as it's non-clinical bench testing.

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

Not applicable. This is a physical medical device (intravascular ultrasound catheter) where performance is measured using engineering and biological standards, not expert interpretation of images or data to establish a ground truth for an AI/ML algorithm.

4. Adjudication Method for the Test Set:

Not applicable, as it's non-clinical bench testing of a physical device.

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 is not an AI/ML device, and no MRMC study was performed or required. The submission explicitly states "Clinical Performance Data: Not applicable; determination of substantial equivalence is based on an assessment of non-clinical performance data."

6. If a Standalone (i.e. algorithm only without human-in-the loop performance) Was Done:

Not applicable. This is not an AI/ML device.

7. The Type of Ground Truth Used:

For the non-clinical performance tests, "ground truth" refers to established engineering specifications, regulatory standards (e.g., ISO, FDA guidance), and physical measurements. For example:

• Dimensional requirements: Engineering specifications for length, diameter, etc.
• Biocompatibility: ISO 10993-1 standards.
• Acoustic Output: FDA guidance for diagnostic ultrasound systems.
• Sterility: Sterility assurance level (SAL).
• Electrical/Mechanical Safety: IEC 60601-1-2.

8. The Sample Size for the Training Set:

Not applicable. This is not an AI/ML device, so there is no "training set."

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

Not applicable. This is not an AI/ML device.

Ask a specific question about this device

The AcQMap 3D Imaging and Mapping Catheter is intended to be used in the right and left atrial chambers to collect ultrasound data for visualizing the selected chamber and recording electrical impulses in patients with complex arrhythmias that may be difficult to identify using conventional mapping systems alone.

Electrode Recording Catheter/Transducer Ultrasound

The provided text describes the AcQMap 3D Imaging and Mapping Catheter, but it primarily focuses on establishing substantial equivalence to a predicate device rather than presenting a detailed study proving the device meets specific acceptance criteria in the manner an AI/ML device would typically be evaluated (e.g., accuracy, sensitivity, specificity for classification tasks).

However, I can extract the information relevant to a "study" that assessed the device's performance, even if it's not a typical AI/ML validation study. The closest an AI/ML validation study with the criteria you listed comes is the "DDRAMATIC-SVT" clinical study.

Here's the breakdown based on the provided text:

Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state quantitative acceptance criteria for parameters like sensitivity, specificity, or image quality, as it's a 510(k) submission focused on substantial equivalence for a medical device that collects data for imaging and mapping, not an AI/ML diagnostic output. The "performance" is primarily described in terms of the device's ability to safely and effectively perform its intended function.

Study Details (Based on the DDRAMATIC-SVT Clinical Study):

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

   • Sample Size: 84 patients.
   • Data Provenance: Prospective, non-randomized, open-label study conducted at eight clinical sites outside the U.S.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • This information is not provided in the text. The study describes "gathering data to create... dipole density activation maps" and "recording electrical impulses," implying physician interpretation of the generated maps and electrical data, but the number and qualifications of experts for ground truth establishment are not detailed.

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

   • This information is not provided in the text.

4. 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:

   • This was not a MRMC comparative effectiveness study and does not involve AI assistance for human readers in the context described. The device itself is a data collection and imaging tool, not an AI diagnostic algorithm.

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

   • This was not a standalone algorithm performance study. The device's function is to collect data for physicians to interpret. The text states it helps in "identifying complex arrhythmias that may be difficult to identify using conventional mapping systems alone," implying human interpretation of the device's output.

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

   • The text describes the study as demonstrating "safety and performance in gathering data to create right and/or left atrial dipole density activation maps." The "ground truth" for the effectiveness of the device as a data collection tool would implicitly be its ability to generate these maps and record electrical impulses as intended, likely verified by the practicing cardiologists using the system during the study. It's not explicitly tied to a definitive "ground truth" like pathology or a hard clinical outcome in the same way an AI diagnostic would be.

7. The sample size for the training set:

   • This information is not applicable/not provided. The device is a hardware system for data collection and imaging, not an AI/ML model that requires a training set.

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

   • This information is not applicable/not provided as there is no training set for an AI/ML model.

Ask a specific question about this device