(14 days)
The EnSite System is indicated for patients for whom electrophysiology studies are indicated.
When used with the EnSite* Catheter, the EnSite System is intended to be used in . the right atrium of patients with complex arrhythmias that may be difficult to identify using conventional mapping systems alone.
OR
When used with the EnSite NavX™ Surface Electrode Kit, the EnSite System is . intended to display the position of conventional electrophysiology catheters in the heart.
The EnSite System is a computerized storage and display system for use in electrophysiology studies of the human heart. The system consists of a console workstation, patient interface unit, and an electrophysiology mapping catheter or surface electrode kit.
This 510(k) summary does not contain the level of detail necessary to answer all the questions about acceptance criteria and the study proving the device meets them as typically presented for AI/ML-based medical devices. This document predates the widespread use of sophisticated AI/ML in medical devices and therefore focuses on traditional medical device validation.
Based on the provided information, here's what can be extracted and what cannot:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state quantitative acceptance criteria or detailed performance metrics in the format requested for an AI/ML device. Instead, it relies on a qualitative assessment of "safety and effectiveness" compared to a predicate device.
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Safety | "as safe as the previously marketed device" |
| Effectiveness | "as effective as the previously marketed device" |
| No new safety/effectiveness issues | "does not raise any new issues of safety and effectiveness" |
| Bench and User Tests | "The changes made to the EnSite System underwent a battery of bench and user tests. Device validation testing was conducted in accordance with in-house procedures." (Specific results not provided) |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided in the document. The abstract mentions "bench and user tests," but details on sample size, data type, or provenance are omitted.
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 information is not provided. Given the nature of the device (an electrophysiology cardiac mapping system and its components), ground truth would likely be established through physician observation during electrophysiology studies, but the specifics are absent.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided.
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
A multi-reader multi-case (MRMC) comparative effectiveness study as typically understood for AI/ML evaluation was not performed or reported. The document describes a traditional medical device submission based on substantial equivalence to a predicate device, not an AI efficacy study. The concept of "human readers improve with AI vs without AI assistance" is not applicable here as it is not an AI/ML device in the modern sense.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This concept is not applicable to this device. The EnSite System is a computerized storage and display system that supports electrophysiology studies, not a standalone AI algorithm producing diagnoses or interpretations without human involvement. Its "performance" is inherently linked to its use by an electrophysiologist in conjunction with catheters or surface electrodes.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
The document does not specify the type of ground truth. For an electrophysiology mapping system, "ground truth" would likely relate to accurate identification and visualization of cardiac activation patterns and catheter positions, validated through clinical observation and established electrophysiological principles, rather than pathology or outcomes data in the traditional sense of a diagnostic AI device.
8. The sample size for the training set
This information is not provided and is not applicable in the context of this traditional medical device submission. The device is not an AI/ML system that undergoes "training" in the computational sense.
9. How the ground truth for the training set was established
This information is not provided and is not applicable.
§ 870.1425 Programmable diagnostic computer.
(a)
Identification. A programmable diagnostic computer is a device that can be programmed to compute various physiologic or blood flow parameters based on the output from one or more electrodes, transducers, or measuring devices; this device includes any associated commercially supplied programs.(b)
Classification. Class II (performance standards).