K Number
K980306
Manufacturer
Date Cleared
1998-04-02

(65 days)

Product Code
Regulation Number
892.1000
Panel
RA
Reference & Predicate Devices
AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
Intended Use

MAGNETIC RESONANCE IMAGING WHOLE BODY

Device Description

The modifications include: new imaging sequences: FSAGE, Multishot EPI, Long ETL FSE, SSE, SSV, and diffusion weighted imaging, automated image filtering, MPR, PCA color coded velocity maps, Time Lapse post processing, Real-Time Localizer, double oblique localization, and multiple presaturation.

AI/ML Overview

Here's an analysis of the provided text regarding the Elscint MRI Software Version 3.0, focusing on acceptance criteria and the study proving its performance:

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria CategorySpecific Metric (Software Version 3.0)Predicate Device PerformanceConclusion (Met/Not Met)
Minimum TE2ms4.3msMet (Improved)
Minimum TR7ms11msMet (Improved)
Minimum FOV4cm6.4cmMet (Improved)
Maximum 2D Acquisition Matrix$1024^2$$512^2$Met (Improved)
Maximum 3D Acquisition Matrix$256^3$ or 128x512x512$256^3$ or 128x512x512Met (Equivalent)
Maximum Number of Echoes88Met (Equivalent)
Maximum Number of Slices8080Met (Equivalent)
Minimum Slice Width0.5mm0.7mmMet (Improved)
Safety (SAR, dB/dt, acoustic noise, Bo)Same as predicate devices(Same as predicate devices)Met (Equivalent)
Software Safety HazardsMinimized by design reviews, code reviews, and testing(Not explicitly stated for predicate, assumed safe)Met (Addressed)

Note: The "acceptance criteria" here are implicitly defined by comparison to the predicate devices. The submission claims the device is "substantially equivalent in safety and effectiveness" and that effectiveness is "improved in non-substantial ways." The actual "study" presented is a direct comparison of technical specifications.

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

  • Sample Size for Test Set: Not applicable. The submission does not describe a clinical study with a "test set" of patient data. The effectiveness evaluation is based on a technical parameter comparison to predicate devices, not on a dataset of images or patients.
  • Data Provenance: Not applicable. No clinical data is mentioned.

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

  • Not applicable. No ground truth for a test set was established. The "truth" is based on the technical specifications of the device and its predicate.

4. Adjudication Method for the Test Set

  • Not applicable. There was no test set or expert adjudication process described.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

  • No MRMC comparative effectiveness study was done. The document does not describe human reader performance or the impact of AI assistance. The focus is on the inherent technical capabilities of the MRI system's software version.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

  • Not explicitly described as a "standalone study" in the modern sense of AI algorithm evaluation. However, the performance metrics (e.g., minimum TE, TR, FOV) inherently represent the standalone capabilities of the MRI "algorithm" or system software without direct human intervention during the acquisition process. The assessment is purely on the system's technical output.

7. Type of Ground Truth Used

  • The "ground truth" for this submission is the technical specifications and performance parameters of the predicate MRI devices. The new software version's performance is compared directly to these established technical benchmarks. There is no biological or clinical ground truth (e.g., pathology, outcomes data) discussed.

8. Sample Size for the Training Set

  • Not applicable. This submission describes software updates to an MRI system, not an AI or machine learning model that would require a "training set" of data.

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

  • Not applicable, as there is no training set for an AI/ML model.

§ 892.1000 Magnetic resonance diagnostic device.

(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.