(28 days)
The Swoop® Portable MR Imaging System™ is a bedside magnetic resonance imaging device for producing images that display the internal structure of the head where full diagnostic examination is not clinically practical. When interreted by a trained physician, these images provide information that can be useful in determining a diagnosis.
The Swoop system is a portable MRI device that allows for patient bedside imaging. The system enables visualization of the internal structures of the head using standard magnetic resonance imaging contrasts. The main interface is a commercial off-the-shelf device that is used for operating the system, providing access to patient data, exam setup, exam execution, viewing MRI image data for quality control purposes, and cloud storage interactions. The system can generate MRI data sets with a broad range of contrasts. The Swoop system user interface includes touch screen menus, controls, indicators, and navigation icons that allow the operator to control the system and to view imagery. The Swoop System image reconstruction algorithm utilizes deep learning to provide improved image quality for T1W, T2W, and FLAIR sequences, specifically in terms of reductions in image noise and blurring.
The subject Swoop System described in this submission includes software modifications related to the following:
- DWI pulse sequence and DWI image reconstruction
- Image uniformity correction for all sequence types
- Noise correction for all sequence types
The provided text describes a 510(k) premarket notification for the "Swoop® Portable MR Imaging System™" and its substantial equivalence to a predicate device (Swoop System K223247). However, it does not contain the specific details about acceptance criteria, reported device performance, sample sizes for test or training sets, ground truth establishment methods, or information about multi-reader multi-case studies that you requested.
The document primarily focuses on:
- Substantial Equivalence Discussion: Comparing the subject device's intended use, patient population, anatomical sites, environment of use, energy used, magnet characteristics, gradient characteristics, computer display, RF coils, patient weight capacity, operation temperature, warm-up time, temperature/humidity control, image reconstruction algorithms, and image post-processing to the predicate device.
- Non-Clinical Performance Testing: Listing the types of verification and validation testing performed (Software Verification, Image Performance, Software Validation) and the standards applied. It also mentions leveraged testing from the predicate device (Biocompatibility, Cleaning/Disinfection, Safety, Performance, Cybersecurity).
Summary of What is Not Available in the Provided Text:
The document does not provide:
- A table of acceptance criteria and reported device performance. It broadly states that "the subject device passed all the testing in accordance with internal requirements and applicable standards to support substantial equivalence," but no specific criteria or performance metrics are detailed.
- Sample size used for the test set and data provenance.
- Number of experts used to establish the ground truth for the test set and their qualifications.
- Adjudication method for the test set.
- Information on whether a multi-reader multi-case (MRMC) comparative effectiveness study was done, or any effect size of human reader improvement with AI assistance.
- Information on whether a standalone (algorithm only without human-in-the-loop performance) study was done.
- The type of ground truth used (expert consensus, pathology, outcomes data, etc.).
- The sample size for the training set.
- How the ground truth for the training set was established.
The text indicates that the device utilizes deep learning for image reconstruction algorithms (specifically mentioning improvements for T1W, T2W, and FLAIR sequences in terms of noise and blurring reduction, and a Fast Iterative Shrinkage Thresholding Algorithm (FISTA) for DWI reconstruction). While this implies an AI component, the document does not elaborate on the specific AI performance characteristics or the studies evaluating them in the detail requested. The listed "Image Performance" testing refers to meeting "all image quality criteria" and applicable NEMA and ACR standards, which are general phantom-based image quality metrics for MR systems, not specific AI performance evaluations against a clinical ground truth for diagnostic accuracy.
§ 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.