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The Swoop Portable MR Imaging System is a portable, ultra-low field magnetic resonance imaging device for producing images that display the internal structure of the head where full diagnostic examination is not clinically practical. When interpreted by a trained physician, these images provide information that can be useful in determining a diagnosis.

Device Description

The Swoop System is portable, ultra-low field MRI device that 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, FLAIR, and DWI sequences.

The subject Swoop System described in this submission includes software modifications related to the pulse sequences.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for the Swoop® Portable MR Imaging® System, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Acceptance Criteria Category	Specific Acceptance Criteria	Reported Device Performance
Advanced Reconstruction	Performance Analysis: Robustness, stability, and generalizability over a variety of subjects, design parameters, artifacts, and scan conditions using reference-based metrics (NMSE and SSIM). The ability of Advanced Reconstruction to reproduce the ground truth image compared to Linear Reconstruction should be superior or demonstrate expected behavior.	NMSE was reduced and SSIM was improved for Advanced Reconstruction test images compared to Linear Reconstruction test images across all models and test datasets. Reconstruction outputs with motion and zipper artifacts were qualitatively assessed to be acceptable.
Contrast-to-Noise Ratio (CNR) Validation	Mean CNR of Advanced Reconstruction required to be greater than the mean CNR of the baseline Linear Reconstruction at a statistical significance level of 0.05 for each sequence type. This demonstrates that pathology features are preserved.	In all cases, CNR of Advanced Reconstruction was greater than or equal to Linear Reconstruction for both hyper- and hypo-intense pathologies. This demonstrates that Advanced Reconstruction does not unexpectedly modify, remove, or reduce the contrast of pathology features.
Image Validation (Radiologist Review)	Advanced Reconstruction required to perform at least as well as Linear Reconstruction in all categories (median score ≥0 on Likert scale) and perform better (≥1 on Likert scale) in at least one of the quality-based categories (noise, sharpness, contrast, geometric fidelity, artifact, and overall image quality).	Advanced Reconstruction achieved a median score of 2 (the most positive rating scale value) in all categories (noise, sharpness, contrast, geometric fidelity, artifact, and overall image quality). This indicates reviewers found Advanced Reconstruction improved image quality while maintaining diagnostic consistency relative to Linear Reconstruction.
Software Verification	Software verification testing in accordance with design requirements.	Passed all testing in accordance with internal requirements and applicable standards (IEC 62304:2016, FDA Guidance, "Content of Premarket Submissions for Device Software Functions").
Image Performance	Testing to verify the subject device meets all image quality criteria.	Passed all testing in accordance with internal requirements and applicable standards (NEMA MS 1-2008 (R2020), NEMA MS 3-2008 (R2020), NEMA MS 9-2008 (R2020), NEMA MS 12-2016, American College of Radiology standards for named sequences).
Cybersecurity	Testing to verify cybersecurity controls and management.	Passed all testing in accordance with internal requirements and applicable standards (FDA Guidance, "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions").
Software Validation	Validation to ensure the subject device meets user needs and performs as intended.	Passed all testing in accordance with internal requirements and applicable standards (FDA Guidance, "Content of Premarket Submissions for Device Software Functions").

Study Details for Advanced Reconstruction Validation (DWI Sequence - updated in this submission)

This section focuses specifically on the studies conducted to validate the Advanced Reconstruction models for the updated DWI sequence. Performance analysis and validation for T1/T2/FLAIR models were leveraged from predicate devices, so this analysis covers the new data.

1. Performance Analysis

Sample Size:
- Test Set (DWI): 8 patients, 31 images.
- Data Provenance: Not explicitly stated, but includes data from 6 different sites. Countries of origin are not specified. The study is retrospective, utilizing existing MRI data.
Ground Truth Establishment (Test Set):
- Number of Experts: Not applicable for quantitative metrics (NMSE, SSIM). Quantitative metrics were reference-based, comparing reconstructed images to ground truth target images (Swoop data, high field images, and synthetic contrast images).
- Qualifications of Experts: N/A.
Adjudication Method: N/A.
MRMC Comparative Effectiveness Study: No, this was a standalone performance analysis comparing Advanced Reconstruction to Linear Reconstruction against a reference standard.
Standalone Performance: Yes. The algorithm's output was compared to ground truth images using quantitative metrics.
Type of Ground Truth: Reference images, including Swoop data, high field images, and synthetic contrast images. Test input data (synthetic k-space) was generated from these target images.
Training Set Sample Size: Not explicitly stated for this particular updated DWI model. The document states "None of these test images were used in model training," implying a separate training set, but its size is not provided.
Ground Truth Establishment (Training Set): Not explicitly stated, but generally for deep learning reconstruction, the training data would include raw k-space data paired with corresponding reference images (often higher quality, known good reconstructions, or synthetic data).

2. Contrast-to-Noise Ratio (CNR) Validation

Sample Size:
- Test Set (DWI): 12 patients, 45 images, 145 Regions of Interest (ROIs).
- Data Provenance: Not explicitly stated, but includes data from 5 different sites. Countries of origin are not specified. Retrospective.
Ground Truth Establishment (Test Set):
- Number of Experts: At least one.
- Qualifications of Experts: An American Board of Radiology (ABR) certified radiologist reviewed the annotations for accuracy.
Adjudication Method: Not explicitly stated as a formal adjudication method (like 2+1), but radiologists reviewed ROI accuracy.
MRMC Comparative Effectiveness Study: No, this was a standalone quantitative comparison of CNR between Advanced Reconstruction and Linear Reconstruction.
Standalone Performance: Yes. The algorithm's output was quantitatively measured and compared to the linear reconstruction, using expert-annotated ROIs for pathology.
Type of Ground Truth: Expert-annotated regions of interest (ROIs) encompassing pathologies, reviewed for accuracy by an ABR-certified radiologist.
Training Set Sample Size: Not explicitly stated.
Ground Truth Establishment (Training Set): Not explicitly stated.

3. Advanced Reconstruction Image Validation (Radiologist Review)

Sample Size:
- Test Set (DWI): 34 patients, 34 sets of DWI images (102 individual images when considering b=0, trace-weighted/single direction, and ADC).
- Data Provenance: Not explicitly stated, but includes data from 8 different sites. Countries of origin are not specified. Retrospective by nature of rating existing images.
Ground Truth Establishment (Test Set): Ground truth for rating was established by consensus of the clinical reviewers' assessments on a Likert scale. There wasn't an independent "definitive" ground truth for image quality beyond the expert reviews.
Number of Experts: Four.
Qualifications of Experts: External, ABR-certified radiologists representing clinical users.
Adjudication Method: Not explicitly stated if there was a formal adjudication if reviewers disagreed. Instead, they rated independently, and median scores were used for evaluation.
MRMC Comparative Effectiveness Study: This study had elements of an MRMC study by using multiple readers (4 radiologists) to rate multiple cases (34 image sets) with and without the AI assistance (Advanced vs. Linear Reconstruction, though not exactly "assisted" as in "human + AI" vs. "human only").
- Effect Size: Advanced Reconstruction achieved a median score of 2 (the most positive rating scale value) in all categories. This indicates a significant improvement in perceived image quality and diagnostic consistency compared to Linear Reconstruction (which would be analogous to "without AI assistance" in this context), as the criteria required only a median score ≥1 in one category for "better performance."
Standalone Performance: Partially. While radiologists rated the images, their input constituted the performance metric. It's not a purely algorithmic standalone performance against a fixed ground truth.
Type of Ground Truth: Expert consensus ratings (Likert scale) on image quality attributes and diagnostic consistency.
Training Set Sample Size: Not explicitly stated.
Ground Truth Establishment (Training Set): Not explicitly stated.

In summary, for the updated DWI sequence validation:

Test Set Sample Sizes:
- Performance Analysis: 8 patients, 31 images
- CNR Validation: 12 patients, 45 images, 145 ROIs
- Image Validation: 34 patients, 34 image sets (102 images)
Data Provenance: Retrospective, multiple sites (6 for performance, 5 for CNR, 8 for image validation via different Swoop System models), countries not specified.
Expert Reviewers: An ABR-certified radiologist for ROI accuracy in CNR validation, and four external ABR-certified radiologists for the image quality review.
Ground Truth: Varied from reference images, to expert-annotated ROIs, to expert consensus ratings.
Training Set Details: Minimal information provided regarding the training set's size or ground truth establishment in this document. The focus here is on the validation of the updated DWI model.

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