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Indications for use vary depending on the specific product and its intended application. These products are designed for use in medical or laboratory settings by trained professionals. Depending on the device, intended uses may include:

• Diagnostic purposes: Analyzing biological samples (e.g., blood, urine, tissue) to identify diseases, conditions, or other health markers. This can include detecting infections, monitoring chronic illnesses, or screening for genetic predispositions.
• Therapeutic procedures: Assisting in or performing medical interventions, such as administering medications, delivering fluids, or providing respiratory support.
• Research and development: Used in laboratory experiments and studies to investigate biological processes, test new drugs, or develop new medical technologies.
• Monitoring physiological parameters: Measuring heart rate, blood pressure, oxygen saturation, or other vital signs.
• Sample collection and preparation: Gathering, processing, and storing biological samples for further analysis.

Specific indications for use are provided in the product's labeling, instructions for use (IFU), or accompanying documentation. Users should always refer to the manufacturer's provided information for the most accurate and complete indications.

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The FDA Clearance Letter for the Swoop® Portable MR Imaging® System (V2) provides details on the acceptance criteria and the studies conducted to demonstrate the device meets these criteria, particularly focusing on the "Advanced Reconstruction" feature which likely incorporates deep learning for image quality optimization.

Here's a breakdown of the requested information:

1. Acceptance Criteria and Reported Device Performance

The core performance of the device's "Advanced Reconstruction" was evaluated through three studies: Performance Analysis, Contrast-to-Noise Ratio (CNR) Validation, and Advanced Reconstruction Image Validation.

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

The document describes three distinct test sets for different validation studies.

• Performance Analysis (Robustness, Stability, Generalizability):
  • Sample Size:
    • T1, T2, FLAIR group: 40 patients, 111 images.
    • DWI group: 29 patients, 94 images.
  • Data Provenance: Not explicitly stated regarding country of origin. The test set was "entirely independent from the dataset used for model training." The "Equipment Type" is listed as "Swoop v2" (with

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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.

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 and image processing.

Here's a summary of the acceptance criteria and the studies that prove the device meets them, based on the provided FDA 510(k) clearance letter for the Swoop® Portable MR Imaging® System:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes and Data Provenance

The provided document does not explicitly state the country of origin for the data or whether it was retrospective or prospective for the training or test sets.

• T1, T2, FLAIR: 44 patients, 92 images
• DWI: 34 patients, 65 images | Not specified in the provided document. |
  | Contrast-to-Noise Ratio (CNR) Validation | Patients: 43
  Images: 95
  ROIs (Regions of Interest): 316 | Not specified in the provided document. |
  | Advanced Reconstruction Image Validation | Patients: 46
  Images: 177
  Per Sequence: At least 16 cases per sequence (with at least 4 cases per sequence-available image orientation) | Not specified in the provided document. |

3. Number of Experts and Qualifications for Ground Truth

4. Adjudication Method

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

• A form of MRMC study was conducted for the "Advanced Reconstruction Image Validation" where five ABR-certified radiologists reviewed images.
• Effect Size with AI vs. without AI assistance: The study compared Advanced Reconstruction (which utilizes deep learning) to Linear Reconstruction (without advanced AI assistance). 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), indicating "improved image quality" relative to Linear Reconstruction. The Likert scale used was not detailed, but a score of 2 on a 5-point scale (where 0 might be "no difference" and higher values indicate improvement) suggests a significant positive effect.

6. Standalone Performance Study

• Yes, a standalone (algorithm only) performance study was conducted.
• The "Performance Analysis" section describes evaluating Advanced Reconstruction's ability to reproduce ground truth images using objective metrics (NMSE, SSIM) without human reader involvement for the primary comparison. The "Contrast-to-Noise Ratio Validation" also measured objective image characteristics (CNR) of the algorithm's output.

7. Type of Ground Truth Used

8. Sample Size for the Training Set

• The document states that the test dataset was "entirely independent from the dataset used for model training."
• However, the specific sample size or characteristics of the training set are not provided in this document.

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

• The document states: "In all cases, models are trained and validated with MRI data and images as the only inputs and outputs" and "Advanced Reconstruction was performed using a test dataset entirely independent from the dataset used for model training."
• Similar to the training set sample size, the establishment of ground truth for the training set is not detailed in the provided document. It can be inferred that if high-field or synthetically derived images were used for validation, similar methods might have been used for training, but this is not explicitly stated.

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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.

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 provided document is a 510(k) Summary for the Hyperfine Swoop Portable MR Imaging System (K240944). It describes the device, its intended use, and compares it to a predicate device (K232760) to demonstrate substantial equivalence.

Here's an analysis of the acceptance criteria and the study information based on the document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list "acceptance criteria" for the device, but rather describes the tests performed and the standards met for demonstrating substantial equivalence. The provided information focuses on engineering and software validation rather than clinical performance metrics such as sensitivity, specificity, or accuracy for a specific diagnostic task.

Here's a table summarizing the tests described and the reported outcome:

The document states that the "subject device passed all the testing in accordance with internal requirements and applicable standards to support substantial equivalence." This implies that the acceptance criteria were met, which were defined by the adherence to these standards and the internal requirements.

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

The document details testing for software verification, image performance (phantom-based), cybersecurity, and software validation. It does not describe a clinical test set involving patient data for the subject device to evaluate diagnostic performance. The image performance testing appears to be based on physical phantoms (NEMA, ACR). Therefore, information on sample size for a "test set" in the context of clinical images and data provenance (country of origin, retrospective/prospective) is not provided.

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

Since no clinical test set evaluating diagnostic performance with patient images is described for the subject device in this document, there is no mention of experts establishing ground truth for such a set. The image performance testing refers to ACR Phantom Test Guidance and standards, which don't typically involve expert reading of collected patient images.

4. Adjudication Method for the Test Set

As no clinical test set for diagnostic performance is described, there is no information on an adjudication method.

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?

No MRMC comparative effectiveness study is mentioned in this document. The submission focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance and leveraging prior test results from the predicate, not on comparative clinical efficacy or improvement with AI assistance for human readers. The device does utilize deep learning for image reconstruction, but its impact on human reader performance is not evaluated in this submission.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done

The document does not describe a standalone performance study in the context of diagnostic accuracy for the AI component (deep learning for image reconstruction). The deep learning is part of an image reconstruction algorithm, and the "image performance" testing is done against established phantom standards, not against a ground truth for diagnostic accuracy.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

For the non-clinical tests described, the "ground truth" refers to:

• Software Verification/Validation: Adherence to design requirements and user needs.
• Image Performance: Adherence to image quality criteria as defined by NEMA and ACR phantom standards. The "ground truth" for these tests would be the known properties of the phantoms and the expected imaging parameters.
• Cybersecurity, Biocompatibility, Cleaning/Disinfection, Safety, Performance (SAR): Adherence to relevant regulatory standards (IEC, ISO, FDA Guidance, ANSI/AAMI, ASTM).

There is no mention of expert consensus, pathology, or outcomes data as ground truth because no clinical diagnostic accuracy study is presented.

8. The Sample Size for the Training Set

The document states that "The Swoop System image reconstruction algorithm utilizes deep learning to provide improved image quality for T1W, T2W, FLAIR, and DWI sequences." However, it does not provide any information regarding the sample size of the training set used for this deep learning algorithm.

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

Since the document does not provide details on the training set for the deep learning algorithm, it also does not specify how the ground truth for that training set was established. Given it's an image reconstruction algorithm, the "ground truth" for training would typically involve pairs of raw MRI data and high-quality reconstructed images (often from different acquisition parameters or iterative reconstruction methods) rather than diagnostic labels from experts.

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