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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 interpreted 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 device pulse sequences; retrained advanced reconstruction models; service and support features; and adds an audible scanner startup tone.

The provided text describes the regulatory clearance of the Hyperfine Swoop® Portable MR Imaging System™ and mentions non-clinical performance testing. However, it does not contain the specific details required to complete all sections of your request, particularly a table of acceptance criteria and reported device performance based on a study, sample sizes, expert qualifications, or details of a multi-reader multi-case (MRMC) study.

The document indicates that the device's 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. It also states that the subject device includes retrained advanced reconstruction models. The non-clinical performance section states "Testing to verify the subject device meets all image quality criteria" and references various NEMA and ACR standards for image performance. It also mentions a "Validation study to ensure the device meets user needs and performs as intended."

Given the limitations of the provided text, I can only fill in the information that is explicitly stated or can be reasonably inferred. Many sections will be marked as "Not provided in the text".

Here's a summary of the available information:

1. Table of Acceptance Criteria and Reported Device Performance
The document mentions that "Testing to verify the subject device meets all image quality criteria" was performed against various NEMA and ACR standards. However, the specific quantitative acceptance criteria or the reported device performance metrics (e.g., specific SNR values, resolution, etc.) are not provided in the text.

2. Sample size used for the test set and the data provenance
Not provided in the text. The document mentions "non-clinical performance" and "verification and validation testing" but does not specify details about a clinical test set, sample size, or data provenance for these image quality assessments.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not provided in the text. The document mentions that the images, "When interpreted by a trained physician, ... provide information that can be useful in determining a diagnosis," but this refers to the intended use of the device, not the ground truth establishment for a specific test set within the regulatory submission.

4. Adjudication method for the test set
Not provided in the text.

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
Not provided in the text. The document focuses on regulatory clearance based on substantial equivalence and non-clinical testing. While the device uses deep learning for image reconstruction, a MRMC study is not mentioned.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Partially addressed: The text states, "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." This implies an algorithm-only component focusing on image quality improvement. The non-clinical performance section includes "Image Performance" testing against NEMA and ACR standards, which would primarily assess the standalone algorithm's output image quality. However, a standalone diagnostic performance study (e.g., sensitivity/specificity for detecting pathologies) is not explicitly detailed.

7. The type of ground truth used
Partially addressed / Inferred: For "Image Performance" testing (which includes assessments against NEMA and ACR standards), the ground truth is typically based on phantom measurements and known physical properties, not clinical outcomes or pathology directly. For the "Validation study to ensure the device meets user needs and performs as intended," the type of ground truth is not specified, but it would likely involve expert evaluation of image quality and usability, rather than a clinical ground truth like pathology.

8. The sample size for the training set
Not provided in the text. The document mentions "retrained advanced reconstruction models," indicating a training process, but no details about the training data size.

9. How the ground truth for the training set was established
Not provided in the text.

Summary Table of Available Information based on the Provided Text:

Specific quantitative acceptance criteria and reported device performance values are not provided in the text. |
| Sample size (Test Set) & Data Provenance | Not provided in the text. |
| Number & Qualifications of Experts (Test Set Ground Truth) | Not provided in the text. |
| Adjudication Method (Test Set) | Not provided in the text. |
| MRMC Comparative Effectiveness Study | Not provided in the text. |
| Standalone (Algorithm Only) Performance | Yes, implicitly. The document states, "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." This is an algorithm-only function. The "Image Performance" testing against NEMA and ACR standards would evaluate the standalone algorithm's output image quality. |
| Type of Ground Truth Used (Test Set) | For "Image Performance": Inferred to be based on phantom measurements and known physical characteristics as per NEMA and ACR phantom protocols.

For "Software Validation" (user needs): Not specified, but likely expert evaluation of image quality and usability. |
| Sample Size for Training Set | Not provided in the text. |
| How Ground Truth for Training Set was Established | Not provided in the text. |

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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 interpreted 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 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 provided text describes a 510(k) premarket notification for the Hyperfine Swoop® Portable MR Imaging System™. It discusses the device's substantial equivalence to a predicate device and lists various non-clinical performance tests conducted. However, the document does not contain specific acceptance criteria, reported device performance metrics (like sensitivity, specificity, accuracy), details about a study proving the device meets acceptance criteria, sample sizes for test sets, data provenance, number of experts, adjudication methods, MRMC comparative effectiveness studies, standalone performance studies, types of ground truth used, or training set sample sizes.

The "Non-Clinical Performance" section broadly states:

• Imaging Performance Test: "Testing to verify image performance meets all image quality criteria." The applicable standards listed are NEMA MS 1-2008 (R2020), NEMA MS 3-2008 (R2020), NEMA MS 9-2008 (R2020), NEMA MS 12-2016, American College of Radiology (ACR) Phantom Test Guidance for Use of the Large MRI Phantom for the ACR MRI Accreditation Program, and American College of Radiology standards for named sequences.
• Result: "The subject device passed all the testing in accordance with internal requirements and applicable standards to support substantial equivalence."

This indicates that the device underwent testing against established standards and internal requirements for image quality, and it passed these tests. However, the specific quantitative acceptance criteria and the detailed results showing how it met them are not provided in this regulatory summary. The document focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed clinical or performance study report.

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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 interpreted 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.

This subject device in this submission includes modified pulse sequence options and an enhancement to the existing noise correction feature to remove residual line noise.

The information provided describes the Swoop® Portable MR Imaging System and its substantial equivalence to a predicate device, focusing on non-clinical performance testing. Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document details the types of testing performed rather than specific numerical acceptance criteria and performance metrics. However, it states that the device "passed all the testing in accordance with internal requirements and applicable standards to support substantial equivalence."

• Advanced reconstruction models do not alter image features or introduce artifacts.
• Image quality with advanced reconstruction is acceptable.
• Basic software functionality is unchanged between releases.
• NESSUS scan found no significant vulnerabilities. |
  | Image Performance | Meets all image quality criteria defined by applicable standards (NEMA MS 1, 3, 9, 12, ACR Phantom Test Guidance, ACR standards for named sequences). | Device passed testing to verify image performance meets all image quality criteria. |
  | Software Validation| Device meets user needs and performs as intended. | Validation studies confirmed the device meets user needs and performs as intended. |
  | Cybersecurity | Cybersecurity controls and management are effective. | Testing verified cybersecurity controls and management. |

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

The document does not specify a separate "test set" in the context of clinical data for evaluating the advanced reconstruction algorithm. The performance evaluation appears to be based on non-clinical phantom testing and software verification/validation. Therefore, information regarding sample size for a test set and data provenance (e.g., country of origin, retrospective/prospective) for clinical data is not provided in this document.

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

Since this involves non-clinical phantom and software testing, there is no mention of experts establishing ground truth in the traditional sense of clinical image interpretation by radiologists. The "ground truth" for image quality likely refers to established physical measurements and industry standards.

4. Adjudication Method for the Test Set

Not applicable, as no clinical test set requiring adjudication by experts is described.

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

No MRMC comparative effectiveness study is mentioned. The document focuses on demonstrating substantial equivalence through non-clinical testing and comparison to a predicate device, rather than assessing the assistive capabilities for human readers.

6. Standalone (Algorithm Only) Performance

The image reconstruction algorithm utilizes deep learning to provide improved image quality. The "Software Verification" and "Image Performance" sections describe testing of this algorithm in a standalone manner (i.e., verifying its performance against image quality criteria and standards) without a human reader in the loop for assessment. Thus, standalone algorithm performance was done through non-clinical testing.

7. Type of Ground Truth Used

For the non-clinical performance evaluation, the ground truth used appears to be:

• Industry standards and established phantom measurements: For image quality assessment ("NEMA MS" standards, "ACR Phantom Test Guidance," "American College of Radiology standards for named sequences").
• Internal requirements and specifications: For software functionality and verification.

8. Sample Size for the Training Set

The document states that the Swoop® System image reconstruction algorithm utilizes deep learning. 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

The document mentions the use of a deep learning algorithm for image reconstruction but does not describe how the ground truth for its training set was established.

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