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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.

Ask a specific question about this device

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, and FLAIR sequences, specifically in terms of reductions in image noise and blurring.

Here's an analysis of the provided text to extract information about the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't present a direct table of acceptance criteria versus reported performance in the typical sense of numerical metrics for a specific clinical task (e.g., sensitivity, specificity for disease detection). Instead, the performance is demonstrated through various non-clinical tests and compliance with established standards.

Summary of Device Features (where performance is implicit):

• Image Reconstruction Algorithm: Utilizes deep learning for improved image quality (reductions in image noise and blurring) for T1W, T2W, and FLAIR sequences.
• DWI Image Post-processing: Fast Iterative Shrinkage Thresholding Algorithm (FISTA).
• Image Post-Processing (General): Advanced Denoising (T1W, T2W, FLAIR, DWI), image orientation transform, geometric distortion correction, receive coil intensity correction, DICOM output.

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

The document does not detail a "test set" in the context of a clinical study with patient data. The evaluation is primarily based on non-clinical performance testing and software verification/validation. Therefore, there is no specific sample size of patients or images from a clinical test set mentioned.

The data provenance for the non-clinical tests would be from internal Hyperfine labs or certified testing facilities that perform imaging phantom tests and software testing.

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

Not applicable in the context of this submission. The "ground truth" for the non-clinical tests is established by industry standards (NEMA, ACR phantom guidelines, ISO, etc.) and engineering requirements rather than expert clinical consensus on patient data.

4. Adjudication Method for the Test Set

Not applicable. There was no clinical test set requiring expert adjudication.

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 the provided text. The document refers to "deep learning to provide improved image quality" for certain sequences, but this is an inherent part of the device's image reconstruction algorithm and not a specific AI-assisted diagnostic aid for human readers. The clinical utility of these improved images (once interpreted by a trained physician) is part of the overall "diagnosis," but no study on AI assistance for human readers is described here.

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

The device is an imaging system; its output is the image, which then requires interpretation by a trained physician. The deep learning component is integrated into the image reconstruction, affecting the quality of the image produced by the algorithm. Therefore, in a sense, the "standalone" performance of the image generation (algorithm only) is what's being evaluated against image quality criteria through non-clinical means. There isn't a separate "AI algorithm" that outputs a diagnostic decision independently.

7. The Type of Ground Truth Used

The ground truth used for demonstrating compliance is primarily:

• Engineering Requirements and Standards: For software functionalities, electrical safety, biocompatibility, cleaning/disinfection, and cybersecurity.
• Imaging Phantoms: For image quality criteria, based on established standards like NEMA and ACR phantom test guidance.

8. The Sample Size for the Training Set

The document states that the image reconstruction algorithm "utilizes deep learning." However, it does not provide any information on the sample size (number of images, patients, etc.) for the training set used for this deep learning model.

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

The document does not provide details on how the ground truth for the deep learning training set was established. It only mentions that deep learning is used for image reconstruction to improve image quality (noise reduction and blurring). For image reconstruction deep learning models, the "ground truth" during training typically involves pairs of lower-quality input images and corresponding higher-quality reference images (often obtained using conventional non-accelerated MRI or higher field strength MRI) or synthetic data representing ideal image characteristics. However, specifics are not in this 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 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:

1. 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.
2. Sample size used for the test set and data provenance.
3. Number of experts used to establish the ground truth for the test set and their qualifications.
4. Adjudication method for the test set.
5. 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.
6. Information on whether a standalone (algorithm only without human-in-the-loop performance) study was done.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.).
8. The sample size for the training set.
9. 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.

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BrainInsight is intended for automatic labeling, spatial measurement, and volumetric quantification of brain structures from a set of low-field MR images and returns annotated and segmented images, color overlays and reports.

BrainInsight is a fully automated MR imaging post-processing medical software that provides image alignment, whole brain segmentation, ventricle segmentation, and midline shift measurements of brain structures from a set MR images. The BrainInsight processing architecture includes a proprietary automated internal pipeline based on machine learning tools. The output annotated and segmented images are provided in standard image format using segmented color overlays and reports that can be displayed on third-party workstations and FDA-cleared Picture Archive and Communications Systems (PACS). The high throughput capability makes the software suitable for use in routine patient care as a support tool for clinicians in assessment of low-field (0.064 T) structural MRIs. BrainInsight provides overlays and reports based on 0.064 T 3D MRI series of T1 Gray/White, T2-Fast, and FLAIR images.

Here's a breakdown of the acceptance criteria and the study details for the BrainInsight™ device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria were defined based on non-inferiority testing, aiming for the model performance to be no worse than the average annotator's discrepancy.

Midline Shift Discrepancy (Lower is Better)

| Application | Modality | Acceptance Criteria (Model 2 to 12 years (20.6%), >12 to 18 to 90 years (70.6%)
* Gender: 33% Female / 41% Male / 25% Anonymized
* Pathology: Stroke (Infarct), Hydrocephalus, Hemorrhage (SAH, SDH, IVH, IPH), Mass/Edema, Tumor.

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

• Number of Experts: The document states that the datasets for training and validation were annotated by "multiple experts." It then mentions that "The entire group of training image sets was divided into segments and each segment was given to a single expert." This phrasing is somewhat ambiguous for the test set specifically. It is implied that multiple experts were involved in the ground truth establishment for the overall process, but it doesn't clearly state how many experts independently evaluated each case in the test set, nor if the "single expert per segment" approach also applied to the test set ground truth.
• Qualifications of Experts: Not specified beyond being referred to as "experts" and "annotators."

4. Adjudication Method for the Test Set

The adjudication method varies by application:

• Midline Shift: Ground truth was determined based on the average shift distance of all annotators. This implies a form of consensus or averaging method rather than a strict adjudication by a senior expert.
• Segmentation (Lateral Ventricles, Whole Brain): Ground truth for segmentation was calculated using Simultaneous Truth and Performance Level Estimation (STAPLE). STAPLE is an algorithm that estimates a "true" segmentation from multiple segmentations, weighting them based on their estimated performance. This is an algorithmic adjudication method.

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

• Was a MRMC study done? No, a traditional MRMC comparative effectiveness study that measures how human readers improve with AI vs. without AI assistance was not explicitly described for this submission. The study focuses on standalone performance of the AI model against expert annotations and the "mean annotator" performance.
• Effect Size of Human Improvement (if applicable): Not applicable, as an MRMC comparative effectiveness study was not detailed.

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

• Was a standalone study done? Yes, the described performance evaluation appears to be a standalone (algorithm only) study. The device's performance is compared directly against the ground truth established by annotators, and against the mean discrepancy of the annotators themselves. There is no mention of human readers using the AI output to improve their performance compared to a baseline.

7. Type of Ground Truth Used

The type of ground truth used varies by the measurement:

• Midline Shift: Expert consensus, calculated as the average shift distance of all annotators.
• Segmentation (Lateral Ventricles, Whole Brain): Algorithmic consensus, calculated using Simultaneous Truth and Performance Level Estimation (STAPLE) based on expert annotations.
• General: It is based on expert annotations of images acquired from the Hyperfine Swoop portable MRI system.

8. Sample Size for the Training Set

• Sample Size for Training Set: The exact numerical sample size for the training set is not explicitly stated. The document only mentions that the data collection for the training and validation datasets was done at "multiple sites."

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

• The data collection for the training and validation datasets was done at multiple sites.
• The datasets were annotated by multiple experts.
• The "entire group of training image sets was divided into segments and each segment was given to a single expert."
• "The expert's determination became the ground truth for each image set in their segment." This implies a form of single-reader ground truth for each segmented batch, rather than multi-reader consensus for every single case within the training set.

Ask a specific question about this device

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. |

Ask a specific question about this device

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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BrainInsight is intended for automatic labeling, spatial measurement, and volumetric quantification of brain structures from a set of low-field MR images and returns annotated and segmented images, color overlans and reports.

BrainInsight is a fully automated MR imaging post-processing medical software that provides image alignment, whole brain segmentation, ventricle segmentation, and midline shift measurements of brain structures from a set of MR images from patients ages 18 years or older. The BrainInsight processing architecture includes a proprietary automated internal pipeline based on machine learning tools. The output annotated and segmented images are provided in standard image format using segmented color overlays and reports that can be displayed on third-party workstations and FDA-cleared Picture Archive and Communications Systems (PACS).

The modified BrainInsight described in this submission includes changes to the machine learning models to allow for the processing Al-reconstructed low-field MR images. The modified device also includes configuration updates and refactoring changes for incremental improvement.

Here's a breakdown of the acceptance criteria and study details for the BrainInsight device, based on the provided text:

BrainInsight Acceptance Criteria and Study Details

1. Table of Acceptance Criteria and Reported Device Performance

For Midline Shift:

For Lateral Ventricles and Whole Brain Segmentation (Dice Overlap):

For Lateral Ventricles and Whole Brain Segmentation (Volume Differences):

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

The document does not explicitly state the numerical sample size for the test set. It mentions the distribution of categories:

• Age: Min: 19, Max: 77
• Gender: 59% Female / 41% Male
• Pathology: Stroke (Infarct), Hydrocephalus, Hemorrhage (SAH, SDH, IVH, IPH), Mass/Edema, Tumor, Multiple sclerosis.

Data Provenance: The images were acquired from "multiple sites" using the "FDA cleared Hyperfine Swoop Portable MR imaging system." It is implied to be retrospective as data collection occurred before the testing. The country of origin is not specified but is likely the US given the FDA submission.

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

The text states that "Ground truth for midline shift was determined based on the average shift distance of all annotators" and "Ground truth for segmentation is calculated using Simultaneous Truth and Performance Level Estimation (STAPLE)." It also mentions that "The datasets were annotated by multiple experts." However, the exact number of experts used for the test set's ground truth and their specific qualifications (e.g., "radiologist with 10 years of experience") are not explicitly stated.

4. Adjudication Method for the Test Set

The ground truth for midline shift was determined by the average shift distance of all annotators. For segmentation, the Simultaneous Truth and Performance Level Estimation (STAPLE) method was used. This implies a form of consensus-based adjudication, but not a strict numerical rule like 2+1 or 3+1. STAPLE is a probabilistic approach to estimate a true segmentation from multiple expert segmentations while simultaneously estimating the performance level of each expert.

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

The document describes a standalone performance study of the algorithm against expert annotations, but does not mention a multi-reader multi-case (MRMC) comparative effectiveness study where human readers' performance with and without AI assistance is compared. Therefore, no effect size of human improvement with AI assistance is provided.

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

Yes, a standalone performance study was conducted. The device's performance (Midline Shift, Dice Overlap, Volume Differences) was evaluated directly against a ground truth established by annotators, and the results were compared to the average annotator discrepancy to demonstrate non-inferiority. This is a standalone evaluation of the algorithm's performance.

7. Type of Ground Truth Used

The ground truth used was expert consensus.

• For midline shift, it was based on the "average shift distance of all annotators."
• For segmentation, it was calculated using "Simultaneous Truth and Performance Level Estimation (STAPLE)" from multiple expert annotations.

8. Sample Size for the Training Set

The document does not explicitly state the numerical sample size for the training set. It only mentions that "Each model was trained using a training dataset to optimize parameters" and "The data collection for the training and validation datasets were done at multiple sites."

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

The ground truth for the training set was established through expert annotation. The text states: "The datasets were annotated by multiple experts. The entire group of training image sets was divided into segments and each segment was given to a single expert. The expert's determination became the ground truth for each image set in their segment."

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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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