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SwiftMR is a stand-alone software solution intended to be used for acceptance, enhancement and transfer of all body parts MR images in DICOM format. It can be used for noise reduction and increasing image sharpness for MR images. SwiftMR is not intended for use on mobile devices.

SwiftMR, is software used as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. The device only processes DICOM images for the end User and is intended to be used by radiology technologists in an imaging center, clinic, or hospital. The device's inputs are MRI images in DICOM format. The deep learning algorithm produces enhanced images as outputs with reduced noise and increased sharpness in DICOM format. The deep learning algorithm performs noise reduction with the ability of adjusting the denoising level 0 to level 8, and sharpening filter performs the sharpening function with the ability of adjusting the sharpness level from level 0 to level 5. SwiftMR provides an automatic image quality enhancement function for MR images acquired in various environments. SwiftMR can only be used for professional purposes and is not intended for use on mobile devices.

Here is the requested information about the acceptance criteria and study proving the device meets them:

Acceptance Criteria and Device Performance

• 0.13% (deep learning model)
• 0.43% (filter level 1)
• 1.7% (filter level 2)
• 2.3% (filter level 3)
• 3.6% (filter level 4)
• 4.5% (filter level 5)
  for at least 90% of the dataset. | The test passed, indicating the device met or exceeded these criteria. |

Study Details

1. Sample size used for the test set and the data provenance:

   • Sample Size: Not explicitly stated as a single number for the entire test set. However, the validation dataset included retrospective clinical images covering a wide range of conditions.
   • Data Provenance: Retrospective clinical images from various manufacturers (SIEMENS, GE, PHILIPS, CANON, ESAOTE, FONAR, FUJIFILM), field strengths (0.25T, 0.6T, 1.5T, 3.0T), anatomical regions (Body, Cardiac, Neuro, Musculoskeletal), and protocols (T1, T2, T2*, FLAIR, PD, DWI, MRA). Demographics included adults (22-93 yrs, 88.4%) and pediatrics (0-21 yrs, 11.6%), with 44.8% male and 55.2% female. The dataset also included images with up to 50% time reduction for reduced scan time images. Importantly, the validation dataset included data from sources not included in the training dataset to demonstrate performance is not hindered by site variability.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified in the provided document. The document describes quantitative metrics (SNR increase, FWHM decrease) as acceptance criteria, suggesting a technical ground truth rather than expert interpretation of a specific condition.

3. Adjudication method for the test set: Not applicable based on the description. The acceptance criteria are based on objective, quantitative measurements (SNR and FWHM changes) derived from the image processing algorithm itself, rather than subjective expert consensus.

4. 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: An MRMC comparative effectiveness study is not mentioned as part of the validation for this 510(k) submission. The validation focuses on the standalone performance of the algorithm in enhancing image quality based on objective metrics.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes, the performance tests described (noise reduction and sharpness increase) assess the standalone performance of the SwiftMR algorithm. The device "only processes DICOM images for the end User" and "performs image processing in the background automatically."

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The ground truth for the performance validation appears to be technical/quantitative metrics (SNR and FWHM measurements) derived from the images themselves, rather than clinical outcomes, pathology, or direct expert consensus on diagnostic accuracy. The aim is to demonstrate that the device effectively reduces noise and increases sharpness as measured objectively.

7. The sample size for the training set: Not specified in the provided document.

8. How the ground truth for the training set was established: Not specified in the provided document. The algorithm uses a deep learning model, and its parameters were "obtained through an image guided optimization process," but the specifics of the training data's ground truth (e.g., if it involved artificially generated noise, paired noisy/clean images, or expert-labeled image quality) are not detailed.

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SwiftMR is a stand-alone software solution intended to be used for acceptance, enhancement and transfer of brain. spine, knee, ankle, shoulder and hip MR images in DICOM format. It can be used for noise reduction and increasing image sharpness for non-contrast enhanced MR images.

SwiftMR is not intended for use on mobile devices.

SwiftMR, is software used as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. The device only processes DICOM images for the end User and is intended to be used by radiology technologists in an imaging center, clinic, or hospital.

The device's inputs are standard of care MRI images in DICOM format. The deep learning algorithm produces enhanced images as outputs with reduced noise and increased sharpness in DICOM format. The deep learning algorithm performs the denoising function and sharpening filter performs the sharpening function with the ability of adjusting the sharpness level from level 0 to level 5.

SwiftMR provides an automatic image quality enhancement function for MR images acquired in various environments. SwiftMR can only be used for professional purposes and is not intended for use on mobile devices.

SwiftMR 's automation procedure is as follows:

• . Receive MR images that are in DICOM format
• . Image quality enhancement using Deep Learning model and sharpening filter
• Transfer enhanced MR image as DICOM format to PACS .

There are four deep learning algorithms: three are for the general pulse sequences and the other one is for the TOF pulse sequences. The four deep learning algorithms share the same network architecture, input and label data generation method, training procedures. The only difference between the four deep learning algorithms is the input / label dataset used for training.

After integration with the facilities PACS, SwiftMR performs image processing in the background automatically. At the same time, SwiftMR allows logged-in users to use its functions and view the processing status. When loqged in as the System Admin, the function is available to the control automation procedure and system change settings. On the User side, the User can retrieve the results of image processing in the form of a worklist by login to the user account.

The software provides three main functions, which are image processing, quality check and progress monitoring.

The software is intended to run automatically in the background so that it does not interrupt the workflow of users. When the user executes MR scans as he/she usually does, the newly acquired images are automatically uploaded to the server and registered in the database (DB) for image processing. Once image processing is complete, the images are sent to PACS.

If the user wishes to monitor this automated workflow to check on the status of image processing, he/she can check the main page of the client application or toast messages will appear on the bottom right corner upon completion of each processing. After using the software, they should log out for security reasons.

A settings menu is provided in the form of a user interface to enable system admin to modify software settings as required by the institution or respective user.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) submission:

1. Table of Acceptance Criteria and Reported Device Performance

• 0.43% or more for level 1
• 1.7% or more for level 2
• 2.3% or more for level 3
• 3.6% or more for level 4
• 4.5% or more for level 5
  for at least 90% of the dataset. | "This test passed." (Explicitly stated that the acceptance criteria for sharpness increase were met.) |

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

• Sample Size: The document does not explicitly state the exact numerical sample size (number of images or patients) for the validation test set. It mentions the dataset includes various manufacturers (SIEMENS, GE, PHILIPS), field strengths (1.5T/3.0T), anatomical regions (Brain, Spine, Knee, Ankle, Shoulder, Hip), protocols (T1, T2, T2 FS, GRE, FLAIR, PD, PD FS, TOF, SWI, MPRAGE), and demographics (age 22-90, gender Male 48.7%/Female 51.3%). It also states that the validation dataset included data from sources not included in the training dataset to demonstrate performance is not hindered by site variability.
• Data Provenance:
  • Country of Origin: Not specified in the provided text.
  • Retrospective or Prospective: The study used "retrospective clinical images" for performance testing.

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

The document does not specify the number of experts or their qualifications for establishing ground truth for the test set. The acceptance criteria for noise reduction (SNR) and sharpness (FWHM) are quantitative, suggesting these metrics were likely derived through automated or semi-automated image analysis rather than direct expert labeling for "ground truth" in the sense of pathological diagnosis. While experts may have been involved in defining the methodologies for SNR and FWHM calculation or in qualitative assessment, this is not detailed.

4. Adjudication Method for the Test Set

Not applicable/Not described. The assessment relied on quantitative measures (SNR, FWHM) against predefined thresholds, not on expert consensus or adjudication of subjective interpretations.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No. The document describes a "Performance test" using retrospective clinical images focused on quantitative metrics (SNR, FWHM) to demonstrate improvements in image quality (noise reduction and sharpness increase). It does not describe an MRMC study comparing human reader performance with or without AI assistance.

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

Yes, the performance tests described are standalone evaluations of the algorithm's output. The criteria (SNR increase, FWHM decrease) directly measure the image processing capabilities of SwiftMR without human intervention in the loop for assessment. The device is described as "SwiftMR, is software used as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners." It operates automatically in the background.

7. The Type of Ground Truth Used

The "ground truth" for the performance evaluation appears to be quantitative, intrinsic image quality metrics:

• Signal-to-Noise Ratio (SNR): A measure of image quality directly related to noise levels.
• Full Width at Half Maximum (FWHM): A measure of image sharpness/resolution.

These metrics are calculated from the images themselves, both original and processed, to quantify the device's effect, rather than based on a diagnostic "ground truth" like pathology outcomes or expert consensus on diagnosis.

8. The Sample Size for the Training Set

The document states, "The only difference between the four deep learning algorithms is the input / label dataset used for training." However, the specific sample size (number of images or patients) for the training set is not provided.

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

The document states that the deep learning algorithms were trained using "input and label data generation method." The "labels" in this context for image enhancement typically refer to reference or target images. It specifies that "Neural network-based filters that simultaneously perform noise reduction and sharpness increase functions are obtained. The parameters of the filters were obtained through an image guided optimization process." For deep learning-based image enhancement, the "ground truth" or "labels" for training often involve:

• Synthetically generated data: Creating pairs of noisy/blurry images and their corresponding "clean/sharp" counterparts.
• Paired clinical data: Acquiring both lower quality (e.g., fast scan) and higher quality (e.g., long scan, high-resolution) images of the same anatomy, with the higher quality images serving as the "ground truth" or target for the lower quality input.

The document does not provide explicit details on the specific method of "label data generation" for training, beyond mentioning an "image guided optimization process."

Ask a specific question about this device

SwiftMR is a stand-alone software solution intended to be used for acceptance, enhancement and transfer of brain MRI images in DICOM format. It can be used for noise reduction and increasing image sharpness for non-contrast enhanced MRI images.

SwiftMR is not intended for use on mobile devices.

SwiftMR, is software used as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. The device only processes DICOM images for the end User and is intended to be used by radiology technologists in an imaging center, clinic, or hospital.

The device's inputs are standard of care MRI images in DICOM format. The deep learning algorithm produces enhanced images as outputs with reduced noise and increased sharpness in DICOM format. The device applies both denoising and sharpness increase functions simultaneously.

SwiftMR provides an automatic image quality enhancement function for MR images acquired in various environments. SwiftMR can only be used for professional purposes and is not intended for use on mobile devices.

SwiftMR 's automation procedure is as follows:

• . Upload MR images that have been taken or converted to the DICOM format
• . Image quality enhancement using Deep Learning model
• . Download enhanced MR image as DICOM format

There are two deep learning algorithms that should be selected by users according to the pulse sequences. One is for the general pulse sequences and the other is for the TOF pulse sequences. The two deep learning algorithms share network architecture, input data generation method, training procedures. The only difference between the two deep learning algorithms is the input / label dataset used for training.

After integration with the facilities PACS, SwiftMR performs image processing in the background automatically. At the same time, SwiftMR allows logged-in users to use its functions and change product settings through the client application. When logged in as the System Admin, the function is available to the control automation procedure and system change settings. On the User side, the User can retrieve the results of image processing in the form of a worklist by login to the user account.

The software provides three main functions, which are image processing, quality check and progress monitoring.

The software is intended to run automatically in the background so that it does not interrupt the workflow of users. When the user executes MR scans and saves the images in PACS as he/she usually does, the newly acquired images are automatically uploaded to the server and registered in the database (DB) for image processing. Once image processing is complete, the images are sent back to PACS.

If the user wishes to monitor this automated workflow to check on the status of image processing, he/she can log in to the Client App, and notifications will be received upon each task completion. Detailed information is also available when the main worklist is opened, and MR Study and/or Series in concern is selected.

A settings menu is provided in the form of a user interface to enable users and system admin to modify software settings as required by the institution or respective user.

Here's a breakdown of the acceptance criteria and study details for SwiftMR, based on the provided FDA 510(k) summary:

1. Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for the Test Set

• Sample Size for Test Set: Not explicitly stated. The document mentions "retrospective clinical images" and "at least 90% of the test datasets," but the absolute number of cases or images is not provided.
• Data Provenance: Retrospective clinical images. The country of origin is not specified, but the applicant (AIRS Medical Inc.) is based in South Korea, suggesting potential involvement of data from that region.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. The document only states that "predetermined acceptance criteria were met," implying internal validation, but does not detail how potential disagreements in ground truth were resolved if multiple experts were used.

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

• MRMC Study Conducted: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not reported. The performance data focuses on technical metrics (SNR and FWHM) of the algorithm's output, not on human reader improvement with or without AI assistance.
• Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was performed.

6. Standalone Algorithm Performance Study

• Standalone Performance Study: Yes, a standalone (algorithm only without human-in-the-loop performance) study was conducted. The performance data presented (SNR increase and FWHM decrease) directly quantifies the technical output of the SwiftMR algorithm.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the performance test appears to be based on quantitative technical metrics of image quality (SNR and FWHM) of the original images, which are then compared to the processed images. It is implicitly assumed that higher SNR and lower FWHM indicate improved image quality. It's not based on expert consensus for clinical diagnosis, pathology, or outcomes data in this summary.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not explicitly stated. The document mentions "input / label dataset used for training" for two deep learning algorithms (general pulse sequences and TOF pulse sequences), but the size of these datasets is not provided.

9. How Ground Truth for the Training Set Was Established

• Ground Truth Establishment for Training Set: The document states that the two deep learning algorithms "share network architecture, input data generation method, training procedures. The only difference between the two deep learning algorithms is the input / label dataset used for training." However, it does not explicitly describe how the "label dataset" (ground truth) for training was established. It can be inferred that the labels would correspond to an idealized "enhanced" version of the input, likely generated through a process that defines optimal noise reduction and sharpness, but the specifics of this generation are not detailed.

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