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SubtleMR is an image processing software that can be used for image enhancement in MRI images. It can be used to reduce image noise for head, spine, neck, abdomen, pelvis, prostate, breast, and musculoskeletal MRI, or increase image sharpness for head MRI.

SubtleMR is Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. As it only processes images for the end user, the device has no user interface. It is intended to be used by radiologists in an imaging center. clinic, or hospital. The software can be used with MR images acquired as part of MRI exams on 1.2 Tesla, 1.5 Tesla or 3 Tesla scanners. The device's inputs are standard of care MRI images. The outputs are images with enhanced image quality.

Here's a breakdown of the acceptance criteria and the study proving the device meets those criteria, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

2. Sample Sizes and Data Provenance

• Test Set Sample Size: Not explicitly stated, but the text mentions "each test dataset" for noise reduction and "at least 90% of the test datasets" for sharpness enhancement, implying a quantifiable number of datasets were used.
• Data Provenance: Retrospective clinical data. The specific country of origin is not mentioned.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated for either test.
• Qualifications of Experts: For the noise reduction test, a "Likert reader study" was conducted, implying human expert readers were involved in rating, but their specific qualifications (e.g., radiologist with X years of experience) are not provided in this document.

4. Adjudication Method for the Test Set

• The document mentions a "Likert reader study" for the noise reduction test to assess non-inferiority. This typically involves multiple readers, and their ratings would be aggregated or adjudicated, but the specific adjudication method (e.g., 2+1, 3+1) is not detailed. For the sharpness enhancement test, the method of assessing "improved" thickness and sharpness in 90% of datasets is not described in terms of human adjudication.

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

• The document mentions a "Likert reader study" for the noise reduction assessment, which is a type of reader study. However, it does not explicitly state that a formal MRMC comparative effectiveness study (comparing human readers with AI vs without AI assistance) was conducted, nor does it provide an effect size for human reader improvement with AI assistance. The focus seems to be on the device's standalone performance or impact on image quality as assessed by readers.

6. Standalone (Algorithm Only) Performance

• Yes, standalone performance was assessed.
  • For Noise Reduction: "signal-to-noise ratio (SNR) of a selected region of interest (ROI) in each test dataset is on average improved by greater than or equal to 5% after SubtleMR enhancement compared to the original images." This is an objective, algorithm-only performance metric.
  • For Sharpness Enhancement: "the thickness of anatomic structure and the sharpness of structure boundaries are improved after SubtleMR enhancement in at least 90% of the test datasets." While this might involve some human interpretation of "improved," the phrasing suggests an objective, algorithm-driven assessment of image characteristics.

7. Type of Ground Truth Used

• Noise Reduction:
  • Objective: Original (unenhanced) MRI images served as a baseline for SNR improvement.
  • Subjective/Expert-based: A "Likert reader study" was used for assessing "visibility of small structures," implying expert human opinion as part of the ground truth for this aspect.
• Sharpness Enhancement: Original (unenhanced) MRI images served as a baseline, and the ground truth for "improved" anatomic structure thickness and boundary sharpness appears to have been derived from a comparison to these original images, likely through objective measurements or expert assessment. The specific method is not fully detailed.

8. Sample Size for the Training Set

• The sample size for the training set is not provided in the given text.

9. How Ground Truth for Training Set Was Established

• The method for establishing ground truth for the training set is not described in the provided text. The document states that the "subject device was validated with test methods identical to those used to test the predicate device" and that the main performance study utilized "retrospective clinical data" for testing, but it does not elaborate on the training data or its ground truth establishment.

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SubtleMR is an image processing software that can be used for image enhancement in MRI images. It can be used to reduce image noise for head, spine, nelvis, prostate, breast and musculosketal MRI, or increase image sharpness for head MRI.

SubtleMR is Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. As it only processes images for the end user, the device has no user interface. It is intended to be used by radiologists in an imaging center, clinic, or hospital. The software can be used with MR images acquired as part of MRI exams on 1.2 Tesla. 1.5 Tesla or 3 Tesla scanners. The device's inouts are standard of care MRI images. The outputs are images with enhanced image quality.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document states that the study "utilized retrospective clinical data." However, it does not explicitly state the sample size for the test set (number of images or patients) or the country of origin of the data.

3. Number of Experts Used and Qualifications of Experts

The document does not explicitly state the number of experts used or their specific qualifications (e.g., "radiologist with 10 years of experience"). It mentions "visibility of small structures" and "thickness of anatomic structure and the sharpness of structure boundaries" were evaluated, implying expert review, but the details are missing.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for establishing the ground truth or evaluating the image quality metrics. It simply states that the tests "passed."

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

The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study involving human readers with and without AI assistance. The performance tests described focus on objective metrics (SNR) and subjective evaluation of image quality changes by the device, not on reader performance improvement.

6. Standalone (Algorithm Only) Performance

Yes, the performance data presented appears to be a standalone (algorithm only) performance evaluation. The metrics (SNR improvement, visibility of small structures, sharpness of structure boundaries) are directly related to the algorithm's output on images rather than evaluating human reader performance with or without the algorithm.

7. Type of Ground Truth Used

The ground truth used appears to be a combination:

• Objective Measurement: For noise reduction, the "signal-to-noise ratio (SNR) of a selected region of interest (ROI)" was objectively measured.
• Expert Consensus/Subjective Evaluation: For "visibility of small structures" and "thickness of anatomic structure and the sharpness of structure boundaries," a subjective evaluation was conducted using a Likert scale for noise reduction, and a percentage of datasets showing improvement for sharpness enhancement. While not explicitly stated as "expert consensus," these evaluations would typically require trained medical professionals (e.g., radiologists) to perform.

8. Sample Size for the Training Set

The document does not provide the sample size for the training set. It mentions the algorithm uses a "convolutional network-based algorithm" and that "parameters of the filters were obtained through an image-guided optimization process," implying a training phase, but the size is not specified.

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

The document does not explicitly state how the ground truth for the training set was established. It mentions "image-guided optimization process" to obtain the parameters of the filters, which implies that the training data had some form of "ground truth" to guide the optimization, but the nature of this ground truth (e.g., perfectly noise-free images, perfectly sharp images) and how it was derived is not detailed.

Ask a specific question about this device

SubtleMR is an image processing software that can be used for image enhancement in MRI images. It can be used to reduce image noise for head, spine, neck and knee MRI, or increase image sharpness for non-contrast enhanced head MRI.

SubtleMR is Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. As it only processes images for the end user, the device has no user interface. It is intended to be used by radiologists in an imaging center, clinic, or hospital. The software can be used with MR images acquired as part of MRI exams on 1.2 Tesla, 1.5 Tesla or 3 Tesla scanners. The device's inputs are standard of care MRI images. The outputs are images with enhanced image quality.

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

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are divided into two main performance tests: noise reduction and sharpness increase.

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

The exact sample size for the test set is not explicitly stated in the provided document. It refers to "each test dataset" for the noise reduction test and "at least 90% of the test datasets" for the sharpness increase test, indicating multiple datasets were used.

The data provenance is stated as retrospective clinical data. The country of origin is not specified.

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

The document does not specify the number of experts used or their qualifications for establishing the ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1) for the test set. The evaluation seems to have been based on quantitative metrics (SNR) and a Likert scale assessment, but the process of aggregation or reconciliation if multiple readers were involved is not described.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study to assess how much human readers improve with AI vs. without AI assistance. The performance tests described focus on quantitative image quality metrics (SNR, sharpness) and a perceptual assessment of small structures, not a reader study of diagnostic accuracy or efficiency.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the described performance tests appear to be standalone (algorithm only) evaluations. The metrics (SNR, Likert scale for structure visibility, and sharpness/thickness improvement percentages) directly assess the output of the algorithm on the images, rather than measuring reader performance with and without the algorithm. The device itself is described as having "no user interface," further suggesting a standalone processing function.

7. The Type of Ground Truth Used

The ground truth for the noise reduction test appears to be derived from a quantitative measurement (SNR) and a perceptual assessment (Likert scale for small structures). For the sharpness increase test, it was based on assessing the improvement in thickness of anatomic structures and sharpness of structure boundaries. These are essentially expert-defined metrics or assessments applied to the processed images, rather than external pathology or outcomes data.

8. The Sample Size for the Training Set

The document does not provide the sample size for the training set. It mentions that the algorithm uses a "convolutional network-based algorithm" whose "parameters... were obtained through an image-guided optimization process," implying a training phase, but the details of the training data are not included in this summary.

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

The document does not explain how the ground truth for the training set was established. It only states that the "parameters of the filters were obtained through an image-guided optimization process," which is vague regarding the ground truth data used for this optimization. For image enhancement tasks, ground truth often involves pairs of original and "ideal" or "target" enhanced images, or noise-free versions of images, but this is not detailed here.

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