(104 days)
The Swoop Point-of-Care Magnetic Resonance 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™ Point-of-Care MRI 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™ Point-of-Care MRI System user interface includes touchscreen menus, controls, indicators, and navigation icons that allow the operator to control the system and to view imagery.
This subject device in this submission includes a change to the image reconstruction algorithm of the Swoop POC MRI device for the T1W, T2W, and FLAIR sequences. The image reconstruction change utilizes deep learning to provide improved image quality, specifically in terms of reductions in image noise and blurring. This change replaces the non-uniform FFT-gridding operation in the reconstruction pipeline with Advanced Gridding and adds an Advanced Denoising step in the image postprocessing stage. All other sections of the image reconstruction pipeline are unchanged with respect to those used in the previously cleared system (K201722/K211818).
The provided text describes the acceptance criteria for a new image reconstruction algorithm in the Swoop™ Point-of-Care Magnetic Resonance Imaging (POC MRI) System and the testing conducted to demonstrate substantial equivalence.
Here's a breakdown of the requested information:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Advanced Reconstruction Verification | |
| Advanced reconstruction models do not alter image features or introduce artifacts. | Passed |
| Ability for expert-mode users to toggle between linear reconstruction and advanced reconstruction. | Passed |
| Image quality with advanced reconstruction is acceptable. | Passed (specifically, provides "improved image quality, specifically in terms of reductions in image noise and blurring.") |
| Basic software functionality is unchanged between releases. | Passed |
| NESSUS scan test to verify any vulnerabilities and serve as a security baseline. | Passed |
| Advanced Reconstruction Performance Analysis | |
| Robustness, stability, and generalizability of the advanced reconstruction models. | Passed |
| Image Performance | |
| Meets all image quality criteria (based on NEMA and ACR standards). | Passed |
| Advanced Reconstruction Validation | |
| Meets user needs and performs as intended. | Passed |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document does not explicitly state the sample size for the test set or the data provenance (country of origin, retrospective/prospective). It mentions "testing to verify image quality with advanced reconstruction is acceptable" and "Validation studies to confirm that the device meets user needs and performs as intended" but lacks specific details about the patient data used for these tests.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
The document does not specify the number of experts or their qualifications used to establish ground truth for the test set. It mentions "expert-mode users" and "trained physician" (in the Indications for Use), but no further details about their roles in evaluating the test set.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
The document does not describe any specific adjudication method (like 2+1 or 3+1) used for the test set.
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
The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or any effect size related to human reader improvement with AI assistance. The focus of the submission is on the image reconstruction algorithm itself, which utilizes deep learning to improve image quality, rather than focusing on a human-in-the-loop performance study.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, standalone performance was assessed for the algorithm. The testing described focuses on the "image reconstruction algorithm" and its "image quality" improvements, such as "reductions in image noise and blurring." This implies an evaluation of the algorithm's output (images) without necessarily involving human interpretation as the primary endpoint for all tests. The "Advanced Reconstruction Verification," "Advanced Reconstruction Performance Analysis," and "Image Performance" tests are all indicative of standalone algorithm evaluation.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
The document does not explicitly state the type of ground truth used for evaluating the image quality and performance of the advanced reconstruction algorithm. Given the nature of MRI image quality assessment, it is highly likely that expert visual assessment and potentially quantitative metrics (derived from NEMA and ACR standards, which are referenced) against established benchmarks or phantom data would have been used. However, "expert consensus," "pathology," or "outcomes data" are not directly cited as the ground truth.
8. The sample size for the training set
The document does not specify the sample size used for the training set of the deep learning image reconstruction algorithm.
9. How the ground truth for the training set was established
The document does not describe how the ground truth for the training set was established. It only states that the device "utilizes deep learning to provide improved image quality" but does not elaborate on the training process or ground truth generation.
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November 17, 2021
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square are the words "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Hyperfine, Inc. % Christine Kupchick Sr. Regulatory Specialist 530 Old Whitfield Street GUILFORD CT 06437
Re: K212456
Trade/Device Name: Swoop" Point-of-Care Magnetic Resonance Imaging (POC MRI) System Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic resonance diagnostic device Regulatory Class: Class II Product Code: LNH, MOS Dated: October 20, 2021 Received: October 21, 2021
Dear Christine Kupchick:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see
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https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE(@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Julie Sullivan -S
for
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
510(k) Number (if known) K212456
Device Name Swoop™ Point-of-Care Magnetic Resonance Imaging System
Indications for Use (Describe)
The Swoop Point-of-Care Magnetic Resonance 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.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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510(k) Summary Swoop™ Point-of-Care Magnetic Resonance Imaging (POC MRI) System K212456
SUBMITTER INFORMATION
| Company Name: | Hyperfine, Inc. |
|---|---|
| Company Address: | 530 Old Whitfield StGuilford. CT 06437 |
CONTACT
| Name: | Christine Kupchick |
|---|---|
| Telephone: | (203) 343-3404 |
| Email: | ckupchick@hyperfine.io |
Date Prepared: October 20, 2021
DEVICE IDENTIFICATION
| Trade Name: | Swoop™ Point-of-Care Magnetic Resonance Imaging (POC MRI)System |
|---|---|
| Common Name: | Magnetic Resonance Imaging |
| Regulation Number: | 21 CFR 892.1000 |
| Classification Name: | System, Nuclear Magnetic Resonance Imaging Coil, Magnetic Resonance, Specialty |
| Product Code: | LNH; MOS |
| Regulatory Class: | Class II |
PREDICATE DEVICE INFORMATION
The subject Swoop™ POC MRI System is substantially equivalent to the predicate POC MRI Scanner System (K201722/K211818).
DEVICE DESCRIPTION
The Swoop™ Point-of-Care MRI 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
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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™ Point-of-Care MRI System user interface includes touchscreen menus, controls, indicators, and navigation icons that allow the operator to control the system and to view imagery.
This subject device in this submission includes a change to the image reconstruction algorithm of the Swoop POC MRI device for the T1W, T2W, and FLAIR sequences. The image reconstruction change utilizes deep learning to provide improved image quality, specifically in terms of reductions in image noise and blurring. This change replaces the non-uniform FFT-gridding operation in the reconstruction pipeline with Advanced Gridding and adds an Advanced Denoising step in the image postprocessing stage. All other sections of the image reconstruction pipeline are unchanged with respect to those used in the previously cleared system (K201722/K211818).
INDICATIONS FOR USE
The Swoop™ Point-of-Care Magnetic Resonance 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.
SUBSTANTIAL EQUIVALENCE DISCUSSION
| Specification | Subject Swoop POC MRI System | Predicate Swoop POC MRI System(K201722/K211818) |
|---|---|---|
| Intended Use/Indications for Use: | The Swoop Point-of-Care MagneticResonance Imaging System is a bedsidemagnetic resonance imaging device forproducing images that display theinternal structure of the head where fulldiagnostic examination is not clinicallypractical. When interpreted by a trainedphysician, these images provideinformation that can be useful indetermining a diagnosis. | Same |
| Patient Population: | Adult and pediatric patients (≥ 0 years) | Same |
| Anatomical Sites: | Head | Same |
| Environment of Use: | At the point of care in medical facilities,including emergency rooms, critical careunits, hospital or rehabilitation rooms. | Same |
| Energy Used and/or delivered: | Magnetic Resonance | Same |
| Magnet: | ||
| Physical Dimensions | 835 mm x 630 mm x 652 mm | Same |
| Bore Opening | 610 mm x 315 mm | Same |
| Weight | 320 kg | Same |
| Field Strength | 63.3 mT permanent magnet | Same |
The table below compares the subject device to the predicate.
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| Specification | Subject Swoop POC MRI System | Predicate Swoop POC MRI System(K201722/K211818) |
|---|---|---|
| Gradient: | ||
| Strength | 24 mT/m | Same |
| Rise Time | 1.1 ms | Same |
| Slew Rate | 22 T/m/s | Same |
| Computer Display | Hyperfine-supplied tablet | Same |
| RF Coils: | 1 head coil | Same |
| Coil Type | TX/RX | Same |
| Coil Geometry | Form-fitting | Same |
| Inner Dimensions (mm) | 205 mm x 240 mm | Same |
| Coil Design | Linear Volume | Same |
| Patient Weight Capacity | 200 kg | Same |
| Operation Temperature | 15-30 C | Same |
| Warm Up Time | <3 minutes | Same |
| Temperature Control | No | Same |
| Humidity Control | No | Same |
| Image Reconstruction Algorithm | ||
| T1W | Advanced Gridding | Non-uniform FFT-griding |
| T2W | Advanced Gridding | Non-uniform FFT-griding |
| FLAR | Advanced Gridding | Non-uniform FFT-griding |
| DWI | Conjugate Gradient | Same |
| Image Post-Processing | Advanced Denoising (applies to T1W,T2W, and FLAIR only) | Image orientation transform |
| Image orientation transform | Geometric distortion correction | |
| Geometric distortion correction | Receive coil intensity correction | |
| Receive coil intensity correction | DICOM output | |
| DICOM output | ||
The subject device and the predicate device have intended use, operating principles, and similar technological characteristics. The subject device differs from the predicate in that it utilizes deep learning for image reconstruction. These differences do not raise new questions of safety and effectiveness as compared to the predicate
NON-CLINICAL TESTING
The subject device has similar technological characteristics as the predicate (K201722/K211818) and differs only in the utilization of deep learning for image reconstruction. As part of demonstrating substantial equivalence to the predicate, a risk analysis was completed to identify the risks associated with the software modification. The following verification and validation testing were conducted to evaluate the subject device as compared to the predicate. The subject device passed all the testing in accordance with internal requirements and applicable standards to support substantial equivalence.
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| Test | Test Description | Applicable Standard(s) |
|---|---|---|
| AdvancedReconstructionVerification | Testing to verify advanced reconstruction models donot alter image features or introduce artifacts. | • IEC 62304:2006• FDA Guidance, "Guidance for theContent of Premarket Submissionsfor Software Contained in MedicalDevices" |
| Testing to verify the ability for the expert-modeusers to toggle between linear reconstruction andadvanced reconstruction. | ||
| Testing to verify that image quality with advancedreconstruction is acceptable, | ||
| Testing to verify basic software functionality isunchanged between releases. | ||
| NESSUS scan test to verify any vulnerabilities andserve as a security baseline. | ||
| AdvancedReconstructionPerformanceAnalysis | Analysis of the verification completed to assessrobustness, stability, and generalizability of theadvanced reconstruction models. | |
| ImagePerformance | Testing to verify image performance with advancedreconstruction meets all image quality criteria. | • 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 ofthe Large MRI Phantom for the ACRMRI Accreditation Program• American College of Radiologystandards for named sequences |
| AdvancedReconstructionValidation | Validation studies to confirm that the device meetsuser needs and performs as intended. | • FDA Guidance, "Guidance for theContent of Premarket Submissionsfor Software Contained in MedicalDevices" |
The following testing was leveraged from the predicate device as provided in K201722. Test results from the predicate were used to support the subject device because the conditions were identical or the subject device modifications did not introduce a new worst-case configuration or scenario for testing.
| Test | Test Description | Applicable Standard(s) |
|---|---|---|
| Biocompatibility | Biocompatibility testing of patient-contactingmaterials. | ISO 10993-1:2018 ISO 10993-5:2009 ISO 10993-10:2010 |
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| Cleaning/Disinfection | Cleaning and disinfection validation of patient-contacting materials. | FDA Guidance, “ReprocessingMedical Devices in Health CareSettings: Validation Methods andLabeling” ISO 17664:2017 ASTM F3208-17 |
|---|---|---|
| Safety | Electrical Safety, EMC, and Essential Performancetesting. | ANSI/AAMI ES 60601-1:2005/(R)2012 IEC 60601-1-2:2014 IEC 60601-1-6:2013 |
| Cybersecurity | Testing to verify cybersecurity controls andmanagement. | Cybersecurity as recommended inFDA guidance, “Content ofPremarket Submissions forManagement of Cybersecurity inMedical Devices” |
| Performance | Characterization of the Specific Absorption Rate forMagnetic Resonance Imaging Systems. | NEMA MS 8-2016 |
CONCLUSION
Based on the indications for use, technological characteristics, performance results, and comparison to the predicate, the subject Swoop Point-of-Care Magnetic Resonance Imaging (POC MRI) System has been shown to be substantially equivalent to the predicate device identified in this submission and does not present any new issues of safety or effectiveness.
§ 892.1000 Magnetic resonance diagnostic device.
(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.