The Lucy Point-of-Care Magnetic Resonance Imaging Device 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.

Device Description

Lucy is a magnetic resonance imaging (MRI) device. Its portability allows patient bedside imaging. It 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, used to operate the system, provide access to patient data, exam set-up, exam execution, and MRI image data viewing for quality control purposes as well as for cloud storage interactions. Lucy 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.

AI/ML Overview

The provided text is a 510(k) summary for the Lucy Point-of-Care Magnetic Resonance Imaging Device. While it describes the device, its intended use, and a comparison to a predicate device, it does not contain information regarding an AI component or a study that specifically proves the device meets AI-related acceptance criteria. The acceptance criteria and performance data outlined below are based on general medical device regulatory submissions and what one would expect for an AI/ML-driven medical device, assuming the device had such a component.

Therefore, many of the requested details, particularly those related to AI algorithm performance (e.g., sample sizes for test and training sets, expert adjudication, MRMC studies, standalone performance, ground truth for AI), cannot be extracted from this specific document.

Based on the provided text, the device described is a Magnetic Resonance Imaging (MRI) device, and the submission is for its substantial equivalence to a predicate MRI device. There is no mention of an AI/ML component in the provided documentation, nor any study proving an AI component meets acceptance criteria.

Therefore, the following information is what would be expected for an AI-powered medical device, but cannot be directly extracted or inferred from the provided text.

Hypothetical Acceptance Criteria and Study (if the Lucy device had an AI component):

Given that the provided text describes a hardware medical device (MRI scanner) rather than an AI/ML algorithm, the concept of "acceptance criteria" and "study that proves the device meets the acceptance criteria" in the context of AI applies to the performance of an algorithm, not the hardware. Since no AI algorithm is mentioned in this document, the following is a hypothetical structure for what such a response would look like if an AI component were present.

1. Table of Acceptance Criteria and Reported Device Performance

If the Lucy device included an AI component (e.g., for automated lesion detection or image quality assessment), the acceptance criteria would typically revolve around diagnostic accuracy metrics.

Metric (Hypothetical for AI Component)	Acceptance Criteria (Hypothetical)	Reported Device Performance (Hypothetical)
Primary Endpoint (e.g., Sensitivity for detecting XYZ condition)	≥ [Target %] for primary indication	[Achieved %]
Secondary Endpoint (e.g., Specificity for detecting XYZ condition)	≥ [Target %]	[Achieved %]
ROC AUC (for classification tasks)	≥ [Target value]	[Achieved value]
Negative Predictive Value (NPV)	≥ [Target %]	[Achieved %]
Positive Predictive Value (PPV)	≥ [Target %]	[Achieved %]
Detection Rate (for certain pathologies)	Within [X]% of expert consensus	[Achieved %]
False Positives per scan	≤ [Target number]	[Achieved number]
False Negatives per scan	≤ [Target number]	[Achieved number]

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

Sample Size (Hypothetical): Typically, several hundreds to thousands of relevant cases are used for a robust test set for AI/ML medical devices. For example, 500-1000 unique patient studies.
Data Provenance (Hypothetical): Data from diverse geographic locations (e.g., multi-center studies including US, Europe, Asia) to ensure generalizability. Data would ideally be a mix of retrospective (for efficiency) and prospective (for real-world validation) collection. For initial clearance, often retrospectively collected data is used, but for broader clinical claims, prospective data is valuable.

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

Number of Experts (Hypothetical): Typically 3 to 5 independent experts are common, sometimes more for complex or ambiguous cases.
Qualifications (Hypothetical): Board-certified radiologists with specific subspecialty expertise related to the device's intended use (e.g., neuroradiologists for head MRI), with significant years of experience (e.g., 5-10+ years) in interpreting the relevant imaging studies.

4. Adjudication Method for the Test Set

Adjudication Method (Hypothetical): Common methods include:
- Majority Rule (e.g., 2+1 or 3+1): If 2 out of 3, or 3 out of 4, experts agree, that serves as the consensus ground truth. If no majority, a senior expert or a consensus discussion among experts may be employed for final arbitration.
- Consensus Panel: Experts meet and discuss all discordant cases to reach a unanimous decision.
- Primary Reader + Adjudicator: One expert makes the initial read, and another adjudicates discordant cases or a percentage of cases for quality control.

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

MRMC Study (Hypothetical for AI): Yes, for devices intended to assist human readers, an MRMC study is standard practice.
Effect Size (Hypothetical): The effect size would quantify the improvement in diagnostic performance (e.g., AUC, sensitivity, specificity, accuracy) of human readers with AI assistance compared to without AI assistance. For example, an MRMC study might show that radiologists' diagnostic accuracy for a specific condition increased by X% (e.g., 5-10%) and/or their reading time decreased by Y% when using the AI tool.

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

Standalone Performance (Hypothetical for AI): Yes, standalone performance is almost always assessed for AI algorithms to understand the intrinsic capability of the algorithm before combining it with human input. This would be reported against the adjudicated ground truth.

7. The Type of Ground Truth Used

Type of Ground Truth (Hypothetical for AI):
- Expert Consensus: The most common for imaging-based AI, established by independent highly-qualified experts.
- Pathology: Biopsy-proven results, considered the gold standard for many disease states (e.g., cancer).
- Clinical Outcomes Data: Longitudinal patient follow-up, lab tests, or other clinical findings that confirm the presence or absence of a condition.
- Hybrid: A combination of the above, often employing pathology or clinical outcomes where available, and expert consensus for cases where definitive pathological or outcome data is not feasible.

8. The Sample Size for the Training Set

Training Set Sample Size (Hypothetical for AI): This varies significantly depending on the complexity of the task, the variety of conditions, and the imaging modality. It could range from thousands to hundreds of thousands or even millions of images/studies, often augmented with data synthesis techniques. For medical imaging, tens of thousands of studies are often used for robust training.

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

Training Set Ground Truth (Hypothetical for AI): This is typically less rigorously established than the test set ground truth due to the sheer volume of data, but must still be reliable. Methods include:
- Single Expert Annotation: A single trained expert (e.g., radiologist, technologist) labels the data.
- Automated Labeling from Reports: NLP tools might extract labels from existing clinical reports, followed by human review of a subset.
- Crowdsourcing (with Quality Control): For certain tasks, a large group of annotators might be used, with mechanisms for quality control and consensus.
- Referral to Clinical Records/EHR: Labels derived from the electronic health record (e.g., diagnosis codes, lab results) can serve as weak labels.
- Existing Clinical Labels: Utilizing labels already present in de-identified clinical datasets.

In summary, the provided document from the FDA clearance K192002 for the "Lucy Point-of-Care Magnetic Resonance Imaging Device" describes a hardware MRI system and its substantial equivalence to a predicate MRI system. It does not refer to any AI/ML component or associated performance studies.

Summary

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February 6, 2020

Hyperfine Research, Inc. % Mr. Brian Sawin Sr. Regulatory Affairs Manager 530 Old Whitfield Street GUILFORD CT 06437

Re: K192002

Trade/Device Name: Lucy Point-of-Care Magnetic Resonance Imaging Device Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic resonance diagnostic device Regulatory Class: Class II Product Code: LNH Dated: January 7, 2020 Received: January 9, 2020

Dear Mr. Sawin:

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

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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see 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.

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)

K192002

Device Name

Lucy Point-of-Care Magnetic Resonance Imaging Device

Indications for Use (Describe)

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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Image /page/3/Picture/0 description: The image shows the word "HYPERFINE" in large, blue, sans-serif font. Below the word is the address "530 Old Whitfield Street, Guilford, CT 06437" followed by the phone number "(203) 204-6900". The address and phone number are in a smaller, black, sans-serif font. The image is likely a logo or contact information for a company called Hyperfine.

510(k) Summary of Safety and Effectiveness

K192002

Submitter Information

Submitter Name and Address

Hyperfine Research, Inc. 530 Old Whitfield St. Guilford, CT 06437 USA (tel.) 203.204.6900 (fax) 203.458.2514 www.hyperfine.io

Contact Person

Brian Sawin Sr. Regulatory Affairs Manager 203.204.6900 bsawin@hyperfine.io

Date Prepared

February 6, 2020

Subject Device - Proprietary/Trade Name

The Lucy Point-of-Care Magnetic Resonance Imaging Device

Subject Device - Common Name

Magnetic Resonance Imaging (MRI)

Classification Name

	Regulation Number	Product Code
System, Nuclear Magnetic ResonanceImaging	892.1000	90-LNH
Coil, Magnetic Resonance, Specialty	892.1000	90-MOS

Classification

Class II

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Predicate Device:

K170978 - Embrace Neonatal MRI System, Aspect Imaging Ltd.

Device Summary:

The user interface includes touch screen menus, controls, indicators and navigation icons that allow the operator to control the system and to view imagery.

Indications for Use:

Comparison ofSpecifications	Embrace Neonatal MRI System(K170978)	Lucy Point-of Care MagneticResonance Imaging device
IntendedUse/Indications forUse	The Embrace Neonatal MRI Systemis indicated for use as a magneticresonance imaging device forproducing axial, sagittal, coronaland oblique images that displaysthe internal structure of neonatalhead with a circumference of up to38 cm and weight between 1Kg and4.5 Kg. When interpreted by atrained physician, these imagesprovide information that can beuseful in determining a diagnosis.	The Lucy Point-of-Care MagneticResonance Imaging Device is abedside magnetic resonanceimaging device for producingimages that display the internalstructure of the head where fulldiagnostic examination is notclinically practical. Wheninterpreted by a trainedphysician, these images provideinformation that can be useful indetermining a diagnosis.
Patient Population	Patients requiring MR images ofthe Neonatal Head	Adult and pediatric patients(above 2 years old)
Anatomical Sites	Neonatal Head	Head
Environment of Use	Hospital setting	At the point of care in medicalfacilities including emergencyrooms, critical care units,hospital or rehabilitation rooms.
Energy Used and/ordelivered	Magnetic Resonance	Magnetic Resonance
Human Factors	The Embrace Neonatal MRI Systemis designed similar to othercommercially available MRISystems and therefore is familiarand easy for use for the user.Furthermore, the device contains auser-friendly software interfacethrough which the user may easilyaccess all device functions.	Lucy is designed similar to othercommercially available MRISystems and therefore isfamiliar and easy to use for theuser. Furthermore, the devicecontains a user-friendly softwareinterface through which the usermay easily access all devicefunctions
Magnet:
PhysicalDimensions	1710 mm x 1450 mm x 1810 mm	835 mm x 630 mm x 652 mm
Bore Opening	180 x 260 mm	610 mm x 315 mm
Weight	5500 (5680 with bed) Kg	320 kg
Field Strength	1 Tesla permanent magnet	64 mT permanent magnet
Gradient:
Strength	150 mT/m	16 mT/m
Rise Time	0.3 mSec	0.5 ms
Slew Rate	500 T/m/Sec	28 T/m/s
Computer:
Display	24" LED Display	User supplied tablet
RF Coils:	1 Head Coil	1 Head Coil
Coil Type	TX/RX	TX/RX
Coil Geometry	Cylindrical	Form-fitting
Inner Dimensions (mm)	143mm Diameter	205 mm x 240 mm
Coil Design	Linear Volume	Linear Volume
Target Population	Neonates with head circumference ofup to 38 cm and weight between 1Kgand 4.5 Kg	Adult and pediatric patients(above 2 years old)
Patient bed dimensions	60.6cm W x 120cm H x 140cm L	n/a
Patient Weight Capacity	4.5 Kg Max	200 kg
Operation Temperature	20.5°C - 36.5°C	18-25 °C
Warm Up Time	50 minutes	3 minutes
Temperature Control	Air	No
Humidity Control	No	No

Comparison of Specifications:

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Summary of Technological Characteristics

The Lucy Point-of-Care Magnetic Resonance Imaging Device has different technological characteristics from the predicate, including field strength and portability; however, the technology meets the same intended use and performs the same actions.

Summary of Safety and Performance

Verification and validation activities were designed and performed to demonstrate that the Lucy Point-of-Care Magnetic Resonance Imaging Device meets pre-determined performance specifications. Clinical images were provided and demonstrate adequate diagnostic quality for the intended use. The following standards in conjunction with inhouse protocols were used to determine appropriate methods for evaluating the performance of the device:

IEC 60601-1:2005, MOD. Medical Electrical Equipment - Part 1: General Requirements for Safety

IEC 60601-2-33: Edition 2.0 - 2015, Medical Electrical Equipment - Part 2-33: Particular Requirements for the Basic Safety and Essential Performance of Magnetic Resonance Equipment for Medical Diagnosis

IEC 60601-1-2: Edition 4.0 - 2014, Medical Electrical Equipment- Part 1-2: General Requirements for Safety - Collateral Standard: Electromagnetic Compatibility - Requirements and Tests

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IEC 60601-1-6: Edition 3.1 - 2013, Medical Electrical Equipment - Part 1-6: General Requirements for Basic Safety and Essential Performance - Collateral Standard: Usability

ISO 10993:2009 Biological Evaluation of Medical Devices. Part 1

ISO 14971: 2012 Application of Risk Management to Medical Devices

IEC 62304: 2006 (Amendment 1) Medical Device Software – Software Lifecycle Process

NEMA MS 1-2008 (R2014) Determination of Signal-to-Noise Ratio (SNR) in Diagnostic Maqnetic Resonance Imaging

NEMA MS 3-200 (R2014) Determination of Image Uniformity in Diagnostic Magnetic Resonance Images

NEMA MS 8 2008 Characterization of the Specific Absorption Rate for Magnetic Resonance Imaging Systems

NEMA MS 9-2008 (R2014) Characterization of Phased Array Coils for Diagnostic Magnetic Resonance Images

NEMA MS 12 Quantification and Mapping of Geometric Distortion for Special Applications

Summary of Substantial Equivalence:

Based on the indications for use, technological characteristics, and safety and performance testing, the subject device meets the requirements that are considered adequate for its intended use and is substantially equivalent in design, principles of operation and indications for use to the predicate device.

Regulation Number and Section

§ 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.