Neurophet SCALE PET

K252563 · Neurophet., Inc. · QIH · May 15, 2026 · Radiology

Device Facts

• Record ID: K252563
• Device Name: Neurophet SCALE PET
• Applicant: Neurophet., Inc.
• Product Code: QIH · Radiology
• Decision Date: May 15, 2026
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 892.2050
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device

AI Performance

Output	Algorithm	Acceptance	Observed	Dev DS	Dev Readers	Test DS	Test Readers
Brain structure segmentation	Static deep learning technologies	Mean DSC [0.75, 0.85] for cortical; [0.80, 0.90] for subcortical	Cortical: 0.80±0.03; Subcortical: 0.85±0.03	Training: ADNI, HCP, IXI, AIBL, PPMI, and private hospital datasets from Republic of Korea	—	Independent test dataset: 64 subjects	4 (trained experts)
Standard uptake volume ratio (SUVR) quantification	Static deep learning technologies	ICC ≥ 0.6; paired SUVR differences ±0.10	FDG PET ICC ≥ 0.937; Amyloid PET ICC ≥ 0.990; 100% of paired differences within ±0.10	Training: ADNI, HCP, IXI, AIBL, PPMI, and private hospital datasets from Republic of Korea	—	Independent test dataset: 74 paired MRI-PET datasets per tracer	—
Centiloid scale calculation	Static deep learning technologies	Kappa ≥ 0.7; ICC ≥ 0.6	Kappa 0.82; ICC 0.996	Training: ADNI, HCP, IXI, AIBL, PPMI, and private hospital datasets from Republic of Korea	—	Independent test dataset: 176 paired T1-weighted MRI and amyloid PET scans	>1 (expert readers)

Indications for Use

Neurophet SCALE PET is software for the registration, fusion, display and analysis of medical images from multiple modalities including MRI and PET. The software aids clinician in the assessment and quantification of pathologies from PET scans of the human brain. It enables automatic analysis and visualization of brain PET through the calculation of standard uptake volume ratios (SUVR) and its derived quantitative values within target regions of interest and comparison to those within the reference regions. The software is deployed via medical imaging workplaces and is organized as a series of workflows which are specific to use with radio-tracer and disease combinations.

Device Story

Software-based medical image management/processing system; inputs include 3D T1-weighted MRI and PET (Amyloid/FDG) scans in DICOM format. Core operation involves automated internal pipeline: artifact correction, brain structure segmentation, SUVR calculation, and Centiloid scale conversion. Deployed on off-the-shelf Windows hardware in clinical settings; operated by clinicians. Output includes visualized brain structures with color mapping, quantitative SUVR/Centiloid values, and reports for DICOM workstations/PACS. Assists clinicians in assessing neurodegeneration and cognitive impairment; provides objective quantitative data to support clinical decision-making regarding disease severity and amyloid accumulation.

Clinical Evidence

Bench testing only. Segmentation accuracy validated on 64 subjects (mean DSC 0.80-0.85). Reproducibility assessed on 50 MR image pairs (AVDP 1.87-1.95%). SUVR quantification validated against K221405 (n=74 per tracer, ICC ≥ 0.937). Centiloid agreement validated against K252496 (n=176, ICC=0.996; kappa=0.82 vs. visual interpretation). All metrics met predefined acceptance criteria.

Technological Characteristics

Software-only device; operates on off-the-shelf Windows hardware. Utilizes static deep learning modules (T1-SegEngine) for automated segmentation of brain structures from 3D T1-weighted MRI and PET intensities. Performs SUVR and Centiloid scale calculations. Supports DICOM input/output. Includes automated quality control (tissue contrast, scan protocol, atlas alignment).

Indications for Use

Indicated for clinicians to assess and quantify pathologies from brain PET scans (e.g., Amyloid, FDG) in adult patients (approx. 20-80+ years) across diverse demographics and clinical states (normal, mild cognitive impairment, Alzheimer's disease).

Regulatory Classification

Identification

A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.

Special Controls

*Classification.* Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).

Predicate Devices

• SCALE PET (K221405)
• Neurophet AQUA AD Plus (K252496)

Submission Summary (Full Text)

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FDA U.S. FOOD & DRUG ADMINISTRATION

May 15, 2026

Neurophet., Inc.
Jin Woo Jeong
RA Team member
12f, 124, Teheran-Ro, Gangnam-Gu
Seoul, Republic Of Korea

Re: K252563
Trade/Device Name: Neurophet SCALE PET
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical image management and processing system
Regulatory Class: Class II
Product Code: QIH, LLZ
Dated: March 20, 2026
Received: March 20, 2026

Dear Jin Woo Jeong:

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 (the 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 available 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.

U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13485 clause 8.3 (Nonconforming product), ISO 13485 clause 8.5.2 (Corrective action), and ISO 13485 clause 8.5.3 (Preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and ISO 13485 clause 7.5) and document changes and approvals in the Medical Device File (ISO 13485 clause 4.2.3).

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 (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/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

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K252563 - Jin Woo Jeong
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the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email ([e-mail]) or phone [phone] or 301-796-7100).

Sincerely,


Daniel M. Krainak. Ph.D.
Assistant Director
DHT8C: Division of Radiological
Imaging and Radiation Therapy Devices
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health

Enclosure

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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.

# Indications for Use

Submission Number (if known)
K252563

Device Name
Neurophet SCALE PET

Indications for Use (Describe)
Neurophet SCALE PET is software for the registration, fusion, display and analysis of medical images from multiple modalities including MRI and PET. The software aids clinician in the assessment and quantification of pathologies from PET scans of the human brain. It enables automatic analysis and visualization of brain PET through the calculation of standard uptake volume ratios (SUVR) and its derived quantitative values within target regions of interest and comparison to those within the reference regions. The software is deployed via medical imaging workplaces and is organized as a series of workflows which are specific to use with radio-tracer and disease combinations.

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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K252563

# 510(k) Summary

[As Required by 21 CFR 807.92]

1. Date Prepared [21 CFR 807.92(a)(1)]

05/13/2026

2. Submitter's Information [21 CFR 807.92(a)(1)]

| Name of Manufacturer | NEUROPHET, Inc. |
| --- | --- |
| Address | 12F, 124, Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea. |
| Contact Name | Jin Woo Jeong |
| Telephone No. | 82-10-3614-9923 |
| Email Address | [e-mail] |

3. Identification of Proposed Device(s) [21 CFR 807.92(a)(2)]

| 510(k) Number | K252563 |
| --- | --- |
| Trade/Device/Model Name | Neurophet SCALE PET |
| Device Classification Name | Medical image management and processing system |
| Regulation Number | 21 CFR 892.2050 |
| Classification Product Code | QIH, LLZ |
| Device Class | Class II |
| 510(k) Review Panel | Radiology |

510(k) Summary

1 / 11 Neurophet SCALE PET

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510(k) Summary
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# 4. Identification of Predicate Device(s) [21 CFR 807.92(a)(3)]

The identified predicate device within this submission is shown as follow;

- Predicate device #1

| 510(k) Number | K221405 |
| --- | --- |
| Trade/Device/Model Name | SCALE PET |
| Device Classification Name | Medical image management and processing system |
| Regulation Number | 892.2050 |
| Classification Product Code | LLZ |
| Device Class | Class II |
| 510(k) Review Panel | Radiology |

- Predicate device #2

| 510(k) Number | K252496 |
| --- | --- |
| Trade/Device/Model Name | Neurophet AQUA AD Plus |
| Device Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 892.2050 |
| Classification Product Code | QIH, LLZ |
| Device Class | Class II |
| 510(k) Review Panel | Radiology |

These predicate devices have not been subject to a design-related recall.

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510(k) Summary
3 / 11 Neurophet SCALE PET

## 5. Description of the Device [21 CFR 807.92(a)(4)]

Neurophet SCALE PET is designed for aiding physicians to make a medical decision for neurodegeneration and cognitive impairment. The system streamlines clinical workflow from patient registration to analysis result archive and report generation with software-based features.

## 6. Indications for Use [21 CFR 807.92(a)(5)]

Neurophet SCALE PET is software for the registration, fusion, display and analysis of medical images from multiple modalities including MRI and PET. The software aids clinician in the assessment and quantification of pathologies from PET scans of the human brain. It enables automatic analysis and visualization of brain PET through the calculation of standard uptake volume ratios (SUVR) and its derived quantitative values within target regions of interest and comparison to those within the reference regions. The software is deployed via medical imaging workplaces and is organized as a series of workflows which are specific to use with radio-tracer and disease combinations.

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# 7. Technological Comparison [21 CFR 807.92(a)(6)]

Provided below is a table that compares technological characteristics of the Neurophet SCALE PET and the predicate device

[Table 1. Comparison of Proposed Device to Predicate Devices]

| | Proposed Device | Predicate Device #1 | Predicate Device #2 | Note |
| --- | --- | --- | --- | --- |
| K Number | K252563 | K221405 | K252496 | - |
| Manufacturer | NEUROPHET, Inc. | NEUROPHET, Inc. | NEUROPHET, Inc. | - |
| Product Name | Neurophet SCALE PET (V2.0) | Neurophet SCALE PET | Neurophet AQUA AD Plus (v3.0) | - |
| Product Code | QIH, LLZ | LLZ | QIH, LLZ | Identical. |
| Regulation Number | 892.2050 | 892.2050 | 892.2050 | Identical |
| 510(k) Review Panel | Radiology | Radiology | Radiology | Identical |
| Indications for Use | Neurophet SCALE PET is software for the registration, fusion, display and analysis of medical images from multiple modalities including MRI and PET. The software aids clinician in the assessment and quantification of pathologies from PET scans of the human brain. It enables automatic analysis and visualization of brain PET through the calculation of standard uptake volume ratios (SUVR) and its derived quantitative values within target regions of interest and comparison to those within the reference | Neurophet SCALE PET is a software for the registration, fusion, display and analysis of medical images from multiple modalities including MRI and PET. The software aids clinician in the assessment and quantification of pathologies from PET Amyloid/FDG scans of the human brain. It enables automatic analysis and visualization of amyloid protein concentration through the calculation of standard uptake volume ratios (SUVR) within target regions of interest and comparison to those | Neurophet AQUA AD Plus is intended for automatic labeling, visualization, and volumetric quantification of segmentable brain structures and lesions, as well as SUVR quantification from a set of MR and PET images. Volumetric measurements may be compared to reference percentile data. | Identical |

510(k) Summary

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| | Proposed Device | Predicate Device #1 | Predicate Device #2 | Note |
| --- | --- | --- | --- | --- |
| | regions. The software is deployed via medical imaging workplaces and is organized as a series of workflows which are specific to use with radio-tracer and disease combinations. | within the reference regions. The software is deployed via medical imaging workplaces and is organized as a series of workflows which are specific to use with radiotracer and disease combinations. | | |
| Design and Incorporated Technology | • Automatic segmentation and quantification of brain structures based on MR and PET image intensities using static deep learning technologies. • Quantifies the standardized uptake value of the region of interest and then calculates the ratio of the standardized uptake value(SUVR) by comparing it with the standardized uptake value of a referenced region. • Automatic calculation of the Centiloid scale by SUVR, which indicate the degree of amyloid accumulation, to quantify the severity of dementia. | • Automatic segmentation and quantification of brain structures based on the MR image intensity and static deep-learning technologies • Quantifies the standardized uptake value of the region of interest and then calculates the ratio of the standardized uptake value (SUVR) by comparing it with the standardized uptake value of a referenced region. | • Automated measurement of brain tissue volumes and structures and lesions • Automatic segmentation and quantification of brain structures and lesions based on MR and PET image intensities using static deep learning technologies. • Quantifies the standardized uptake value of the region of interest and then calculates the ratio of the standardized uptake value(SUVR) by comparing it with the standardized uptake value of a referenced region. • Automatic calculation of the Centiloid scale by SUVR, which | Similar |

510(k) Summary

5 / 11 Neurophet SCALE PET

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| | Proposed Device | Predicate Device #1 | Predicate Device #2 | Note |
| --- | --- | --- | --- | --- |
| | | | indicate the degree of amyloid accumulation, to quantify the severity of dementia. | |
| Physical characteristics | • Software package • Operates on off-the-shelf hardware (multiple vendors) | • Software package • Operates on off-the-shelf hardware (multiple vendors) | • Software package Operates on off-the-shelf hardware (multiple vendors) | Identical |
| Operating System | Supports windows | Supports windows | Supports windows | Identical |
| Processing Architecture | Automated internal pipeline that performs: -artifact correction -segmentation -SUVR calculation -Centiloid Scale calculation -report generation | Automated internal pipeline that performs: -artifact correction -segmentation -SUVR calculation -report generation | Automated internal pipeline that performs: -artifact correction -segmentation -lesion quantification -volume calculation -SUVR calculation -Centiloid Scale calculation -report generation | Similar |
| Data Source | • MRI scanner: 3D T1-weighted • PET scanner: Amyloid PET, FDG PET • SCALE PET Supports DICOM format as input | • MRI scanner: 3D T1-weighted • PET scanner: Amyloid PET, FDG PET • SCALE PET Supports DICOM format as input | • MRI scanner: 3D T1 and T2 FLAIR, T2* GRE / SWI MRI • PET scanner: Amyloid PET AQUA AD Plus Supports DICOM format as input | Identical |
| Output | • provides the capabilities to adjust image transparency and apply color mapping to | • provides the capabilities to adjust image transparency and apply color mapping to | • Provides volumetric measurements of brain structures and lesions | Similar |

510(k) Summary

6 / 11 Neurophet SCALE PET

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510(k) Summary
7 / 11 Neurophot SCALE PET

| | Proposed Device | Predicate Device #1 | Predicate Device #2 | Note |
| --- | --- | --- | --- | --- |
| | individual brain structures • Quantifies the standardized uptake value (SUV) of the region of interest and calculates the standardized uptake value ratio (SUVR) by comparing it with the SUV of a reference region. The calculated SUVR is then converted into a Centiloid unit and provided. • Supports DICOM format as output of results that can be displayed on DICOM workstations and Picture Archive and Communications Systems | individual brain structures • Quantifies the standardized uptake value (SUV) of the region of interest and calculates the standardized uptake value ratio (SUVR) by comparing it with that of a reference region. • Supports DICOM format as output of results that can be displayed on DICOM workstations and Picture Archive and Communications Systems | • provides the capabilities to adjust image transparency and apply color mapping to individual brain structures • Automatically compares results to reference percentile data and to prior scans when available • Quantifies the standardized uptake value (SUV) of the region of interest and calculates the standardized uptake value ratio (SUVR) by comparing it with the SUV of a reference region. The calculated SUVR is then converted into a Centiloid unit and provided. Supports DICOM format as output of results that can be displayed on DICOM workstations and Picture Archive and Communications Systems | |
| Safety | Automated quality control functions: -Tissue contrast check | Automated quality control functions: -Tissue contrast check | Automated quality control functions: -Tissue contrast check | Identical |

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| | Proposed Device | Predicate Device #1 | Predicate Device #2 | Note |
| --- | --- | --- | --- | --- |
| | -Scan protocol verification -Atlas alignment check Results must be reviewed by a trained physician | -Scan protocol verification -Atlas alignment check Results must be reviewed by a trained physician | -Scan protocol verification -Atlas alignment check Results must be reviewed by a trained physician | |

The technological parameters of the Neurophet SCALE PET are either identical or similar to those of the predicate devices, and the differences do not raise new types of questions regarding the safety and effectiveness for the proposed indications for use.

510(k) Summary

8 / 11 Neurophet SCALE PET

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# 8. Non-Clinical Test Summary

The following data were provided in support of the substantial equivalence determination:

1) Software Validation

The Neurophet SCALE PET contains enhanced document level of concern software. The software was designed and developed according to a software development process and was verified and validated. Software information is provided in accordance with FDA guidance:

- "Content of Premarket Submissions for Device Software Functions," dated June 14, 2023.

2) Performance characteristics

Neurophet SCALE PET was validated for its intended use and evaluated to determine substantial equivalence to the predicate devices. The device consists of AI modules for automated segmentation and quantitative analysis using MR and PET images. Performance characteristics were established through a series of independent tests, summarized as follows:

a) Training Data

Distinct training datasets were constructed for core module (T1-SegEngine), using clinically normal subjects, comprising a combination of public research datasets (ADNI, HCP, IXI, AIBL, and PPMI), and private hospital datasets collected from the Republic of Korea (Catholic university, Yeouido ST. Mary's hospital). Training data covered:

- Adult subjects across a broad age range (approximately 20~80+ years), with both sexes represented and including multiple racial/ethnic groups (e.g., White, Asian, Black).
- MRI acquired on major vendor platforms (GE, Siemens, Philips) at 3T using standard 3D T1-weighted.

Training and test datasets were strictly separated at the subject level. No images or manual labels from the training datasets were reused in the test datasets, ensuring independence of performance estimates.

b) Performance Test

Standalone performance tests were conducted for all modules using datasets that were completely independent from those used for development and training. These test sets reflected a variety of scanner vendors, image acquisition protocols, geographic regions, demographic backgrounds, and clinical diagnoses.

510(k) Summary
9 / 11 Neurophet SCALE PET

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T1-SegEngine: The T1-SegEngine module was validated using datasets collected from multiple acquisition sites. The dataset included both male and female subjects, with age groups ranging from 20 to 80+, and racial and ethnic groups including White, Asian, Black, and Hispanic/Latino. Clinical subgroups and potential confounders including scanner vendor, magnetic field strength, and diagnosis (clinically normal, mild cognitive impairment, and Alzheimer's disease) were evaluated through subgroup analysis. MR images were acquired across multiple sites using scanners from GE, Siemens, and Philips with both 1.5T and 3.0T field strengths.

Segmentation accuracy was assessed using Dice Similarity Coefficient (DSC) on an independent test dataset of 64 subjects (one MR image per subject) for both cortical and subcortical brain structures. The reference standard was established through manual segmentation performed by four trained experts. All regions met the predefined acceptance criteria (major cortical: mean DSC 0.80±0.03, 95% confidence interval [0.79, 0.81], acceptance range in [0.75, 0.85]; major subcortical: mean DSC 0.85±0.03, 95% confidence interval [0.84, 0.86], acceptance range in [0.80, 0.90]). Subgroup analyses stratified by sex, age, race, scanner vendor, magnetic field strength, and diagnosis demonstrated that the acceptance criteria were satisfied across all subgroups for both cortical and subcortical structures.

Reproducibility of T1-SegEngine segmentation was evaluated in 50 MR image pairs collected from the same subjects scanned within a 6- to 12-month interval, using Average Volume Difference Percentage (AVDP). All cortical and subcortical structures met the predefined acceptable range of [1.0%, 5.0%] (cortical: 1.95±0.88, 95% confidence interval [1.70, 2.20]; subcortical: 1.87±0.80, 95% confidence interval [1.65, 2.10]), and the acceptance criteria were satisfied across all subgroups.

PET-Engine: Quantitative SUVR values were validated against an FDA-cleared predicate (K221405) in 74 paired MRI-PET datasets per tracer (74 Amyloid PET and 74 FDG PET) and across acquisition sites. Excellent consistency was achieved in all regions reported to the user (all ICC values using FDG PET ≥ 0.937 and using Amyloid PET ≥ 0.990; acceptance criterion: ≥ 0.6).

Furthermore, supporting analysis in the worst-case ROIs demonstrated that all paired SUVR differences fell within the predefined range of ±0.10 for both amyloid PET (100%, 74/74) and FDG PET (100%, 74/74), confirming that the consistency criterion was met even in the most challenging ROIs.

Repeatability/Reproducibility for SUVR were validated in 49 paired MRI-PET datasets including multiple tracers and acquisition sites. All regions showed excellent consistency (ICC ≥ 0.859, minimum threshold 0.75), with low variability.

510(k) Summary

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Centiloid binary agreement was validated in 176 paired T1-weighted MRI and amyloid PET scans, with a kappa value of 0.82 meeting the acceptance criterion (κ ≥ 0.7), indicating substantial agreement between Centiloid-based binary classification (using predefined cutoff Centiloid≥30) and expert visual interpretation. Also, the consistency with an FDA-cleared predicate device (K252496) showed good results (ICC=0.996, minimum threshold 0.6) and a proportion of subjects within a predefined range of ±10CL (95.45%). Meanwhile, repeatability/reproducibility was validated in 49 paired datasets with great consistency (≥0.997, minimum threshold 0.75)

All test results demonstrate that Neurophet SCALE PET achieves segmentation and quantification performance that is substantially equivalent to its FDA-cleared predicate devices. The device demonstrates consistent and reliable performance across diverse patient populations and imaging conditions, supporting its intended use.

3) Cybersecurity

- "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions", on September 27, 2023

9. Substantial Equivalence [21 CFR 807.92(b)(1) and 807.92]

There are no significant differences between the subject, predicate device #1 (K221405) and predicate device #2(K252496) that would adversely affect the use of the product. It is substantially equivalent to these devices in indications for use and technology characteristics.

10. Conclusion [21 CFR 807.92(b)(3)]

In according with the Federal Food & Drug and cosmetic Act, 21 CFR Part 807, and based on the information provided in this premarket notification, concludes that the Neurophet SCALE PET is substantially equivalent in safety and effectiveness to the predicate device as described herein.

510(k) Summary

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