Retrospective clinical data from the ADNI cohort was used to conduct internal and external validation of the device's prognostic similarity score, including performance metrics (AUROC, sensitivity, specificity) and censoring-adjusted Kaplan-Meier analysis.
Independent ADNI holdout cohort of 180 subjects from 12 held-out U.S. sites
—
Indications for Use
NEXT-Cue is a software application indicated as a prognostic tool to aid in the clinical assessment of patients aged 55 through 88 years who are diagnosed with mild cognitive impairment (MCI) and are being evaluated for progression to clinical Alzheimer's disease (AD) dementia within five years of assessment. NEXT-Cue analyzes inputs of brain magnetic resonance imaging (MRI), patient demographic information, and cognitive assessment scores to produce a similarity score between 0 and 100 reflecting the degree of similarity between the patient and two reference populations of MCI patients who either did not progress to clinical AD-dementia within 5 years (non-converters) or progressed to clinical AD-dementia within 5 years (converters). NEXT-Cue is intended to be used in clinical environments by trained healthcare professionals as a supplementary tool, and its outputs are meant to support, not replace, comprehensive clinical evaluation and must be interpreted alongside clinical history, physician examination, and other diagnostic tests. NEXT-Cue is not intended to be used as a diagnostic tool for MCI or Alzheimer's disease.
Device Story
Software-only device; cloud-based. Inputs: T1-weighted structural brain MRI, patient demographics, cognitive assessment scores. Processing: Automated image analysis and machine learning-based algorithms generate a 0-100 similarity score comparing patient data to reference populations (converters vs. non-converters to AD dementia). Used in clinical environments by trained healthcare professionals. Output: Similarity score provided in a clinical report to support prognostic assessment. Clinicians interpret output alongside clinical history, physical exam, and other tests. Benefits: Provides supplementary prognostic information to aid clinical decision-making regarding patient monitoring and management. Not a standalone diagnostic tool.
Clinical Evidence
Clinical evaluation included internal validation (5-fold cross-validation, n=487) and external validation (n=180). Internal validation AUROC: 0.9350. External validation AUROC: 0.9142. Censoring-adjusted analysis through 60 months showed 71.3% sensitivity (95% CI: 61.6%–80.9%) and 93.7% specificity (95% CI: 88.5%–98.0%). Results indicate sensitivity point estimate met the 70% criterion, though the lower bound of the CI fell below it. Performance is considered acceptable for a supplementary tool.
Indicated for patients aged 55-88 years with mild cognitive impairment (MCI) being evaluated for progression to clinical Alzheimer's disease (AD) dementia within 5 years. Not for use as a diagnostic tool for MCI or AD.
Regulatory Classification
Identification
Prognostic assessment software of mild cognitive impairment is a prescription-only device that combines information from multiple clinical data sources to produce an output indicative of the likelihood that a patient diagnosed with mild cognitive impairment will progress to more severe forms of cognitive impairment, or will develop dementia, such as Alzheimer's disease dementia, within a specified period of time. This device is intended for adjunctive use and not intended as a stand-alone diagnostic or prognostic tool.
Special Controls
In combination with the general controls of the FD&C Act, the prognostic assessment software of mild cognitive impairment is subject to the following special controls:
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**U.S. FOOD & DRUG**
ADMINISTRATION
October 8, 2026
Neuroxt, Inc.
Sunyoung Kim
Regulatory Affairs Team Leader
#917-919, 48, Ahasan-Ro 17-Gil, Seongdong-Gu
Seoul, 04799
Republic Of Korea
Re: K261805
Trade/Device Name: NEXT-Cue
Regulation Number: 21 CFR 882.1454
Regulation Name: Prognostic Assessment Software Of Mild Cognitive Impairment
Regulatory Class: Class II
Product Code: QWT
Dated: May 31, 2026
Received: June 1, 2026
Dear Sunyoung Kim:
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.
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Before making any change significantly affecting the safety or effectiveness of the device, you must submit a new premarket notification in accordance with 21 CFR 807.81. 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
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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-devices/device-advice-comprehensive-regulatory-assistance/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,
# Patrick Antkowiak -S
Patrick Antkowiak, PhD
Assistant Director
DHT5A: Division of Neurosurgical,
Neurointerventional, and
Neurodiagnostic Devices
OHT5: Office of Neurological and
Physical Medicine Devices
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
# **Indications for Use**
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.
510(k) Number (if known)
K261805
Device Name
NEXT-Cue
Indications for Use (Describe)
NEXT-Cue is a software application indicated as a prognostic tool to aid in the clinical assessment of patients aged 55 through 88 years who are diagnosed with mild cognitive impairment (MCI) and are being evaluated for progression to clinical Alzheimer's disease (AD) dementia within five years of assessment.
NEXT-Cue analyzes inputs of brain magnetic resonance imaging (MRI), patient demographic information, and cognitive assessment scores to produce a similarity score between 0 and 100 reflecting the degree of similarity between the patient and two reference populations of MCI patients who either did not progress to clinical AD-dementia within 5 years (non-converters) or progressed to clinical AD-dementia within 5 years (converters).
NEXT-Cue is intended to be used in clinical environments by trained healthcare professionals as a supplementary tool, and its outputs are meant to support, not replace, comprehensive clinical evaluation and must be interpreted alongside clinical history, physician examination, and other diagnostic tests. NEXT-Cue is not intended to be used as a diagnostic tool for MCI or Alzheimer's disease.
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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FORM FDA 3881 (8/23)
Page 1 of 1
PSC Publishing Services (301) 443-6740 EF
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NeuroXT Inc.
# NEXT-Cue 510(k) Summary
This 510(k) Summary is being provided in accordance with 21 CFR 807.92.
## 1. Submitter Information
Submitter: NeuroXT Inc.
Address: 917-919, 48, Achesan-ro 17-gil, Seongdong-gu, 04799 Seoul, Republic of Korea.
Contact Person: Sunyoung Kim, Leader, Regulatory Affairs Team.
Phone: +82 10 7416-2337.
Email: sykim@neuroxt.com.
## 2. Device Name
Trade/Proprietary Name: NEXT-Cue.
Common/Usual Name: Brain imaging analysis software.
Classification Name / Regulation: Prognostic assessment software for mild cognitive impairment, 21 CFR 882.1454.
Product Code: QWT.
## 3. Predicate Device
The predicate device for this submission is BrainSee, DEN220066.
## 4. Device Description
NEXT-Cue is a software-only medical device that provides artificial intelligence-supported analysis of T1-weighted structural brain MRI and selected clinical information to support prognostic assessment in patients with mild cognitive impairment (MCI). The software receives input data including T1-weighted structural brain MRI, patient demographic information, and cognitive assessment scores, and processes these data using automated image analysis and machine learning-based algorithms.
Based on these inputs, NEXT-Cue generates a similarity score between 0 and 100 reflecting the degree of similarity between the patient and two reference populations of MCI patients who either did not progress to clinical Alzheimer's disease (AD) dementia within 5 years (non-converters) or progressed to clinical AD dementia within 5 years (converters). NEXT-Cue supports clinical workflows through case management, automated analysis-status tracking, report generation, and report-delivery functions.
## 5. Intended Use and Indications for Use
NEXT-Cue is a software application indicated as a prognostic tool to aid in the clinical assessment of patients aged 55 through 88 years who are diagnosed with mild cognitive impairment (MCI) and are being evaluated for progression to clinical Alzheimer's disease (AD) dementia within five years of assessment.
NEXT-Cue analyzes inputs of brain magnetic resonance imaging (MRI), patient demographic information, and cognitive assessment scores to produce a similarity score between 0 and 100 reflecting the degree of similarity between the patient and two reference populations of MCI patients who either did not progress to clinical AD-dementia within 5 years (non-converters) or progressed to clinical AD-dementia within 5 years (converters).
NEXT-Cue is intended to be used in clinical environments by trained healthcare professionals as a supplementary tool, and its outputs are meant to support, not replace, comprehensive clinical evaluation and must be interpreted alongside clinical history, physician examination, and other diagnostic tests. NEXT-Cue is not intended to be used as a diagnostic tool for MCI or Alzheimer's disease.
## 6. Substantial Equivalence Comparison
NEXT-Cue is compared with the predicate device, BrainSee (DEN220066), with respect to regulatory classification, indications for use, intended patient and user populations, prescription-use status, clinical role, and technological characteristics. Both devices combine structural brain MRI, demographic information, and cognitive assessment
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information to generate a 0–100 similarity score for supplementary prognostic assessment of progression to clinical Alzheimer's disease dementia within five years.
The comparison below identifies similarities and differences in the devices' labeling and implementation. NEXT-Cue is indicated for patients aged 55 through 88 years diagnosed with mild cognitive impairment (MCI), whereas BrainSee is indicated for patients aged 55 through 95 years diagnosed with amnestic mild cognitive impairment (aMCI). These labeling differences are identified explicitly and considered together with the clinical performance evidence and limitations described in Section 7.
The regulatory, intended-use, user-related, and technological characteristics of NEXT-Cue and BrainSee are summarized below.
| Device Characteristic | NEXT-Cue | Predicate Device: BrainSee | Comparison and Assessment |
| --- | --- | --- | --- |
| Intended Use / Clinical Purpose | Supplementary prognostic assessment to aid clinical evaluation of progression from MCI to clinical AD dementia within five years. | Supplementary prognostic assessment to aid clinical evaluation of progression from aMCI to clinical AD dementia within five years. | Similar prognostic purpose and time horizon. The difference in labeled patient-population terminology is identified below. Neither device is intended to replace comprehensive clinical evaluation. |
| Indications for Use | NEXT-Cue is a software application indicated as a prognostic tool to aid in the clinical assessment of patients aged 55 through 88 years who are diagnosed with mild cognitive impairment (MCI) and are being evaluated for progression to clinical Alzheimer's disease (AD) dementia within five years of assessment.NEXT-Cue analyzes inputs of brain magnetic resonance imaging (MRI), patient demographic information, and cognitive assessment scores to produce a similarity score between 0 and 100 reflecting the degree of similarity between the patient and two reference populations of MCI patients who either did not progress to clinical AD-dementia within 5 years (non-converters) or progressed to clinical AD-dementia within 5 years (converters).NEXT-Cue is intended to be used in clinical environments by trained healthcare professionals as a supplementary tool, and its outputs are meant to support, not replace, comprehensive | BrainSee is a software application indicated as a prognostic tool to aid in the clinical assessment of how likely patients aged between 55 and 95 years old who are diagnosed with amnestic mild cognitive impairment (aMCI) are to progress to clinical Alzheimer's disease (AD)-dementia within 5 years of assessment. BrainSee analyzes inputs of brain magnetic resonance imaging (MRI), patient demographic information, and cognitive assessment scores to produce a similarity score between 0 and 100 reflecting the degree of similarity between the patient and two reference populations of aMCI patients who either did not progress to clinical AD-dementia within 5 years (non-converters) or progressed to clinical AD-dementia within 5 years (converters). Clinical AD-dementia is defined as probable Alzheimer's disease established according to the National Institute of Neurological and Communicative Disorders and Stroke — Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) Alzheimer's Criteria.•Similarity scores less than 50 indicate that a patient's inputs are | Similar supplementary prognostic purpose, input modalities, and similarity-score output. Differences include the labeled cognitive-impairment population, age range, and device-specific output-interpretation wording. These differences are considered with the submitted clinical performance evidence and labeling limitations. |
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| | clinical evaluation and must be interpreted alongside clinical history, physician examination, and other diagnostic tests. NEXT-Cue is not intended to be used as a diagnostic tool for MCI or Alzheimer's disease. | more similar to those of the non-converter population. •Similarity scores greater than 50 indicate that a patient's inputs are more similar to those of the converter population. •Patients with a similarity score close to 50 have inputs that are similar to both the converter and non-converter aMCI patient populations used to develop and train the BrainSee device. The similarity score output is provided along with information to assist in the interpretation of the output to aid in evaluating the prognosis of patients diagnosed with aMCI to progress to clinical AD-dementia within 5 years. The BrainSee analysis is intended to provide supplemental information for interpretation in conjunction with a standard neurological assessment. Patient management decisions should not be made solely on the results of the BrainSee analysis. BrainSee is not indicated as a stand-alone diagnostic or prognostic tool. BrainSee should not be used to diagnose aMCI. | |
| --- | --- | --- | --- |
| Intended Patient Population | Patients aged 55 through 88 years with a clinical diagnosis of MCI who are being evaluated for progression to clinical AD dementia within five years. | Patients aged 55 through 95 years with a diagnosis of aMCI who are being evaluated for progression to clinical AD dementia within five years. | Different labeled age ranges and cognitive-impairment population terminology. NEXT-Cue's age range falls within the predicate's age range. MCI and aMCI are not represented as identical patient-population labels; the clinical evidence and its limitations are described in Section 7. |
| Intended User Population | Trained healthcare professionals in clinical environments. Primary clinical interpretation is performed by appropriately qualified clinicians responsible for assessment and management of patients with MCI. | Clinicians using BrainSee to supplement clinical assessment of patients with aMCI. | Similar professional clinical-user setting. NEXT-Cue's submitted user-role description distinguishes primary clinical interpretation from technical imaging-support functions. |
| Intended User / User Role | A trained healthcare professional interprets the output together with clinical history, physician examination, cognitive assessment, neuroimaging, and other relevant diagnostic | A clinician reviews the similarity-score output as supplemental information in conjunction with a standard neurological assessment. Patient-management decisions must not be based solely on the BrainSee analysis. | Similar clinician-led interpretation and non-standalone clinical role. Device-specific workflow and role assignments do not transfer clinical decision- |
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| | information. Technical imaging-support personnel may support MRI/PACS workflow and report access but do not independently interpret the score as a patient-specific prognosis. | | making responsibility to the software. |
| --- | --- | --- | --- |
| Prescription Use Only | Yes. Prescription use only; not intended for over-the-counter use. | Yes. Prescription use under 21 CFR 801.109. | Same prescription-use status. |
| Intended Use Environment | Clinical environments with access to required MRI and clinical information; web-based software with clinical report delivery. | Cloud-based platform accessed by clinicians through a web portal for clinical information entry, MRI upload, and report review. | Similar clinical use of a cloud-based software platform. Device-specific differences in data transfer and report-delivery workflow are addressed through software and interoperability verification. |
| Device type | Cloud-based software-only medical device | Cloud-based software-only medical device | Similar. Both devices are software-based prognostic assessment tools. |
| Input data | T1-weighted structural brain MRI, patient demographic information, and cognitive assessment scores | T1-weighted brain MRI, patient age and sex, and cognitive/behavioral assessment scores, including MMSE and CDR-Sum of Boxes | Similar. Both devices combine structural brain MRI, demographic information, and cognitive assessment information. |
| Image analysis | Automated image analysis of T1-weighted structural brain MRI and machine learning-based processing | Automated processing of standard 3-dimensional T1-weighted brain MRI to derive brain tissue volumes | Similar fundamental technological approach. Both devices use automated quantitative analysis of structural brain MRI as part of the prognostic assessment workflow. |
| Algorithm | Machine learning-based algorithm operating in a locked configuration | Proprietary software algorithm generating a similarity score from MRI and clinical inputs | Similar. Both use algorithmic processing of MRI and clinical inputs to generate a prognostic similarity output. |
| Output | Similarity score between 0 and 100, reflecting similarity to MCI non-converter and converter reference populations | Similarity score between 0 and 100, reflecting similarity to aMCI non-converter and converter reference populations | Similar. Both devices provide a 0–100 similarity score for supplementary prognostic assessment. |
| User access and workflow | Web-based access with case management, automated analysis-status tracking, report generation, and delivery through the clinical workflow | Web-based platform for entering clinical information, uploading MRI data, and viewing, downloading, or printing the output report | Difference in workflow implementation. Both devices provide software-based access to a prognostic report. NEXT-Cue workflow functions are controlled through software verification and validation, interoperability testing, cybersecurity controls, and risk management. |
| User control of algorithm | Model configuration, model weights, operating threshold, and score transformation | Algorithmic processing is performed by the software; users provide required input | Similar. Users do not modify the algorithmic basis of the prognostic output during clinical use. |
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| | rule are locked and not user-modifiable | information and review the output report | |
| --- | --- | --- | --- |
| Clinical role | Supplementary prognostic tool; output must be interpreted with clinical history, physician evaluation, and other relevant diagnostic information | Adjunctive prognostic tool; not intended as a stand-alone diagnostic or prognostic tool | Similar. Neither device is intended to replace comprehensive clinical evaluation or serve as a stand-alone diagnostic tool. |
| Regulatory Classification | Proposed Class II classification under 21 CFR 882.1454, Prognostic assessment software of mild cognitive impairment; Product Code: QWT. | Class II under 21 CFR 882.1454, Prognostic assessment software of mild cognitive impairment; Product Code: QWT. | NEXT-Cue's proposed regulatory classification, regulation number, and product code match those of the predicate. |
Differences between NEXT-Cue and BrainSee include the labeled patient population and age range, as identified in the comparison table, as well as device-specific implementation of the software workflow, user interface, report delivery, and algorithm configuration. These differences do not alter the fundamental technological characteristics of the devices: both are software-based tools that combine structural brain MRI, demographic information, and cognitive assessment information to generate a 0–100 similarity score for supplementary prognostic assessment.
The performance, software verification and validation, interoperability, cybersecurity, risk management, and human factors evidence provided in this submission support that the technological differences do not raise different questions of safety and effectiveness.
## 7. Clinical Performance Data
Clinical performance evaluation of NEXT-Cue included internal validation, external validation, and subgroup analysis using pre-specified converter/non-converter reference classifications. The clinical performance evaluation assessed the ability of the locked NEXT-Cue configuration to generate a Prognitor Score for supplementary prognostic assessment of progression from MCI to clinical AD dementia.
Performance endpoints included area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Sensitivity, specificity, PPV, and NPV were evaluated at the fixed operating threshold corresponding to Prognitor Score 50.
| Study | Study Design and Population | Endpoint | Results | Conclusion |
| --- | --- | --- | --- | --- |
| Internal Validation | Five-fold subject-level cross-validation using an ADNI development cohort of 1,407 subjects. Five-year converter/non-converter performance was evaluated using pooled out-of-fold predictions from 487 subjects who met the pre-specified evaluation criteria. | AUROC; sensitivity, specificity, PPV, and NPV at Prognitor Score 50. | AUROC: 0.9350; sensitivity: 71.43%; specificity: 95.34%; PPV: 95.89%; NPV: 68.66% | The internal-validation results provide supportive development evidence within the pre-specified evaluable cohort. |
| External Validation | Independent ADNI holdout cohort of 180 subjects from 12 held-out U.S. sites, with no overlap with the 54 training sites used for model development. The original classification analysis used the pre-specified converter/non-converter reference labels and was not censoring-adjusted. | AUROC; sensitivity, specificity, PPV, and NPV at Prognitor Score 50. | AUROC: 0.9142; sensitivity: 71.91%; specificity: 97.80%; PPV: 96.97%; NPV: 78.07% | The original external-validation results are presented together with the updated censoring-adjusted analysis below and are interpreted subject to the stated study limitations. |
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| Subgroup Analysis | Subgroup analyses of the external validation cohort | Performance assessment by available demographic and clinical subgroup | Results are summarized in the Performance Test Report and interpreted in consideration of the available sample size and outcome distribution for each subgroup. | Subgroup findings are presented with applicable sample-size, outcome-distribution, and statistical-interpretability limitations. Subgroups with insufficient sample sizes or single-class outcomes are not interpreted as providing reliable subgroup-specific performance estimates. |
| --- | --- | --- | --- | --- |
### Censoring-Adjusted Performance Through 60 Months
A censoring-adjusted Kaplan–Meier analysis was conducted in the original 180-subject external-validation cohort to evaluate NEXT-Cue's performance through 60 months while accounting for incomplete follow-up. Original index visits, locked Prognitor Scores, and the pre-specified operating threshold of Prognitor Score 50 were retained. No subject was excluded based on follow-up duration. Subjects without an observed qualifying conversion event by 60 months were right-censored at the earlier of their last valid CDR assessment or 60 months after the index visit. The results are summarized below.
| Metric | Estimate | Two-sided 95% Confidence Interval |
| --- | --- | --- |
| Censoring-adjusted sensitivity | 71.3% | 61.6%–80.9% |
| Censoring-adjusted specificity | 93.7% | 88.5%–98.0% |
| Five-year PPV | 90.9% | 82.5%–96.3% |
| Five-year NPV | 78.8% | 70.0%–85.3% |
In the censoring-adjusted analysis through 60 months, the sensitivity point estimate was 71.3%, exceeding the pre-specified 70% sensitivity criterion on a point-estimate basis. However, the lower bound of the two-sided 95% confidence interval was 61.6%, below that criterion. Accordingly, these results do not demonstrate that sensitivity exceeds 70% with 95% confidence. This uncertainty reflects the cohort size and observed event distribution. The analysis also relies on the assumption that censoring is non-informative within each Prognitor Score group, conditional on observed data.
This sensitivity estimate and its associated uncertainty are considered clinically acceptable in the context of NEXT-Cue's supplementary role because patients with MCI receiving a false-negative output are likely to receive continued clinical evaluation and longitudinal follow-up for their MCI condition. Furthermore, the NEXT-Cue algorithm is tuned to favor specificity over sensitivity to reduce false-positive outputs. NEXT-Cue's output must be interpreted together with clinical history, physician evaluation, and other relevant diagnostic information and must not be used as the sole basis for discontinuing follow-up or making patient-management decisions. A lower-score output does not exclude progression to clinical AD dementia and must not be used to defer otherwise clinically indicated evaluation, cognitive and functional monitoring, referral, diagnostic work-up, or management.
PPV and NPV are prevalence-dependent; the observed converter proportion in the external validation cohort is a cohort-specific outcome composition and is not presented as the expected five-year converter prevalence in routine clinical practice.
The external validation cohort was derived from ADNI research sites and does not fully represent all demographic groups, clinical practice settings, routine MRI workflows, scanner/protocol combinations, or follow-up conditions encountered in U.S. clinical practice. The subgroup findings are descriptive and do not establish equivalent performance across demographic strata. The sponsor has committed to prospective non-ADNI U.S. post-market evidence generation for the locked released configuration.
The Prognitor Score is a similarity score and is not an individual patient-specific probability of progression to clinical AD dementia.
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## 8. Nonclinical Performance Data
Nonclinical testing and assessments were performed to verify that NEXT-Cue meets its specified software, image-quality, interoperability, cybersecurity, usability, and risk-management requirements. The results are summarized below.
| Test Category | Applicable Standard or Method | Results | Conclusion |
| --- | --- | --- | --- |
| Image Quality Control Validation | Staged image quality control framework applied to 436 3D T1-weighted MRI scans | The framework achieved an overall catch rate of 82.1% for non-conforming scans and maintained 100.0% specificity and 100.0% precision for block decisions in the locked framework. | Image quality control functions appropriately identified non-conforming scans and prevented inappropriate input data from proceeding to analysis. |
| Software Verification and Validation | Verification and validation against specified software requirements, including unit, integration, and system-level testing | NEXT-Cue v1.0.0.0 met specified requirements and intended-use requirements. Overall software validation result: PASS. | Software functions were verified and validated for the submitted configuration. |
| Interoperability and Workflow Verification | Verification of EHR/PACS-related data handling, analysis-status tracking, report generation, and report delivery functions | Required workflow functions, status handling, failure handling, and report-delivery functions were verified for the submitted configuration. | The device supports the intended automated clinical workflow with defined status, failure, and recovery controls. |
| Cybersecurity Verification and Risk Management | Cybersecurity lifecycle controls, access control, audit logging, session management, software update controls, and applicable risk-management activities | Security-related controls were verified for the submitted configuration. Residual cybersecurity risks were evaluated through the risk-management process. | Cybersecurity controls support controlled access, traceability, and secure operation within the intended use environment. |
| Human Factors / Usability Assessment | Risk-based human factors and usability assessment, including use-related risk analysis of the intended users, use environments, user interface, and foreseeable use errors. | The use-related risk analysis identified no critical tasks that, if performed incorrectly or not performed at all, could reasonably foreseeably result in serious harm to patients. | The assessment supports that human factors validation testing data are not needed to support the substantial equivalence determination for NEXT-Cue. |
| Risk Management | Risk-management process in accordance with the Risk Management Plan and applicable risk-management procedures | Residual risks were evaluated as acceptable. Limited AFAP process residual risks were accepted and monitored through post-market surveillance. | The overall residual risk was considered acceptable in relation to the intended use and expected benefits of NEXT-Cue. |
The nonclinical performance evidence supports that NEXT-Cue meets its specified requirements and that the submitted device configuration provides appropriate controls for image quality, software operation, clinical workflow, cybersecurity, usability, and use-related risks.
## 9. Substantial Equivalence
Both NEXT-Cue and BrainSee are software-based devices that use structural brain MRI, demographic information, and cognitive assessment information to generate a 0–100 similarity score for supplementary prognostic assessment of progression from mild cognitive impairment to clinical AD dementia. The devices use different patient-population terminology in their respective labeling; the intended-use comparison and associated rationale are provided in the substantial equivalence comparison table.
The comparison presented in Section 6 identifies similarities in regulatory classification, prescription-use status, clinical purpose, and technological characteristics, and identifies differences in labeled patient populations, age ranges, and device-specific implementation. The implementation differences relate primarily to device-specific software workflow, user interface, report generation and delivery, and implementation of the locked algorithm configuration.
{12}
NEXT
NeuroXT Inc.
The performance, software verification and validation, image-quality control, interoperability, cybersecurity, human factors, and risk-management evidence provided in this submission support that the identified technological differences do not raise different questions of safety and effectiveness. Accordingly, NEXT-Cue is considered substantially equivalent to BrainSee.
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the ? button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only Software as a Medical Device, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (Software as a Medical Device), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.