NeuroICH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of suspected ICH patients to a specialist, independent of standard of care workflow.

The device uses an artificial intelligence algorithm to analyze non-contrast CT images of the head acquired in the acute setting for findings suggestive of intracranial hemorrhage (ICH) in parallel to the ongoing standard of care image interpretation and notify an appropriate clinician of these findings. Notifications include non-diagnostic preview images that are meant for informational purposes only. The device does not alter or remove the original medical image and is not intended to be used as a diagnostic device. Images can be previewed through a mobile application.

Notified clinicians are responsible for viewing high quality images on a diagnostic viewer per the standard of care and engaging in appropriate patient evaluation in conjunction with other patient information before making care-related decisions. NeuroICH is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

Device Description

NeurolCH is a software-only parallel workflow tool designed for use by hospital networks and trained clinicians to identify and communicate prioritized images of specific patients to an appropriate specialist such as neurovascular or neurosurgical specialist independent of the standard of care workflow. NeuroICH mainly consists of an image analysis module hosted on cloud, and a mobile application for preview of notification and non-diagnostic images. The standalone software device automatically receives and analyzes non-contrast head CT (NCCT) studies of patients undergoing stroke protocol, for image features that indicate the presence of an intracranial hemorrhage (ICH) using deep learning artificial intelligence algorithm, and upon detection of a suspected ICH case, sends a notification along with non-diagnostic image on mobile application to alert a specialist clinician.

AI/ML Overview

Here's an analysis of the acceptance criteria and study details for NeuroICH, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance for NeuroICH

1. Table of Acceptance Criteria and Reported Device Performance

Metric	Acceptance Criteria (Lower Bound of 95% CI)	Reported Device Performance (95% CI)	Met Criteria?
Sensitivity	80%	94.81% (89.68% - 97.43%)	Yes
Specificity	80% (Implied by comparison to predicate)	92.53% (88.50% - 95.21%)	Yes
Accuracy	Not explicitly stated (reported for context)	93.35% (90.37% - 95.45%)	N/A
AUC	Not explicitly stated (reported for context)	0.9367	N/A
Time-to-Notification (TTN)	Comparable to predicate (0.49±0.08 min)	0.37 ± 0.20 minutes	Yes

Note: The acceptance criteria for specificity, accuracy, and AUC are not explicitly quantified in the text as a lower bound. However, the text states that the performance was "comparative to the values achieved by the predicate device Viz ICH" and that "the clinical utility and potential benefits of the classifier" were demonstrated. For Time-to-Notification, the primary criterion was comparability to the predicate device.

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

Sample Size: 376 studies.
Data Provenance: The document states "recognizable representation of positive and negative ICH cases (35.90 % ICH positive studies and 64.09 % normal studies)". No specific country of origin is mentioned, nor is it explicitly stated whether the data was retrospective or prospective. However, given it's a "retrospective, blinded study," the data provenance is retrospective.

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

Number of Experts: Three (3)
Qualifications: US board-certified Neurologists. No specific years of experience are mentioned.

4. Adjudication Method for the Test Set

The adjudication method is not explicitly stated as 2+1, 3+1, or similar. It only mentions that the ground truth was "established by three US board certified Neurologists." This implies a consensus-based approach, but the specific mechanics (e.g., majority vote, independent review with tie-breaking) are not detailed.

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

There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study being performed to assess how much human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the AI algorithm and its time-to-notification compared to standard of care and the predicate device.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone study was conducted. The performance metrics (Sensitivity, Specificity, Accuracy, AUC, and Time-to-Notification) were calculated by comparing the "NeurolCH's output to the ground truth." This indicates the algorithm's performance without direct human intervention in the detection or interpretation phase. The document specifies it's a "notification-only, parallel workflow tool," further supporting its standalone function in identifying and notifying.

7. Type of Ground Truth Used

The ground truth used was expert consensus from three US board-certified Neurologists.

8. Sample Size for the Training Set

The document does not specify the sample size used for the training set. It only mentions the test set of 376 studies.

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

The document does not provide information on how the ground truth for the training set was established. It only describes the ground truth establishment for the test set.

Summary

{0}------------------------------------------------

November 7, 2024

Image /page/0/Picture/1 description: The image shows the logos of the Department of Health and Human Services and the Food and Drug Administration (FDA). The Department of Health and Human Services logo is on the left, and the FDA logo is on the right. The FDA logo is a blue square with the letters "FDA" in white. To the right of the square, the words "U.S. FOOD & DRUG ADMINISTRATION" are written in blue.

Neurocareai Inc. Junaid Siddiq Kalia Chief Executive Officer 8992 Preston Rd Ste 110-255 Frisco. Texas 75034

Re: K241719

Trade/Device Name: NeuroICH Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological Computer Aided Triage And Notification Software Regulatory Class: Class II Product Code: QAS Dated: October 8, 2024 Received: October 8, 2024

Dear Junaid Siddiq Kalia:

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.

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"

{1}------------------------------------------------

(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 System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

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 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-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 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-device-advicecomprehensive-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-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-regulatory

{2}------------------------------------------------

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,

Samul for

Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

{3}------------------------------------------------

Indications for Use

Form Approved: OMB No. 0910-0120 Expiration Date: 07/31/2026 See PRA Statement below.

Submission Number (if known)

K241719

Device Name

NeurolCH

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)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

{4}------------------------------------------------

K241719

Image /page/4/Picture/3 description: The image is a blue banner with the words "NeuroCare.AI" in white. To the left of the text is a white outline of a brain. The banner is rectangular with rounded corners.

510(k) Summary of NeuroICH bv NEUROCAREAI INC

Applicant Name: NEUROCAREAI INC. 8992 PRESTON RD STE 110-255 FRISCO, TX 75034 Phone Number: +1 (214) 346-6083 Whatsapp: +1 (469) 954-0346
Contact Person: Junaid Kalia Chief Executive Officer Email: junaidkalia@neurocare.ai
Date Prepared: June 12, 2024

Device Name and Classification

Name of Device: NeuroICH

Classification Name: Radiological Computer Aided Triage and Notification Software

Common Name: Intracranial Hemorrhage Detection and Notification Software

Classification Panel: Radiology
Regulation Number: 21 C.F.R. § 892.2080

Regulatory Class: Class II

Product Code: QAS

Predicate Device:

Manufacturer	Device Name	Application Number
Viz.ai, Inc.	Viz ICH	K210209

{5}------------------------------------------------

Device Description:

Intended Use:

The intended use of NeurolCH software is to detect and notify neurovascular specialists or trained clinicians regarding the presence of intracranial hemorrhage in non-contrast head CT scan images. Intracranial hemorrhage is identified if any one of the subtypes - extradural, subdural, subarachnoid, intraparenchymal and intraventricular hemorrhages is detected. The device uses an artificial intelligence algorithm to analyze images in parallel to the ongoing standard of care image interpretation and present users with notifications and preview images of suspected ICH patients on the mobile application, that are meant for informational purposes only and not intended for diagnostic use.

Indications for Use:

NeurolCH is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of suspected ICH patients to a specialist, independent of standard of care workflow.

Notified clinicians are responsible for viewing high quality images on a diagnostic viewer per the standard of care and engaging in appropriate patient evaluation in coniunction with other patient information before making care-related decisions. NeurolCH is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

Comparison of Technological Characteristics:

The subject device and predicate device have equivalent indications for use as both of them analyze non-contrast head CT scans of the patients for the features suggestive of the same abnormality i.e., intracranial hemorrhage and upon detection send the notification to designated neurovascular or neurosurgical specialist.

The technological characteristics of subject and predicate devices are similar as both of them use the similar process of automatic data identification and transfer to send images from the local hospital network to a remote location for image storage, processing and analysis.

{6}------------------------------------------------

Both the subject and predicate device uses a deep learning artificial intelligence algorithm to analyze non-contrast CT scan images of the head and classify cases with suspected ICH in parallel to the ongoing standard of care image interpretation. Like the predicate device, the NeuroICH algorithm does not externalize any internal segmentation, analysis, or intermediate outputs used in determining if an ICH is present in the NCCT, nor does either algorithm mark, highlight or draw attention to the specific regions of the analyzed NCCT image.

Both NeurolCH and Viz ICH software supports a mobile application interface that allows a user to receive push notifications, preview related non-diagnostic images, and view patient details associated with a series. The NeurolCH mobile application is subject to the same non-diagnostic viewing limitations as the predicate and has the same non-diagnostic warning on the image viewing screen as the predicate. Furthermore, the mobile application for both the devices can perform similar image viewing functions (window level change, image rotation, zoom, scroll through a cine, slice change).

Similar to the predicate device, the NeurolCH software does not affect the normal standard of care workflow of the hospital and moreover does not remove cases from a reading queue of the hospital PACs system. In conclusion, the NeurolCH device is substantially equivalent to the predicate device, Viz ICH in terms of both indications for use and technological characteristics.

Parameter	Subject DeviceNeuroICH	Predicate DeviceViz ICH
Indications ofuse	NeurolCH is a notification-only,parallel workflow tool for use byhospital networks and trainedclinicians to identify andcommunicate images ofsuspected ICH patients to aspecialist, independent ofstandard of care workflow.	Viz ICH is a notification-only,parallel workflow tool for use byhospital networks and trainedclinicians to identify andcommunicate images of specificpatients to a specialist, independentof standard of care workflow.
	The device uses an artificialintelligence algorithm to analyzenon-contrast CT images of thehead acquired in the acute settingfor findings suggestive ofintracranial hemorrhage (ICH) inparallel to the ongoing standard ofcare image interpretation andnotify an appropriate clinician ofthese findings. Notificationsinclude non-diagnostic previewimages that are meant forinformational purposes only. Thedevice does not alter or removethe original medical image and isnot intended to be used as adiagnostic device. Images can be	Viz ICH uses an artificialintelligence algorithm to analyzeimages for findings suggestive of aprespecified clinical condition andto notify an appropriate medicalspecialist of these findings inparallel to standard of care forimage interpretation. Identificationof suspected findings is not fordiagnostic use beyond notification.Specifically, the device analyzesnon-contrast CT images of the brainacquired in the acute setting, andsends notifications to aneurovascular or neurosurgicalspecialist that a suspectedintracranial hemorrhage has beenidentified and recommends review
	previewed through a mobileapplication.	of those images. Images can bepreviewed through a mobileapplication.
	Notified clinicians are responsiblefor viewing high quality images ona diagnostic viewer per thestandard of care and engaging inappropriate patient evaluation inconjunction with other patientinformation before makingcare-related decisions. NeurolCHis limited to analysis of imagingdata and should not be usedin-lieu of full patient evaluation orrelied upon to make or confirmdiagnosis.	Images that are previewed throughthe mobile application may becompressed and are forinformational purposes only and notintended for diagnostic use beyondnotification. Notified clinicians areresponsible for viewingnon-compressed images on adiagnostic viewer and engaging inappropriate patient evaluation andrelevant discussion with a treatingphysician before makingcare-related decisions or requests.Viz ICH is limited to analysis ofimaging data and should not beused in-lieu of full patient evaluationor relied upon to make or confirmdiagnosis.
Devicecomponents	1. Image forwarding moduleconfigured on site machine athospital end for transferringDICOM studies.2. Image analysis softwarealgorithm hosted on AWS cloudmanaged by NEUROCAREAI.3. Mobile application softwaremodule for review of notificationand non-diagnostic images.4. Admin panel as web applicationfor registration and managementof systems, sites and cliniciansaccounts.	1. Image analysis softwarealgorithm hosted on Viz.ai'sservers.2. Mobile application softwaremodule for review of notificationand non-diagnostic images.
Anatomicalregionofinterest	Head	Head
Diagnosticapplication	Notification only	Notification only
Intended user	Neurovascular or NeurosurgicalSpecialist	Neurovascular or NeurosurgicalSpecialist
Dataacquisition	Acquires medical image data fromDICOM compliant imagingdevices and modalities.	Acquires medical image data fromDICOM compliant imaging devicesand modalities.
Dataacquisition	Non contrast CT scan of the head	Non contrast CT scan of the head

DICOMcompatible	Yes	Yes
View DICOMdata	DICOM Information about thepatient, study, and current image	DICOM Information about thepatient, study and current image
Segmentationof the regionof interest	No; the device does not mark,highlight, or direct users' attentionto a specific location in theoriginal image.	No; the device does not mark,highlight, or direct users' attentionto a specific location in the originalimage.
Al used	Yes	Yes
Notification	Yes	Yes
Previewimages	Presentation of a preview of thestudy for initial assessment notmeant for diagnostic purposes.The device operates in parallelwith the standard of care, whichremains the default option for allcases.	Presentation of a preview of thestudy for initial assessment notmeant for diagnostic purposes.The device operates in parallel withthe standard of care, which remainsthe default option for all cases.
Alterationoforiginal image	No	No
Removal ofcases fromworklist queue	No	No
Abnormalitiestriaged	ICH	ICH
Previewimageinformation	Preview images returned to theMobile application for view.	Preview images returned to theMobile application for view.

A table comparing the key features of the subject and predicate device is provided below:

{7}------------------------------------------------

{8}------------------------------------------------

Performance Data:

A retrospective, blinded study was conducted to evaluate the software's performance in identifying non-contrast (NCCT) head CT scans containing intracranial hemorrhage (ICH). Primarily 376 studies were used with recognizable representation of positive and neqative ICH cases (35.90 % ICH positive studies and 64.09 % normal studies) to calculate Sensitivity (Se), Specificity (Sp), Accuracy, Area Under the Curve (AUC) and Time-to-Notification (TTN) regarding suspected ICH case.

Sensitivity, specificity, AUC and accuracy were calculated as primary endpoints with 95% clopper-pearson confidence interval, comparing the NeurolCH's output to the ground truth as established by three US board certified Neurologists. Sensitivity and specificity on the primary dataset were observed to be 94.81% (89.68% - 97.43%) and 92.53% (88.50% - 95.21%), respectively. Because the lower bound of each confidence interval exceeded 80%, the study met the pre-specified performance goals of 80% for sensitivity and was comparative to the values achieved by the predicate device Viz ICH. In addition, the accuracy and area under the receiver operating characteristic curve (AUC) were 93.35% (90.37% -95.45%) and 0.9367 respectively, demonstrating the clinical utility and potential benefits of the classifier based on the imaging study results.

{9}------------------------------------------------

Image /page/9/Figure/2 description: The image is a plot of the receiver operating characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate. The ROC curve is a measure of the performance of a binary classification model. The area under the ROC curve (AUC) is 0.94, which indicates that the model has good performance. A dashed line is also plotted, which represents a model that has no skill.

The performance metrics were calculated for individual data distributions as well to showcase the evaluation on important data cohorts. The stratification of device performance is demonstrated in tables below:

Device Performance by Age
Age Range (Years)	Sensitivity [95% CI]	Specificity [95% CI]
22 to 50	94.87% (83.08% - 98.43%)	89.86% (80.48% - 94.93%)
50 to 70	95.83% (86.02% - 98.72%)	92.05% (84.46% - 96.04%)
70 +	93.75% (83.13% - 97.73%)	95.24% (88.39% - 98.06%)

Device Performance by Gender
Gender	Sensitivity [95% CI]	Specificity [95% CI]
Male	93.07% (86.37% - 96.55%)	90.91% (84.74% - 94.30%)

{10}------------------------------------------------

Female	100% (90% - 99.93%)	96.92% (89.48% - 99.05%)

Device Performance by Slice Thickness
Slice Thickness	Sensitivity [95% CI]	Specificity [95% CI]
2 mm ≤ Slice Thickness < 3mm	96.15% (81.03% - 99.09%)	98% (89.55% - 99.52%)
3 mm ≤ Slice Thickness ≤ 5mm	94.50% (88.51% - 97.40%)	91.10% (86.20% - 94.35%)

Device Performance by ICH Sub-Type
ICH Sub-Type	Sensitivity [95% CI]
Subdural Hemorrhage	91.89% (83.40% - 96.16%)
Intraparenchymal Hemorrhage	98.25% (90.76% - 99.58%)
Subarachnoid Hemorrhage	100% (93.40% - 99.95%)
Intraventricular Hemorrhage	96.43% (82.24% - 99.15%)
Epidural Hemorrhage	100% (71.51% - 99.77%)

Device Performance by Scanner Manufacturer
Scanner Manufacturer	Sensitivity [95% Cl]	Specificity [95% Cl]
GE Medical Systems	94.44% (81.81% - 98.30%)	95.60% (91.57% - 97.73%)
Siemens	94.25% (87.24% - 97.46%)	84.62% (72.41% - 91.93%)
Philips	100% (63.06% - 99.68%)	100% (29.24% - 99.16%)
Siemens Healthineers	N/A	75% (28.36% - 94.73%)
TOSHIBA	100% (54.07% - 99.58%)	0% (1.26% - 84.19%)

{11}------------------------------------------------

Device Performance by CT Scanner Make/Model
ScannerManufacturer	Scanner Model	Sensitivity [95% CI]	Specificity [95% CI]
GE MedicalSystems	Optima CT660	66.67% (19.41% - 93.24%)	95.10% (90.24% - 97.57%)
	Revolution CT	100% (87.66% - 99.91%)	50% (9.43% - 90.57%)
	Revolution EVO	83.33% (42.13% - 96.33%)	100% (88.43% - 99.92%)
	Discovery RT	N/A	100% (29.24% - 99.16%)
	Discovery 690	N/A	100% (54.07% - 99.58%)
	LightSpeed Pro 16	N/A	100% (15.81% - 98.74%)
Siemens	SOMATOM Drive	100% (29.24% - 99.16%)	100% (73.54% - 99.79%)
	SOMATOMPerspective	90% (58.72% - 97.72%)	88.89% (55.50% - 97.48%)
	SOMATOM DefinitionAS	100% (92.13% - 99.94%)	77.78% (44.39% - 93.33%)
	SOMATOM DefinitionAS+	100% (82.35% - 99.87%)	75% (46.19% - 90.91%)
	SOMATOM go.Up	50% (9.43% - 90.57%)	100% (39.76% - 99.37%)
	Perspective	70% (39.03% - 89.07%)	80% (35.88% - 95.67%)
	SOMATOM DefinitionEdge	100% (15.81% - 98.74%)	N/A
	SOMATOM go. Top	N/A	33% (6.76% - 80.59%)
	Biograph Horizon	N/A	100% (15.81% - 98.74%)
Philips	Brilliance 16	100% (59.04% - 99.64%)	100% (29.24% - 99.16%)
	Incisive CT	100% (15.81% - 98.74%)	N/A
	SiemensHealthineers	SOMATOM go.UP	N/A
	SOMATOM go. Top	N/A	100% (29.24% - 99.16%)
	TOSHIBA	Aquilion	100% (29.24% - 99.16%)
	Aquilion ONE	100% (29.24% - 99.16%)	N/A
	Aquilion PRIME	100% (15.81% - 98.74%)	0% (1.26% - 84.19%)

{12}------------------------------------------------

Device Performance by ICH Volume
Minimal Volume Threshold(mL)	Sensitivity Above Threshold[95% CI]	Sensitivity Below/EqualThreshold [95% CI]
1	100% (91.96% - 99.94%)	92.86% (68.05% - 98.34%)
5	100% (87.23% - 99.91%)	96.77% (83.78% - 99.23%)
10	100% (83.89% - 99.88%)	97.30% (86.19% - 99.36%)

Device Performance by Scanner Reconstruction Method
ScannerManufacturer	ReconstructionMethod	Sensitivity [95% CI]	Specificity [95% CI]
GE Medical Systems	STANDARD	93.94% (80.32% - 98.14%)	95.60% (91.57% - 97.73%)
GE Medical Systems	SOFT	100% (39.76% - 99.37%)	N/A
Siemens	J37f, 3	100% (29.24% - 99.16%)	100% (73.54% - 99.79%)
Siemens	J30s, 2	84% (65.13% - 93.45%)	88.24% (65.29%- 96.42%)
	H31f	100% (88.06% - 99.91%)	66.67% (19.41%- 93.24%)
	J37f, 2	100% (76.84% - 99.82%)	50.00% (%18.41% -81.59%)
	Hr40f, 3	50% (9.43% - 90.57%)	80.00% (35.88%- 95.67%)
	Hf38s	100% (15.81% - 98.74%)	N/A
	H30s	100% (63.06% - 99.68%)	100% (63.06% - 99.68%)
	J37s, 2	100% (47.82% - 99.49%)	0% (0.84% - 70.76%)
	Hc40f, 2	100% (29.24% - 99.16%)	N/A
	J30s, 1	100% (29.24% - 99.16%)	N/A
	Hr38s	N/A	100% (15.81% - 98.74%)
	H30f	100% (15.81% - 98.74%)	N/A
Philips	U, B	100% (63.06% - 99.68%)	100% (29.24% - 99.16%)
SiemensHealthineers	Hr40f, 3	N/A	75% (28.36% - 94.73%)
TOSHIBA	FC68	100% (29.24% - 99.16%)	0% (1.26% - 84.19%)
	FC26	100% (29.24% - 99.16%)	N/A
	FC23	100% (15.81% - 98.74%)	N/A

{13}------------------------------------------------

As a secondary endpoint, the clinical benefit of using NeuroICH to prioritize these NCCT scans was quantified by comparing the time to open exam by a radiologist in the Standard of Care (TTO) referenced from Viz ICH versus the time that NeurolCH notification was received (TTN) for all the 128 eligible head CT scans included in the standalone performance study. The NeurolCH time-to-notification (TTN) includes the time to get the DICOM exam, analyze and send a notification to the mobile application component. The standard of care time-to-open-exam (TTO) consisted of the time from the initial scan of the patient to when the

{14}------------------------------------------------

radiologist first opened the exam for review. It was referenced from the literature and the predicate device Viz ICH.

The average time to alert a specialist by NeurolCH was 0.37 ± 0.20 minutes, which is lower than the average time to open an exam seen in the Standard of Care 18.3±14.2 minutes and comparable to the time reported by the predicate device Viz ICH 0.49±0.08 minutes. This result generally demonstrates that specialists have the opportunity to become involved in the clinical workflow early with notifications from the NeuroICH software.

Device Performance by Time
Parameter	NeuroICH Time withStandard Deviation	Viz ICH Time with StandardDeviation
Time to Notification (TTN)	$0.37 \pm 0.20$ minutes	$0.49\pm0.08$ minutes

Conclusion:

NeurolCH is as safe and effective as the legally marketed predicate device, Viz ICH (K210209). It shares the same indications for use and has similar technological characteristics and principles of operation as Viz ICH. The minor differences in technological characteristics do not raise any new safety concerns. Additionally, performance data demonstrate substantial equivalence to the predicate device and detects ICH in critical patients with comparable accuracy. Therefore, NeuroICH is claimed to be substantially equivalent to Viz ICH.

Regulation Number and Section

§ 892.2080 Radiological computer aided triage and notification software.

(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.