The NEUROShield™ medical image processing software is intended for automatic labelling, visualization, and volumetric quantification of the Hippocampus brain structure from a set of MR images.

Device Description

NEUROShield™ is a fully automated brain geometry-based quantifying analytics tool/cloud platform that uses Al/Deep Net to support physicians as a clinical decision support tool for neurologists and neuroradiologists. NEUROShield™ takes 3D MR images as input and calculates brain volumes that can assist physicians in devising optimal treatment plans. The Al tool branded as NEUROShield™ provides volumetric measurements of the Hippocampus brain structure. It replaces time-consuming manual processes with leading-edge automated technology that accelerates the analysis for clinical and research purposes. Brain Volume Quantification is a wellestablished methodology for differential and enhanced interpretation of medical images. We are using a locked algorithm, and any proposed modifications will be submitted to the FDA for review.

AI/ML Overview

Here's a summary of the acceptance criteria and the study that proves NEUROShield meets those criteria, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Measure	Threshold (Acceptance Criteria)	NEUROShield™ 95% Confidence Intervals (Reported Performance)	Criteria (Pass/Fail)
Dice Coefficient	0.75	(0.90, 0.92)	Pass
Hausdorff Distance (mm)	6.1	(3.57, 4.06)	Pass
Correlation (Volume)	0.82	(Not explicitly given as CI, but stated as "passed")	Pass
Relative Volume Difference	24.6%	(Not explicitly given as CI, but stated as "passed")	Pass
Mean Difference in BA plots (Total Hippocampus)	1010 mm³	(Not explicitly given as CI, but stated as "passed")	Pass

2. Sample Size for the Test Set and Data Provenance

Sample Size: 280 subjects
Data Provenance:
- Country of Origin: USA (collected from the publicly available ADNI - Alzheimer's Disease Neuroimaging Initiative - dataset, with approximately equal geographical distribution across East, Central, and West US regions).
- Retrospective or Prospective: Retrospective

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

Number of Experts: 3
Qualifications: US Board Certified Radiologists

4. Adjudication Method for the Test Set

Adjudication Method: The ground truth was established by combining the manual segmentations of the 3 radiologists into one tracing per case using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm. This STAPLE-derived ground truth was then compared with individual radiologist segmentations to ensure validity.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study that assesses the effect size of human readers improving with AI vs. without AI assistance was not reported in this summary. The study focuses on evaluating the standalone performance of the NEUROShield™ algorithm against an expert-derived ground truth.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone performance study was done. The NEUROShield™ algorithm's automated segmentations and volume calculations were directly compared against the ground truth established by expert radiologists.

7. The Type of Ground Truth Used

Ground Truth Type: Expert consensus, specifically "STAPLE-derived ground truth building on the three US Board Certified Radiologists' provided segmentations."

8. The Sample Size for the Training Set

Sample Size: 186 cases

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

The ground truth for the training set was established by manual segmentation of the Hippocampus structure by subject matter experts. This manually segmented data was then used as input to train the deep net Segmentation Model.

Summary

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In-Med Prognostics L3C Latha Poonamallee 4918 September Street San Diego, California 92110 September 14, 2023

Re: K220034

Trade/Device Name: NEUROShield Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: June 19, 2023 Received: June 20, 2023

Dear Latha Poonamallee:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE(@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely.

Robert Sauer Deputy Director 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

Indications for Use

510(k) Number (if known) K220034

Device Name

NEUROShield

Indications for Use (Describe)

The NEUROShield™ medical image processing software is intended for automatic labelling, visualization, and volumetric quantification of the Hippocampus brain structure from a set of MR images.

Type of Use (Select one or both, as applicable)	X Prescription Use (Part 21 CFR 801 Subpart D)	Over-The-Counter Use (21 CFR 801 Subpart C)
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Revision: 01

Date: 11 Sept 2023

510(k) Summary

I. Submitter

Name	In-Med Prognostics L3C
Address	4918 September Street, San Diego, CA 92110, USA
Contact Person	Dr. Latha Poonamallee
Telephone Number	+1 (906) 231-1135
Email	drlatha@inmed.ai

II. Device

Device Trade Name	NEUROShield™
Common Name	Medical Image Processing Software
Classification Name	Medical image management and processing system
Regulation Number	21 CFR 892.2050
Regulation Description	Picture archiving and communications system
Product Code	QIH
Classification Panel	Radiology

III. Predicate Device:

Device	NeuroQuant
510(k) Number	K170981
Manufacturer	CorTechs Labs, Inc
Product Code:	LLZ

IV. Device Description:

NEUROShield™ takes 3D MR images as input and calculates brain volumes that can assist physicians in devising optimal treatment plans. The Al tool branded as NEUROShield™ provides volumetric measurements of the Hippocampus brain structure. It replaces time-consuming manual processes

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with leading-edge automated technology that accelerates the analysis for clinical and research purposes. Brain Volume Quantification is a wellestablished methodology for differential and enhanced interpretation of medical images.

We are using a locked algorithm, and any proposed modifications will be submitted to the FDA for review.

V. Indications for Use:

The NEUROShield™ medical image processing software is intended for automatic labelling, visualization, and volumetric quantification of the Hippocampus brain structure from a set of MR images.

VI. Comparison of Technological Characteristics with the Predicate Device:

Feature	Subject Device	Predicate Device
Device Name	NEUROShield™	NeuroQuant
Organization	In Med Prognostics L3C	CorTechs Labs, Inc
Classification	Class II	Class II
Product Code	QIH	LLZ
Indications for Use	Automatic labelling,visualization, and volumetricquantification of theHippocampus brain structurefrom a set of MR images.	Automatic labelling, visualizationand volumetric quantification ofsegmentable brain structuresand lesions from a set of MRimages. Volumetric data may becompared to referencepercentile data.
Design andIncorporatedTechnology	• Software as a medicaldevice to be used in theprocess of Imaging andquantification of theHippocampus brainstructure from a set of MRimages.• Fully automated braingeometry-based quantifyinganalytics tool/cloud platformdeveloped using DeepNet /U-Net methodologies.	• Automated measurement ofbrain tissue volumes andstructures and lesions• Automatic segmentation andquantification of brain structuresusing a dynamic probabilisticneuroanatomical atlas, with ageand gender specificity, based onthe MR image intensity.
PhysicalCharacteristics	• Software package(accessible via web browser)• Operates on off-the-shelfhardware (multiple vendors)	• Software package• Operates on off-the-shelfhardware (multiple vendors)
Operating System	Supports Windows and MacOS latest (No older thanCatalina)	Supports Linux, Mac OS X andWindows.
ProcessingArchitecture	An automated internalpipeline that performs:- segmentation- volume calculation- report generation	Automated internal pipeline thatperforms:- artifact correction- segmentation- lesion quantification
- volume calculation- report generation
Feature	Subject Device	Predicate Device
Data Source •	• MRI scanner: 3D T1 MRIscans acquired with specifiedprotocols• NEUROShield requiresuncompressed DICOM filesas input.	• MRI scanner: 3D T1 MRIscans acquired with specifiedprotocols • NeuroQuantSupports DICOM format as input
Output	Provides volumetricmeasurements ofHippocampus brain structure.	Provides volumetricmeasurements of brainstructures and lesions.Includes segmented coloroverlays and morphometricreports• Automatically compares resultsto reference percentile data andto prior scans when available• Supports DICOM format asoutput of results that can bedisplayed on DICOMworkstations and Picture Archiveand Communications Systems
Safety	• Automated quality controlfunctions- Scan protocol verification• Results must be reviewedby a trained physician.	• Automated quality controlfunctions- Tissue contrast check- Scan protocol verification- Atlas alignment check• Results must be reviewed by atrained physician.

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VII. Performance Testing:

NEUROShield is a machine learning/deep learning algorithm-based device. This algorithm was developed by training the Deep Net Segmentation Models with the help of the training set.

Specifications of the Training dataset

Data was collected from multiple sites across India, between January 2020 to December 2021.

The dataset of 186 cases, which was used for training, was carefully gathered and brought together by studying several factors such as variance, Tesla-strength (1.5T, 3T), qualities of image and equipment manufacturers. The collected data was prepared as the ground truth by manual segmentation of the Hippocampus structure by subject matter experts, and this was used as an input to the deep net Segmentation Model.

The table below provides the categorization of subjects.

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Subgroups		Count
	Healthy controls	186
Magnetic fieldstrength	1.5T	109
	3 T	77
Slice thickness	1	81
	1.2	50
	2	7
	2.2	48
EquipmentManufacturers	GE	57
	Siemens	112
	Philips	17

Validation Study:

NEUROShield's performance was evaluated by a validation study summarized as follows:

A. Data Description:

The performance testing/ validation dataset was collected from the publicly available ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. This dataset is independent of the training data and was not used to develop the NEUROShield's algorithm.

Data Size: 280 subjects ●
Study Type: Analytical & Cross-Sectional ●
Data collection type: Retrospective ●
Data Sampling: Stratified Random Sampling .
Recruitment factors: ADNI 1 & ADNI 3 dataset (Alzheimer's Disease ● Neuroimaging Initiative)
MRI Equipment manufacturers: GE medical systems, Philips medical systems, ● Siemens Healthineers.
Magnetic Field Strength: 1.5 & 3 T ●
MRI Sequences/ protocol: 3D T1 MPRAGE ●
. Slice thickness: 1, 1.2
Approximately equal geographical distribution in USA: East coast, Central US ● regions, West coast.

The distribution for age bands is as follows:

Age group	Count
55-64	109
65-69	56
70-74	50
75-79	29
80-84	20
85-90	16

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The mean age of subjects was found to be 68 ± 8.3 years.

	Subgroups	Count	Mean (mL)	Standard deviation (mL)
Clinical Sub-groups	ADNI-control	140	6.8	1
	ADNI-MCI	70	6.6	1.1
	ADNI-AD	70	5.5	1.1
Gender	Male	140	6.6	1.1
	Female	140	6.2	1.3
Magnetic field strength	1.5T	139	6.2	1.2
	3 T	141	6.7	1.1
Slice thickness	1	118	6.7	1
	1.2	162	6.2	1.2
US Region	East	100	6.5	1.2
	West	87	6.3	1.3
	Central	93	6.5	1

The table below provides the categorization of subjects according to selection criteria:

B. Ground Truth:

1. 3 US Board Certified Radiologists performed manual segmentation of 280 subjects' MRI Brain scans using a widely accepted segmentation guideline method. This ground truth was combined into one tracing per case by the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm. The STAPLE-derived ground truth was then compared with segmentation provided by each radiologist and statistical tests were performed to ensure the validity of ground truth.
1. NEUROShield™ algorithm provided automated segmentation for the Hippocampus brain structure with markings and labelling on all subjects using algorithms and performed segmentation and computed volumes.

C. Statistical Analysis:

1. Validation of Ground Truth: Ground truth validation using 3 radiologist segmentations was performed by comparing the segmentations obtained by these radiologists with the STAPLE annotations, utilizing Dice coefficient, sensitivity, specificity and Hausdorff distance.
  Statistical analysis revealed no significant differences between the ground truth and radiologist segmentations (p > 0.05) for all comparisons, supporting the reliability of the ground truth annotations and the consistency of the radiologist segmentations.
1. Geometric comparison of NEUROShield™ with Ground Truth: Ground Truth was compared with the segmentation output generated by NEUROShield™ and validated using Dice score and Hausdorff distance. NEUROShield passed the criteria for both statistical methods.

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1. Quantitative Comparison of volumes of NEUROShield™ (NS) with Ground Truth: STAPLE-derived ground truth building on the three US Board Certified Radiologists' provided segmentations was used to calculate the volume of the Hippocampus brain structure for all the cases. ITK Snap was used for this purpose. These volumes were compared with the volumes computed by NEUROShield™ using correlation analysis, Bland-Altman plots, and relative volume difference. NEUROShield passed the criteria for all three of the statistical methods, showing correspondence between NEUROShield™ volume and STAPLE volume.
1. Pass/fail criteria: The pass/fail criteria were defined by selecting thresholds for each measure and performing hypothesis testing for the same. The thresholds for Dice score and Hausdorff distance are 75% and 6.1mm respectively, whereas for correlation and relative volume difference the passing criteria was defined as 0.82 and 24.6% respectively. For mean difference in BA plots, the threshold selected for total hippocampus was 1010 mm3
1. Subgroup Error Analysis: For all the different subgroups, like magnetic field strength, gender, slice thickness, clinical subgroups and US geographical regions, the dice score values were above 90% and Hausdorff distance was less than 4 mm which represents a high level of accuracy of NEUROShield™ Hippocampus segmentation. Furthermore, NEUROShield™ passes the criteria both for correlation and relative volume differences for different subgroups.

D. Results:

1. The average dice coefficient and Hausdorff distance was found to be 0.91 and 3.8 mm respectively. The following table shows the 95% confidence interval for both.

Measure	Threshold	NEUROShield™ 95 %confidence intervals	Criteria(Pass/Fail)
Dice	0.75	(0.90, 0.92)	Pass
Hausdorff distance	6.1	(3.57, 4.06)	Pass

1. The outcomes of subgroup error analysis are as follows:

	Dice score			Hausdorff distance (mm)
Clinical subgroups	Control	MCI	AD	Control	MCI	AD
Measured value	0.91	0.92	0.9	3.77	3.43	4.3
NEUROShieldTM 95 % confidence intervals	(0.9, 0.92)	(0.91, 0.93)	(0.88, 0.91)	(3.42, 4.13)	(3.03, 3.82)	(3.75, 4.85)
Criteria	Pass	Pass	Pass	Pass	Pass	Pass

a) Clinical subgroups:

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b) Gender:

	Dice score		Hausdorff distance (mm)
Gender	Female	Male	Female	Male
Measured value	0.91	0.91	3.92	3.71
NEUROShield™ 95 %confidence intervals	(0.90,0.92)	(0.90,0.92)	(3.5,4.3)	(3.4,4.02)
Criteria	Pass	Pass	Pass	Pass

c) Magnetic field strength:

	Dice score		Hausdorff distance (mm)
MRI strength	3T	1.5T	3T	1.5T
Measured value	0.92	0.9	3.66	3.9
NEUROShield™95 % confidenceintervals	(0.92,0.93)	(0.89, 0.91)	(3.36, 3.96)	(3.59, 4.37)
criteria	Pass	Pass	Pass	Pass

d) Slice thickness:

	Dice score		Hausdorff distance (mm)
slice thickness	1 mm	1.2mm	1 mm	1.2mm
Average value	0.92	0.9	3.5	4
NEUROShield™95 % confidenceintervals	(0.92,0.93)	(0.89,0.91)	(3.21, 3.8)	(3.68,4.41)
criteria	Pass	Pass	Pass	Pass

e) US geographical regions:

	Dice score			Hausdorff distance
Region	East US	West US	Central US	East US	West US	Central US
Averagevalue	0.91	0.9	0.92	3.78	3.94	3.73

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95 %confidenceintervals	(0.9, 0.92)	(0.88, 0.91)	(0.91, 0.93)	(3.37, 4.19)	(3.43, 4.45)	(3.38, 4.09)
Criteria	Pass	Pass	Pass	Pass	Pass	Pass

The analysis of Neuroshield hippocampus segmentation revealed that the overall dice score values were above 90% and Hausdorff distance consistently below 4mm, reflecting a remarkable level of accuracy. These findings affirm the precision in both correlation and relative volume differences for segmenting hippocampal structures.

VIII. Conclusions:

This comprehensive evaluation demonstrates the reliability and effectiveness of NeuroShield in hippocampal segmentation. The assessment concludes that Neuroshield is a highly accurate device/tool for its intended use, thereby promising significant contributions to neuroimaging research and clinical applications.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
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.(b)
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).