See-Mode Augmented Reporting Tool, Thyroid (SMART-T) is a stand-alone reporting software to assist trained medical professionals in analyzing thyroid ultrasound images of adult (>=22 years old) patients who have been referred for an ultrasound examination.

Output of the device includes regions of interest (ROIs) placed on the thyroid ultrasound images assisting healthcare professionals to localize nodules in thyroid studies. The device also outputs ultrasonographic lexicon-based descriptors based on ACR TI-RADS. The software generates a report based on the image analysis results to be reviewed and approved by a qualified clinician after performing quality control.

SMART-T may also be used as a structured reporting software for further ultrasound studies. The software includes tools for reading measurements and annotations from the images that can be used for generating a structured report.

Patient management decisions should not be made solely on the basis of analysis by See-Mode Augmented Reporting Tool, Thyroid.

Device Description

See-Mode Augmented Reporting Tool, Thyroid (SMART-T) is a stand-alone, web-based image processing and reporting software for localization, characterization and reporting of thyroid ultrasound images.

The software analyzes thyroid ultrasound images and uses machine learning algorithms to extract specific information. The algorithms can identify and localize suspicious soft tissue nodules and also generate lexicon-based descriptors, which are classified according to ACR TI-RADS (composition, echogenicity, shape, margin, and echogenic foci) with a calculated TI-RADS level according to the ACR TI-RADS chart.

The software then generates a report based on the image analysis results to be reviewed and approved by a qualified clinician after performing quality control. Any information within this report can be changed and modified by the clinician if needed during quality control and before finalizing the report.

The software runs on a standard "off-the-shelf" computer and can be accessed within the client web browser to perform the reporting of ultrasound images. Input data and images for the software are acquired through DICOM-compliant ultrasound imaging devices.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study details for the See-Mode Augmented Reporting Tool, Thyroid (SMART-T) device, based on the provided text:

Acceptance Criteria and Device Performance

Acceptance Criteria Category	Specific Metric	Acceptance Criteria (Explicitly Stated or Inferred)	Reported Device Performance (Aided)	Reported Device Performance (Unaided)	Standalone Performance (Algorithm Only)
Nodule Localization	AULROC (IOU > 0.5)	Improvement over unaided performance	0.758 (0.711, 0.803)	0.736 (0.693, 0.780)	0.703 (0.642, 0.762)
	AULROC (IOU > 0.6)	Improvement over unaided performance	0.734 (0.682, 0.781)	0.682 (0.632, 0.730)	N/A
	AULROC (IOU > 0.7)	Improvement over unaided performance	0.686 (0.629, 0.740)	0.548 (0.490, 0.610)	N/A
	AULROC (IOU > 0.8)	Improvement over unaided performance	0.593 (0.529, 0.658)	0.356 (0.293, 0.423)	N/A
	Localization Accuracy (Bounding box IOU > 0.5)	Superior to unaided performance	95.6% (94.1, 97.0)	93.6% (92.1, 95.0)	95.1%
TI-RADS Descriptors	Composition Accuracy	Significant improvement over unaided performance	84.9% (82.2, 87.5)	80.4% (77.3, 83.4)	86.7%
	Echogenicity Accuracy	Significant improvement over unaided performance	77.4% (74.4, 80.3)	70.0% (67.0, 72.8)	68.2%
	Shape Accuracy	Significant improvement over unaided performance	90.8% (88.2, 93.1)	86.4% (83.7, 88.8)	93.4%
	Margin Accuracy	Significant improvement over unaided performance	73.5% (70.2, 76.7)	57.3% (53.3, 61.2)	58.4%
	Echogenic Foci Accuracy	Significant improvement over unaided performance	75.2% (71.9, 78.5)	71.1% (67.1, 74.9)	70.3%
TI-RADS Level Agreement	Overall TI-RADS Level Agreement	Significant improvement over unaided performance	60.0% (56.8, 63.3)	51.1% (47.8, 54.5)	63.8% (60.0, 67.7)
	TI-RADS Level Agreement (TR-1)	Improvement over unaided performance	59.0% (42.3, 74.9)	52.9% (37.3, 68.3)	61.9% (40.0, 82.6)
	TI-RADS Level Agreement (TR-2)	Improvement over unaided performance	38.1% (31.1, 45.6)	31.2% (24.6, 38.1)	41.1% (31.7, 50.4)
	TI-RADS Level Agreement (TR-3)	Significant improvement over unaided performance	68.9% (62.6, 74.9)	58.8% (52.2, 65.4)	71.7% (64.9, 78.3)
	TI-RADS Level Agreement (TR-4)	Significant improvement over unaided performance	61.4% (56.5, 66.3)	52.1% (47.2, 57.0)	65.5% (59.1, 71.6)
	TI-RADS Level Agreement (TR-5)	Significant improvement over unaided performance	71.3% (61.8, 80.5)	62.0% (52.2, 71.5)	77.0% (66.1, 87.3)

Note: The acceptance criteria are largely inferred from the study's objective to demonstrate "superior performance," "significant improvement," and "consistent performance" compared to unaided reading, and "on-par" with aided use for standalone. Exact numerical thresholds for acceptance were not explicitly stated as distinct acceptance criteria.

Study Details

2. Sample size used for the test set and the data provenance:

Test Set Sample Size: 600 cases from 600 unique patients.
Data Provenance: Retrospective collection of thyroid ultrasound images. 74% of the data was acquired from the US. The cases in the MRMC study were sourced from institutions or sources not part of the model training or development datasets to ensure generalizability.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

Number of Experts: Two expert US-board certified radiologists and one adjudicator (also a US-board certified radiologist with the most years of experience).
Qualifications: US-board certified radiologists, with one having "the most years of experience" for adjudication.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

Adjudication Method: 2+1 (Two expert radiologists' consensus, with an additional expert radiologist adjudicating disagreements). Specifically, the text states "consensus labels of two expert US-board certified radiologists and an adjudicator (also US-board certified radiologist with the most years of experience)."

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, if so, what was the effect size of how much human readers improve with AI vs without AI assistance:

MRMC Study Done: Yes.
Effect Size of Improvement (Aided vs. Unaided):
- AULROC (IOU > 0.5): 0.022 (0.758 aided - 0.736 unaided)
- AULROC (IOU > 0.6): 0.052 (0.734 aided - 0.682 unaided)
- AULROC (IOU > 0.7): 0.138 (0.686 aided - 0.548 unaided)
- AULROC (IOU > 0.8): 0.237 (0.593 aided - 0.356 unaided)
- Localization Accuracy: 2.0% improvement (95.6% aided - 93.6% unaided)
- TI-RADS Descriptors Accuracy Improvements:
  - Composition: 4.5% (84.9% vs 80.4%)
  - Echogenicity: 7.4% (77.4% vs 70.0%)
  - Shape: 4.4% (90.8% vs 86.4%)
  - Margin: 16.2% (73.5% vs 57.3%)
  - Echogenic Foci: 4.1% (75.2% vs 71.1%)
- Overall TI-RADS Level Agreement: 8.9% (60.0% vs 51.1%)

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

Standalone Study Done: Yes. The text explicitly states: "To evaluate the standalone performance of our device, where the output of the models are directly compared against ground truth labels."

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

Nodule Benign/Malignant Status: Sourced from reference standard of Fine Needle Aspiration (FNA) or 2-year follow-up for benign cases (outcomes data/pathology).
Localization, ACR TI-RADS Lexicon Descriptors, and TI-RADS Level Agreement: Expert consensus based on the labels of two expert US-board certified radiologists and an adjudicator.

8. The sample size for the training set:

The document states that the cases in the MRMC study were sourced from institutions or sources not part of the model training or development datasets. However, the specific sample size for the training set is not provided in the given text.

9. How the ground truth for the training set was established:

The document implies that the training data was distinct from the test set, but it does not explicitly describe how the ground truth for the training set was established. It only details the ground truth establishment for the test set used in the standalone and MRMC studies.

Summary

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See-Mode Technologies Pte. Ltd. % Sadaf Monajemi Co-founder and Director 32 Carpenter Street. #03-01 Singapore, 059911 Singapore

September 9, 2024

Re: K240697

Trade/Device Name: See-Mode Augmented Reporting Tool, Thyroid (SMART-T) Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection And Diagnosis Software Regulatory Class: Class II Product Code: QDQ, QIH Dated: August 2, 2024 Received: August 2, 2024

Dear Sadaf Monajemi:

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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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 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 (QS) 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

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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,

Jessica Lamb

Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT 8B: Division of Radiological Imaging Devices and Electronic Products OHT 8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

Submission Number (if known)

K240697

Device Name

See-Mode Augmented Reporting Tool, Thyroid (SMART-T)

Indications for Use (Describe)

Patient management decisions should not be made solely on the basis of analysis by See-Mode Augmented Reporting Tool, Thyroid.

Type of Use (Select one or both, as applicable)

Prescription Use (Part 21 CFR 801 Subpart D)

Over-The-Counter Use (21 CFR 801 Subpart C)

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Image /page/4/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized, teal-colored graphic above the text "See-Mode". The graphic appears to be two overlapping wave-like shapes, possibly representing a signal or data visualization. The text "See-Mode" is in a simple, sans-serif font.

K240697

This "510(k) Summary" was prepared per section 807.92(c).

ADMINISTRATIVE INFORMATION 1.

Date of Preparation:	September 5, 2024
Prepared by:	Sadaf Monajemi, PhD. Co-founder and Director
Manufacturer:	See-Mode Technologies Pte. Ltd.32 Carpenter Street #03-01Singapore 059911SINGAPOREEmail: sadaf@see-mode.comTel: +61 415 952 782www.see-mode.com
Official Contact:	Dr. Sadaf Monajemi, PhD, Co-founder and DirectorSee-Mode Technologies32 Carpenter Street #03-01Singapore 059911SINGAPOREEmail: sadaf@see-mode.com www.see-mode.com

2. DEVICE NAME AND CLASSIFICATION

Trade/Proprietary Name: See-Mode Augmented Reporting Tool, Thyroid (SMART-T) Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological computer-assisted detection and diagnosis software Classification Name: System, Image Processing, Radiological Review Panel: Radiology Regulatory Class: Class II Product Code: QDQ/QIH

3. INTENDED USE

Localization and characterization of thyroid ultrasound images.

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Image /page/5/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized wave-like graphic above the text "See-Mode." Both the graphic and the text are in a teal color. The wave graphic appears to be two overlapping sine waves.

4. INDICATIONS FOR USE

Patient management decisions should not be made solely on the basis of analysis by See-Mode Augmented Reporting Tool, Thyroid.

DEVICE DESCRIPTION 5.

See-Mode Augmented Reporting Tool, Thyroid (SMART-T) is a stand-alone, web-based image processing and reporting software for localization, characterization and reporting of thyroid ultrasound images.

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Image /page/6/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized wave-like graphic above the text "See-Mode". The graphic is a teal color and appears to be two overlapping sine waves. The text "See-Mode" is also teal and in a sans-serif font.

The data produced by the software is intended to be used by trained medical professionals, including but not limited to physicians and medical technicians. The software is not intended to be used as an independent source of medical advice or to determine or recommend a course of action or treatment for patients.

6. SUBSTANTIAL EQUIVALENCE

6.1. Predicate Device

Manufacturer: TaiHao Medical Inc. Trade Name: BU-CAD 510(k) Identifier: K210670 Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection/Diagnosis Software For Lesions Suspicious For Cancer Classification Name: Radiological Computer Assisted Detection/Diagnosis Software For Lesions Suspicious For Cancer Classification Panel: Radiology Regulatory Class: Class II Product Code: QDQ, LLZ Date Cleared: December 21, 2021

6.2. Tabular Comparison of Features and Specifications of the Subject Device, Predicate Device, and Reference Device

	Subject DeviceSee-Mode AugmentedReporting Tool, Thyroid(SMART-T)(K240697)	Predicate DeviceBU-CAD (K210670)	Reference DeviceKoios DS (K212616)
	Administrative information
Regulation	21 CFR 892.2090	21 CFR 892.2090	21 CFR 892.2060
	Radiologicalcomputer-assisted detectionand diagnosis software forlesions suspicious for cancer	Radiologicalcomputer-assisted detectionand diagnosis software forlesions suspicious for cancer	Radiologicalcomputer-assisted diagnosticsoftware for lesionssuspicious of cancer
RegulatoryClass	Class II	Class II	Class II
Subject Device	Predicate Device	Reference Device
See-Mode AugmentedReporting Tool, Thyroid(SMART-T)(K240697)	BU-CAD (K210670)	Koios DS (K212616)
ProductCode	QDQ, QIH	QDQ, LLZ	POK, QIH
510(k)Number	K240697	K210670	K212616
Intended Use
Indicationsfor Use	See-Mode AugmentedReporting Tool, Thyroid(SMART-T) is a stand-alonereporting software to assisttrained medicalprofessionals in analyzingthyroid ultrasound imagesof adult (>=22 years old)patients who have beenreferred for an ultrasoundexamination.Output of the deviceincludes regions of interest(ROIs) placed on the thyroidultrasound images assistinghealthcare professionals tolocalize nodules in thyroidstudies. The device alsooutputs ultrasonographiclexicon-based descriptorsbased on ACR TI-RADS. Thesoftware generates a reportbased on the image analysisresults to be reviewed andapproved by a qualifiedclinician after performingquality control.SMART-T may also be usedas a structured reportingsoftware for furtherultrasound studies. The	BU-CAD is a softwareapplication indicated to assisttrained interpretingphysicians in analyzing thebreast ultrasound images ofpatients with soft tissuebreast lesions suspicious forbreast cancer who are beingreferred for further diagnosticultrasound examination.Output of the device includesregions of interest (ROIs) andlesion contours placed onbreast ultrasound imagesassisting physicians to identifysuspicious soft tissue lesionsfrom up to two orthogonalviews of a single lesion, andregion-based analysis oflesion malignancy upon thephysician's query. Theregion-based analysisindicates the score of lesioncharacteristics (SLC), andcorresponding BI-RADScategories in user-selectedROIs or ROIs automaticallyidentified by the software. Inaddition, BU-CAD alsoautomatically classifies lesionshape, orientation, margin,echo pattern, and posterior	Koios DS is an artificialintelligence (AI)/machinelearning (ML)-basedcomputer-aided diagnosis(CADx) software deviceintended for use as anadjunct to diagnosticultrasound examinations oflesions or nodulessuspicious for breast orthyroid cancer.Koios DS allows the user toselect or confirm regions ofinterest (ROIs) within animage representing a singlelesion or nodule to beanalyzed. The software thenautomatically characterizesthe selected image data togenerate an AI/ML-derivedcancer risk assessment andselects applicablelexicon-based descriptorsdesigned to improve overalldiagnostic accuracy as wellas reduce interpretingphysician variability.Koios DS may also be used asan image viewer ofmulti-modality digitalimages, including ultrasound
Subject DeviceSee-Mode AugmentedReporting Tool, Thyroid(SMART-T)(K240697)	Predicate DeviceBU-CAD (K210670)	Reference DeviceKoios DS (K212616)
annotations from the imagesthat can be used forgenerating a structuredreport.Patient managementdecisions should not bemade solely on the basis ofanalysis by See-ModeAugmented Reporting Tool,Thyroid.	BU-CAD may also be used asan image viewer ofmulti-modality digital images,including ultrasound andmammography. The softwareincludes tools that allow usersto adjust, measure anddocument images, and outputinto a structured report (SR).Patient management decisionsshould not be made solely onthe basis of analysis byBU-CAD.	allow users to adjust,measure and documentimages, and output into astructured report.Koios DS software isdesigned to assist trainedinterpreting physicians inanalyzing the breastultrasound images of adult(>= 22 years) femalepatients with soft tissuebreast lesions and/orthyroid ultrasounds of alladult (>= 22 years) patientswith thyroid nodulessuspicious for cancer. Whenutilized by an interpretingphysician who hascompleted the prescribedtraining, this device providesinformation that may beuseful in recommendingappropriate clinicalmanagement.
IntendedPopulation	Patients with thyroidnoduleswho are being referredfor ultrasound scan(Prescription only)	Patients with softtissue breast lesionswho are being referredfor ultrasoundinterpreting(Prescription only)	Patients with thyroid nodulessuspicious for cancer(Prescription only)
ImageSource	Ultrasound images	Ultrasound images	Ultrasound images
Rx only?	Yes	Yes	Yes
	Subject Device	Predicate Device	Reference Device
	See-Mode AugmentedReporting Tool, Thyroid(SMART-T)(K240697)	BU-CAD (K210670)	Koios DS (K212616)
ApplicationDescription	The subject device is astand-alone, web-basedimage processing andreporting software forlocalization, characterizationand reporting of thyroidultrasound images.The software analyzesthyroid ultrasound imagesand uses machine learningalgorithms to extract specificinformation. The algorithmscan identify and localizesuspicious soft tissuenodules and also generatelexicon-based descriptors,which are classifiedaccording to ACR TI-RADS(composition, echogenicity,shape, margin, and echogenicfoci) with a calculatedTI-RADS category accordingto the ACR TI-RADS chart.The software then generatesa report based on the imageanalysis results to bereviewed and approved by aqualified clinician afterperforming quality control.Any information within thisreport can be changed andmodified by the clinician ifneeded during qualitycontrol and before finalizingthe report.	BU-CAD is a software systemdesigned to assist users inanalyzing breast ultrasoundimages including identificationof regions suspicious for breastcancer and assessment of theirmalignancy. BU-CAD consistsof a viewer, a lesionidentification module, and alesion analysis module.The lesion identificationmodule identifies regions ofinterest (automated ROIs) of asingle suspicious soft tissuelesion in up to two orthogonalviews of breast ultrasoundimages for assisting users indetecting soft tissue lesions.Additionally, the lesionidentification modulegenerates an ROI and a lesioncontour on each breastultrasound image.The lesion analysis moduleanalyzes given ROIs of a breastlesion on ultrasound images,and generates a score of lesioncharacteristics (SLC) in termsof malignancy or benignity of alesion, BI-RADS category, andBI-RADS descriptors.	Koios DS is acomputer-aided diagnosis(CADx) software deviceintended for use as anadjunct to diagnosticultrasound examinations oflesions or nodulessuspicious for breast orthyroid cancer.Koios DS allows the user toselect or confirm regions ofinterest (ROIs) within animage representing a singlelesion or nodule to beanalyzed. Koios DS softwarecontains functionality forautomatically classifyingthyroid nodules suspiciousfor cancer.The system generates anoutput aligned to either theTI-RADS or ATA classificationguidelines. The systemautomatically generatesuser-modifiable thyroidnodule descriptors(Composition, Echogenicity,Shape, Margin, EchogenicFoci) and a direct,image-derived cancer riskassessment that is translatedinto an optionallexicon-specific (TI-RADS orATA) modifier.
AnatomicalLocation	Thyroid	Breast	Thyroid and Breast
Input	Medical imagesprovided in a DICOMformat	Medical imagesprovided in a DICOMformat	Medical imagesprovided in a DICOMformat
	Subject DeviceSee-Mode AugmentedReporting Tool, Thyroid(SMART-T)(K240697)	Predicate DeviceBU-CAD (K210670)	Reference DeviceKoios DS (K212616)
Output	ROIs placed on thyroidnodulesTI-RADS lexicon descriptorsTI-RADS category accordingto the ACR TI-RADS chart	ROIs and lesioncontours placed onsuspicious softtissue lesionBI-RADS lexicon descriptorsA region-based scoreof lesion malignancyBI-RADS category	ThyroidTI-RADS lexicon descriptorsbased on user-selected ROIsTI-RADS category accordingto the ACR TI-RADS chartA direct, deep-learningderived cancer riskassessment that is translatedinto an optionallexicon-specific modifier.The software's direct,non-descriptor-based cancerrisk assessment is presentedas the Koios "AI Adapter" thatcan be used in conjunctionwith the ACR TI-RADS or ATAguidelines for nodule riskstratificationBreastCategorical and continuousoutputs (confidence levelindicator) that align toBI-RADScategoriesAuto classification ofBI-RADS lexicon descriptors(shape and orientation)
OperatingPlatform	Client-server technology.	Client-server technology	Client-server technology
ImageFormat	DICOM	DICOM	DICOM
2D viewingcapabilities	Yes	Yes	Yes
Imagestorage andreportgeneration	Yes	Yes	Yes

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Image /page/7/Picture/1 description: The image contains the logo for See-Mode. The logo consists of a stylized, abstract symbol above the text "See-Mode". The symbol is composed of two overlapping, curved lines that resemble waves or a stylized letter 'M'. The color of both the symbol and the text is a light teal or green.

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Image /page/8/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized, green, wave-like symbol above the text "See-Mode", which is also in green. The wave symbol appears to be two overlapping curves, creating a visual representation of movement or flow.

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Image /page/9/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized, connected, wavy line in a light green color, resembling a sine wave or a stylized letter 'M'. Below the graphic is the text "See-Mode" in a sans-serif font, also in the same light green color.

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Image /page/10/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized wave-like graphic above the text "See-Mode". The graphic is a connected line that forms two peaks and valleys, resembling a simplified waveform. The text "See-Mode" is in a sans-serif font and is positioned directly below the graphic.

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Image /page/11/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized wave-like graphic above the text "See-Mode". The graphic is a teal color and appears to be two overlapping sine waves. The text "See-Mode" is also teal and in a sans-serif font.

6.3. Comparison with Predicate and Reference Devices

Similarities

· Intended Use: The intended use of SMART-T is the same as that of the legally marketed predicate device, BU-CAD. Both are intended to be used by clinicians interpreting radiological images to help them localize and characterize soft tissue lesions. SMART-T and the predicate device are both intended to be used concurrently with the reading of images and are not intended as a replacement for the review of a clinician or their clinical judgment.
Target Population: SMART-T, BU-CAD, and Koios DS share the same intended ● population. All devices are intended to be used for assisting trained interpreting clinicians in analyzing patients with soft tissue lesions or suspicious nodules that are being referred for diagnostic ultrasound examination.
Localization and Characterization: See-Mode Augmented Reporting Tool, Thyroid . (SMART-T) is similar to BU-CAD, the legally marketed predicate device, in its intent to localize soft tissue lesions in ultrasound images. While the reference Koios DS device analyzes thyroid lesions, it relies on users to manually identify the nodules, whereas the subject device automatically localizes the nodules.

Additionally, similar to the predicate and reference devices, the subject device characterizes nodules based on lexicon descriptors. The subject device (SMART-T), predicate device (BU-CAD), and reference device (Koios DS) all use established classification systems for their ultrasonographic lexicon descriptors. They all use the American College of Radiology Systems for describing soft tissue lesions or nodules. The ACR atlases provide standardized imaging terminology, report organization, assessment structure, and a classification system, which enables radiologists to communicate results clearly and consistently. The predicate BU-CAD device uses ACR BI-RADS for breast ultrasound images. Both SMART-T and the reference Koios DS device use ACR TI-RADS for analyzing and reporting thyroid ultrasound images.

Both BU-CAD and SMART-T automatically localize soft tissue findings, with the findings classified in line with lexicon descriptors. Similar to BU-CAD, our subject device provides automatic regions of interest (ROIs), rather than manually selected, to localize thyroid nodules. The ROI highlights the bounding box around the detected nodules for the user to easily review and make their own assessment.

Performance Testing: When comparing clinical validation between SMART-T, ● BU-CAD and Koios DS, the devices were evaluated using similar endpoints in their clinical studies. The Area Under the Curve (AUC) shift was used when comparing the performance of users with and without the aid of the device.

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Image /page/12/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized, wavy line in a teal color, resembling two interconnected 'M' shapes, positioned above the text "See-Mode". The text is also in the same teal color as the wavy line above it. The logo has a clean and modern design.

The number of cases evaluated in the MRMC reader study for BU-CAD was 628, the number of cases for Koios DS was 650 and the reader study for SMART-T included 600 cases. The number of readers included in the BU-CAD reader study was 16 (14 radiologists and 2 breast surgeons), the number of readers for Koios DS was 15 (14 radiologists and 1 endocrinologist) and the number of readers for SMART-T was 18 (all radiologists).

Following the primary endpoint, the AUC shift between predicate and subject device was similar. Similarly, the MRMC for BU-CAD showed an improvement of readers' determination of BI-RADS descriptors (Shape, Orientation, Margin, Echo Pattern, and Posterior Features) for at least one or more subcategories for each descriptor. The MRMC study for SMART-T, however, showed reader improvement in all TI-RADS descriptors (Composition, Echogenicity, Shape, Margin and Echogenic Foci).

The SMART-T MRMC reader study demonstrated substantially equivalent performance to BU-CAD and Koios DS by showing similar study design, success criteria and performance.

Differences

Anatomical Regions: BU-CAD is specifically focused on breast ultrasound images, whereas See-Mode Augmented Reporting Tool, Thyroid (SMART-T) analyzes thyroid ultrasound images. This is aligned with our reference device, Koios DS, which assesses and characterizes both breast lesions and thyroid nodules using ultrasound image data. SMART-T localizes thyroid nodules and characterizes the nodule with their lexicon descriptors (composition, echogenicity, shape, margin, echogenic foci) in line with ACR TI-RADS.
Modality: Contrary to BU-CAD and Koios DS, SMART-T only interprets ultrasound . images. SMART does not analyze mammography images or other multi-modality digital images.

The technological characteristics of the subject device and its predicate and reference device have been evaluated to determine equivalence. Upon reviewing and comparing intended use, design, materials, principle of operation, and overall technological characteristics, the subject device is determined to be substantially equivalent to predicate and reference devices.

Both devices (subject device and predicate) have the same intended use and are indicated for the same use. The subject device uses standard principles of operation, methods, and algorithms for processing, measurement, and quantification of the images and is intended to be used by trained professionals, which is similar to the predicate and reference devices.

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Image /page/13/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized, teal-colored symbol resembling two interconnected sine waves or mountain peaks. Below the symbol is the company name, "See-Mode," also in teal, with a hyphen connecting the two words.

The comparison of technological characteristics, non-clinical performance data, clinical data, and software validation data demonstrate that See-Mode Augmented Reporting Tool, Thyroid (SMART-T) is as safe and effective when compared to the predicate and reference devices that are currently marketed for similar intended use and indications for use. The table above provides a comparison between the proposed subject device, the predicate device, and the reference device.

7. PERFORMANCE DATA - CLINICAL AND BENCH TESTING

The performance of our device has been validated both in an MRMC reader study and standalone, as described below:

Standalone Study: To evaluate the standalone performance of our device, where the . output of the models are directly compared against ground truth labels.
Multi-reader Multi-Case (MRMC) Study: To compare the performance of expert readers (radiologists) with and without the aid of our device. The study evaluated the readers' localization and characterisation of thyroid nodules in scenarios both unaided and aided. In the MRMC study, we had 18 radiologists read 600 cases from 600 patients twice, once with the aid of the device and once without. There was a one-month washout period in between the two reads.

To ensure the generalizability and satisfactory performance of our models on new, unseen data, all cases in our MRMC study were sourced from institutions or sources not part of the model training or development datasets. The preceding results of this study, demonstrating the models' success with data from novel institutions, have been presented.

The performance of the device was evaluated across different sub-groups of patient sex, patient age, nodule size, ultrasound machine, data sources (US vs. Non-US), reader category (US vs. Non-US), and reader experience.

Data Selection

The dataset has been collected retrospectively from thyroid ultrasound images of patients who have been referred for an ultrasound examination. The study consisted of 600 cases from unique patients with 74% of the data acquired from the US. The age range is respective of the target population with an 81.5% female cohort. The ethnicity distribution in our dataset is representative of the broader US population.

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Image /page/14/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized wave-like graphic above the text "See-Mode". The graphic is a teal color and appears to be two sine waves connected together. The text "See-Mode" is also teal and is in a sans-serif font.

The cases in the dataset contained both negative and positive biopsy results with all TI-RADS levels represented. The data is sampled such that the distribution of the data accounts for patient sex, age, nodule size, malignancy, a priori clinical ACR TI-RADS level and a minimum sample is present for each subgroup. The data was curated from images sourced from accepted ultrasound systems, such as GE Healthcare, Philips Medical Systems and Siemens Medical Systems.

The patient distribution of the dataset:

Patient sex
- o Female: 489 cases
- o Male: 111 cases
Patient age
- o < 30 years: 29 cases
- 30-49 years: 131 cases o
- o 50-69 years: 320 cases
- 70+ years: 120 cases O

Reader Selection

The study consisted of 18 board-certified radiologists with experience ranging from 0 to 11 + years. All readers were trained and used the American College of Radiology guidelines for interpreting thyroid ultrasound studies. Each reader was asked to identify the nodule, select TI-RADS lexicon descriptors, select TI-RADS category, and a nodule suspicion score with and without the aid of the subject device.

Establishing ground truth

In both the standalone and the MRMC study we evaluated the performance of our software on localisation, TI-RADS descriptors, and FNA outcomes. The ground truthing approach for each is described below:

. Ground truth labels of benign or malignant status were assigned to the nodule for each case, sourced from the reference standard of FNA or 2 year follow-up for benign status.
The ground truth labels for localisation, ACR TI-RADS lexicon descriptors, and ● TI-RADS level agreement were based on the labels of two expert US-board certified radiologists and an adjudicator (also US-board certified radiologist with the most years of experience). This allowed for evaluating the accuracy of the readers in aided

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Image /page/15/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized, teal-colored graphic above the text "See-Mode". The graphic appears to be two connected sine waves, forming a shape reminiscent of the letter 'M' or a mountain range. The text "See-Mode" is in a bold, sans-serif font, also in teal, and is positioned directly below the graphic.

and unaided fashion, as well as evaluating the standalone performance of the device in localisation and classifying nodules against ground truth labels.

Performance improvement with LROC

Given that our device is intended for both localisation and characterisation, LROC analysis together with AULROC and LROC curves are essential for evaluating the performance of our device.

The results of the LROC analysis are shown in the table below. We have considered IOU > 0.5 as the criterion for successful location detection in the LROC analysis. In this analysis, the malignant cases where the detection IOU is below or equal to 0.5 are penalised as false negative.

IOU Criteria	Average Aided AUC(95% CI)	Average Unaided AUC(95% CI)	Standalone(95% CI)
IOU > 0.5	0.758 (0.711, 0.803)	0.736 (0.693, 0.780)	0.703 (0.642, 0.762)

To provide a comprehensive analysis and evaluate the performance of our device with various IOU thresholds, we have obtained AULROCs for different IOU thresholds ranging from 0.5 to 0.8 with 0.1 increments as shown in the table below. It was observed that the use of the device results in an improved AULROC performance for the readers across different IOU thresholds. Specifically, AULROC is improved significantly when applying more rigorous IOU criteria (>0.6, >0.7, and >0.8), showing the promise of the device to improve readers' performance.

	Average Reader AUC (95% CI)
Analysis	Aided	Unaided	Difference (Aided - Unaided)
AURLOC IOU > 0.5	0.758 (0.711, 0.803)	0.736 (0.693, 0.780)	0.022 (-0.012, 0.056)
IOU > 0.6	0.734 (0.682, 0.781)	0.682 (0.632, 0.730)	0.052 (0.008, 0.093)
IOU > 0.7	0.686 (0.629, 0.740)	0.548 (0.490, 0.610)	0.138 (0.082, 0.195)
IOU > 0.8	0.593 (0.529, 0.658)	0.356 (0.293, 0.423)	0.237 (0.168, 0.307)

Identification and localisation of nodules

To evaluate the localisation performance of the device, we calculated the average accuracy of the readers on localisation in aided and unaided scenarios, as well as the standalone performance. Localisation accuracy for each reader is calculated as the number of cases

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Image /page/16/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized, teal-colored graphic above the text "See-Mode". The graphic appears to be two connected sine waves, forming a shape reminiscent of the letter 'M' or a mountain range. The text "See-Mode" is in a bold, sans-serif font, also in teal, and is positioned directly below the graphic.

where the reader's bounding box has an overlap greater than 0.5 with the ground truth, divided by the total number of cases.

	Average Aided(95% CI)	Average Unaided(95% CI)	Standalone (95% CI)
LocalisationAccuracy	95.6% (94.1, 97.0)	93.6% (92.1, 95.0)	95.1%

As demonstrated in the table above, the aid of our device results in superior performance of the readers for localising thyroid nodules. It was also observed that the results of the algorithm are consistent across different sub-groups.

Thyroid lexicon descriptors

Aside from localisation, the other output of our device is characterisation of thyroid nodules according to ACR TI-RADS lexicon descriptors. In the table below, we have calculated the accuracy of the readers in determining each of the ACR TI-RADS descriptors (composition, echogenicity, shape, margin, and echogenic foci). We have also evaluated the standalone performance of the device on TI-RADS lexicon descriptors. As described above, the ground truth for the TI-RADS descriptors is the consensus labels of two expert US-board certified radiologists and an adjudicator (also US-board certified radiologist with the most years of experience).

TI-RAD Descriptor	Average Aided(95% CI)	Average Unaided(95% CI)	Standalone (95%CI)
Composition	84.9% (82.2, 87.5)	80.4% (77.3, 83.4)	86.7%
Echogenicity	77.4% (74.4, 80.3)	70.0% (67.0, 72.8)	68.2%
Shape	90.8% (88.2, 93.1)	86.4% (83.7, 88.8)	93.4%
Margin	73.5% (70.2, 76.7)	57.3% (53.3, 61.2)	58.4%
Echogenic Foci	75.2% (71.9, 78.5)	71.1% (67.1, 74.9)	70.3%

As can be seen from the table above, the device provides significant improvement and achieves superiority for characterizing all lexicon descriptors according to ACR TI-RADS. It

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Image /page/17/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized, abstract symbol above the text "See-Mode". The symbol is composed of two wavy lines that intersect in the middle, forming a shape reminiscent of mountains or waves. The color of both the symbol and the text is a muted green.

was also observed that the results of the algorithm are consistent across different sub-groups.

Attaining superiority across all TI-RADS descriptors is a strong proof point for the performance of our device, as it aligns with its primary function and intended use according to the ACR TI-RADS guideline.

TI-RADS level agreement

The accuracy of TI-RADS (TR) levels is critical as it directly informs clinical decisions regarding patient management according to the ACR TI-RADS guideline. To evaluate the impact of our device on TR level agreement, we have compared the overall TR level agreement percentage of each of the readers against the ground truth in both aided and unaided scenarios. As described above, the ground truth for the TI-RADS descriptors and, therefore the TR level, is based on the consensus labels of two expert US-board certified radiologists and an adjudicator (also US-board certified radiologist with the most years of experience).

To further evaluate the level of agreement, we also calculated the agreement for each TR level (TR-1 to TR-5). The agreement percentage for each TR level for each reader is calculated by dividing the number of cases where a reader's TR level matches the ground truth TR level by the total number of cases with that specific TR level. The average TR level agreement is then calculated as the average over all the readers.

TI-RADS	Average Aided(95% CI)	Average Unaided(95% CI)	Standalone (95%CI)
Overall	60.0% (56.8, 63.3)	51.1% (47.8, 54.5)	63.8 (60.0, 67.7)
TR-1	59.0% (42.3, 74.9)	52.9% (37.3, 68.3)	61.9 (40.0, 82.6)
TR-2	38.1% (31.1, 45.6)	31.2% (24.6, 38.1)	41.1 (31.7, 50.4)
TR-3	68.9% (62.6, 74.9)	58.8% (52.2, 65.4)	71.7 (64.9, 78.3)
TR-4	61.4% (56.5, 66.3)	52.1% (47.2, 57.0)	65.5 (59.1, 71.6)
TR-5	71.3% (61.8, 80.5)	62.0% (52.2, 71.5)	77.0 (66.1, 87.3)

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Image /page/18/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized wave-like symbol above the text "See-Mode". The wave symbol is made up of two connected curves, resembling an "M". The color of the logo is a light green.

The table above highlights a significant improvement in TR level agreement among readers with the use of the device. It is important to note that TR level agreement has been improved across all TR levels, from TR-1 to TR-5, with particularly significant gains at higher TR levels (TR-3, TR-4, and TR-5). Notably, these higher TR levels (TR-3) are the levels that are of high clinical importance with follow-up or FNA considerations. It was also observed that the results of the algorithm are consistent across different sub-groups.

Subgroup analysis

Subgroup analysis of patient sex (female, male), patient age (<30, 30-49, 50-69, 70+), nodule size (<10mm, 10-15mm, 15-20mm, 20-25mm, >=25mm), ultrasound machine (GE, Philips, Samsung, Siemens, Canon/Toshiba), reader category (US/Canada, Non-US/Canada), reader experience (0-3 years, 4-7 years, 8-10 years, >=11 years), and source of data (US, non-US) from the MRMC reader study were performed. The readers aided by SMART-T achieved consistent performance across all of the subgroups.

Conclusion

The results of our MRMC reader study shows improvement in reader performance with the aid of our device. Based on these results it was observed that the use of the device results in superior performance of the readers on nodule localisation, TI-RADS lexicon characterisation, and TR level agreement. The use of the device can also improve the performance of the readers with regards to malignancy and benignity status.

The standalone performance of the device is also on-par with the aided use of our device, indicating the validity of our algorithms. The outputs of our study are in line with the intended use of See-Mode Augmented Reporting Tool, Thyroid.

NON-CLINICAL DATA 8.

The design and development of SMART-T has been implemented according to recognised standards. See-Mode Technologies has performed software verification and validation testing for the subject device according to the FDA's guidance document "Content of Premarket Submissions for Device Software Functions," as well as "IEC 62304:2006/AC: 2015 - Medical Device Software - Software Lifecycle Processes". Special Controls were added according to 21 CFR 892.2090, "DEN180005 Evaluation of automatic class III designation for OsteoDetect – Decision summary with special controls".

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Image /page/19/Picture/1 description: The image shows the logo for See-Mode. The logo consists of a stylized, teal-colored graphic resembling two overlapping sine waves above the text "See-Mode" in a bold, sans-serif font, also in teal. The sine wave graphic is smooth and continuous, creating a visual representation of waves or signals.

The risk analysis was completed and the hazard risk analysis has been submitted as part of this application. It has been observed that all the risks identified with the subject device are acceptable and have been reduced as far as possible in accordance with ISO 14971:2019 Medical devices - Application of risk management to medical devices.

9. CONCLUSIONS

A detailed analysis has been conducted between See-Mode Augmented Reporting Tool, Thyroid (SMART-T) and its predicate and reference devices. It is noted that no new additional questions of safety and effectiveness are raised by these technologies. Through reviewing the similarities and differences of the intended use, technological characteristics and principles of operation, as well as assessing the clinical and non-clinical performance data, See-Mode Augmented Reporting Tool, Thyroid (SMART-T) is as safe, as effective and performs as well as the predicate and reference devices.

Regulation Number and Section

§ 892.2090 Radiological computer-assisted detection and diagnosis software.

(a)
Identification. A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.(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 image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output 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 improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(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 device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) 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 or for certain subpopulations), as applicable.(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.