Predicate Devices

Reference Devices

Not Found

AI/ML (Artificial Intelligence / Machine Learning)

Yes
The "Intended Use / Indications for Use" section explicitly states that the device is an "artificial intelligence (AI)/machine learning (ML)-based computer-aided diagnosis (CADx) software device".

Therapeutic

No
The device is a computer-aided diagnosis (CADx) software intended to assist physicians in analyzing medical images for cancer risk assessment, not for direct therapeutic intervention.

Diagnostic

Yes
The intended use explicitly states that Koios Decision Support (DS) is a "computer-aided diagnosis (CADx) software device" and its purpose is to generate an "AI/ML-derived cancer risk assessment and selects applicable lexicon-based descriptors designed to improve overall diagnostic accuracy."

SaMD (Software as a Medical Device)

Yes

The device description explicitly states that Koios Decision Support (DS) is a "software application" and a "web application". It is accessed by a user through a compatible client and the processing occurs on a server. There is no mention of any hardware components being part of the device itself.

IVD (In Vitro Diagnostic)

Based on the provided information, this device is not an In Vitro Diagnostic (IVD).

Here's why:

Intended Use: The intended use clearly states that Koios Decision Support (DS) is a software device intended for use as an adjunct to diagnostic ultrasound examinations. It analyzes image data and provides a cancer risk assessment and lexicon-based descriptors. This is focused on the interpretation of medical images, not on analyzing biological samples (like blood, urine, tissue, etc.) outside of the body.
Device Description: The device description confirms it's a software application that processes DICOM images from ultrasound. It doesn't mention any components or processes related to the analysis of biological specimens.
Lack of IVD Characteristics: IVDs are typically used to examine specimens derived from the human body to provide information for diagnosis, monitoring, or screening. This device operates on medical images, which are a representation of the body's internal structures, not a biological specimen itself.

While the device provides information that may be useful in recommending clinical management, this information is derived from image analysis, not from the in vitro examination of biological samples.

PCCP (Predetermined Change Control Plan) Authorized

No
The provided text does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.

Overview

Intended Use / Indications for Use

Koios Decision Support (DS) is an artificial intelligence (AI)/machine learning (ML)-based computer-aided diagnosis (CADx) software device intended for use as an adjunct to diagnostic ultrasound examinations of lesions or nodules suspicious for breast or thyroid cancer.

Koios DS allows the user to select or confirm regions of interest (ROIs) within an image representing a single lesion or nodule to be analyzed. The software then automatically characterizes the selected image data to generate an AI/ML-derived cancer risk assessment and selects applicable lexicon-based descriptors designed to improve overall diagnostic accuracy as well as reduce interpreting physician variability.

Koios DS software may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report.

Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. When utilized by an interpreting physician who has completed the prescribed training, this device provides information that may be useful in recommending appropriate clinical management.

Limitations:
• Patient management decisions should not be made solely on the results of the Koios DS analysis.

Koios DS software is not to be used for the evaluation of normal tissue, on sites of postsurgical excision, or images with doppler, elastography, or other overlays present in them.
Koios DS software is not intended for use on portable handheld devices (e.g. smartphones or tablets) or as a primary diagnostic viewer of mammography images.
• The software does not predict the presence of the thyroid nodule margin descriptor, extrathyroidal extension. In the event that this condition is present, the user may select this category manually from the margin descriptor list.

Product codes (comma separated list FDA assigned to the subject device)

POK, QIH

Device Description

Koios Decision Support (DS) is a software application designed to assist trained interpreting physicians in analyzing breast and thyroid ultrasound images. The software device is a web application that is deployed to a Microsoft IIS web server and accessed by a user through a compatible client. Once logged in and granted access to the Koios DS application, the user examines selected breast or thyroid ultrasound DICOM images. The user selects Regions of Interest (ROIs) of orthogonal views of a breast lesion or thyroid nodule for processing by Koios DS. The ROI(s) are transmitted electronically to the Koios DS server for image processing and the results are returned to the user for review.

Breast Functionality:

Koios DS software automatically classifies breast lesions suspicious for cancer based on image data into one of four ACR BI-RADS® Atlas2 or European U1-U5 Classification System-aligned categories (Benign, Probably Benign, Suspicious or Indeterminate, or Probably Malignant) and also displays a continuous graphical Confidence Level Indicator depicting where the lesion falls within its respective category and its relation to neighboring categories. The software automatically classifies the shape (Round, Oval, Irregular) and orientation (Parallel) of the selected lesion.

Thyroid Functionality:

Koios DS is a software medical device used to analyze ultrasound data to classify user-selected regions containing thyroid nodules suspicious for cancer. The software generates a set of usereditable sonographic nodule descriptor recommendations (Composition, Echogenicity, Shape, Margin, Echogenic Foci) along with an optional, deep-learning derived cancer risk assessment of the suspected nodule from two orthogonal views. Nodule descriptor recommendations are subsequently mapped to a categorical assessment and risk level rating via the ACR TI-RADS™ ATLAS or American Thyroid Association (ATA) risk stratification systems (RSSs) based on user preference. The software's direct, non-descriptor-based cancer risk assessment is presented as the Koios "AI Adapter" that, when used in conjunction with the ACR TI-RADS or ATA guidelines for nodule risk stratification, is shown to improve overall diagnostic performance of both systems. The Al Adapter operates as an optional lexicon-specific input used to modify the final categorization in the ACR TI-RADS and ATA RSSs. The AI adapter positively impacts performance through either a point-based modification (either positive or negative) or a risk-shift modification (either positive or negative) for ACR TI-RADS and the ATA systems, respectively.

This process creates an Al-augmented categorization that is meant to be used with no other modifications to the decision-making pathway of either RSS. A trained interpreting physician may choose to incorporate or exclude the Koios AI Adapter from the overall assessment when finalizing their diagnostic interpretation.

Koios DS enables the following functionality:

Breast and Thyroid Diagnostic Core AI Engines enabled by state-of-the-art computer . vision and machine learning techniques capable of reading, interpreting, analyzing, classifying and generating findings from ultrasound image data resulting in an automated risk assessment for breast lesions and thyroid nodules suspicious for cancer.
. Automatic classification of thyroid nodule TI-RADS and ATA Descriptors of: Composition, Echogenicity, Shape, Margin, and Echogenic Foci based on user-selected regions of interest (ROIs).
. Automatic classification of breast lesion BI-RADS and U1-U5 Descriptors Shape and Orientation based on user-selected or confirmed regions of interest (ROIs).
. Annotation and description of ultrasound images based on ACR BI-RADS Breast Imaging Atlas and U1-U5 for Koios DS Breast and ACR TI-RADS Atlas for thyroid lexicon classification forms and ATA classification guidelines for Koios DS Thyroid.
. Reporting forms for breast lesion or thyroid nodule identification and tracking in the Electronic Health Record.
. Smart Calipers - extraction of user-supplied ROI data (alternately referred to as Calipers) embedded in DICOM SR files from the ultrasound modality.
. Smart Click - for streamlining the manual ROI selection process. The Smart Click functionality enables the user to click on the center of a lesion in order to activate a system-generated region of interest surrounding the selected lesion for the user.
. Image Registration and Matching - allows users to select images and regions of interest through their own image viewers when interacting with Koios DS Breast and Koios DS Thyroid, and facilitates a flexible viewer agnostic workflow. When the Image Matching Engine is given a screenshot of a medical image with coordinates for a region of interest, it identifies the original full quality image and translates the coordinates to its frame of reference.
. Automatic Size and Position population using Optical Character Recognition (OCR) - the Koios DS Optical Character Recognition engine uses machine learning and rulebased methods to create a system which is capable of retrieving fast, accurate transcriptions of the text overlaid on ultrasound images. Given an ultrasound image that has been annotated by a radiologist or technician, the OCR function identifies all text in the image and extracts relevant information to the documentation of lesions or nodules.

This allows users to quickly interpret and transcribe the locations and measurements of ultrasound findings.

. Remote analysis interface to generate and view results within compatible software (e.g. ultrasound equipment or PACS workstation software).
Installer and Configuration Wizard.
Single Sign-on (SSO) Windows and LDAP Authentication. ●
Operating system and platform-agnostic usage.
. Zero-footprint web-based HTML5 DICOM image viewer with image manipulation and annotation tools.
Ability to save findings to PACS.
Ability to export findings to reporting software. ●

Mentions image processing

Yes, "The ROI(s) are transmitted electronically to the Koios DS server for image processing and the results are returned to the user for review." and "The Koios DS Client transmits the ROI screenshot to the Koios DS Server Image Matching Engine to generate the ROI on the underlying DICOM image and displays it to the user for review. The ROI and associated DICOM data are sent to the Koios DS Server for processing by the appropriate Diagnostic Engine."

Mentions AI, DNN, or ML

Yes, "Koios Decision Support (DS) is an artificial intelligence (AI)/machine learning (ML)-based computer-aided diagnosis (CADx) software device" and "Breast and Thyroid Diagnostic Core AI Engines enabled by state-of-the-art computer . vision and machine learning techniques"

Input Imaging Modality

Ultrasound and mammography (for image viewing only for mammography)

Anatomical Site

Breast, Thyroid

Indicated Patient Age Range

Adult (>= 22 years)

Intended User / Care Setting

Trained interpreting physicians, trained sonographers / healthcare setting (in a clinic or hospital)

Description of the training set, sample size, data source, and annotation protocol

Not Found

Description of the test set, sample size, data source, and annotation protocol

Breast Engine Standalone Testing:
- Sample Size: 900 lesions from 900 different patients (1800 images, two orthogonal images per lesion). An expanded validation set of 1014 cases (including the 900 and an additional 114) was used to test for dataset drift.
- Data Source: Images sourced from a wide variety of ultrasound hardware.
- Annotation Protocol: Ground truth for malignancy risk classification was determined by pathology or 1-year follow-up for cases that were not biopsied.
Thyroid Engine Standalone Testing:
- Sample Size: 500 lesions from 500 different patients (1000 images, two orthogonal images per lesion).
- Data Source: Images sourced from a wide variety of ultrasound hardware and from independent sites separate from the training data.
- Annotation Protocol: Ground truth for malignancy risk classification was determined by pathology results only.
Thyroid Smart Click Testing:
- Sample Size: 650 nodules.
- Annotation Protocol: Physician-drawn ROIs were used as reference ROIs. A user "click" was simulated by calculating the center of each nodule.
Image Registration and Matching Testing:
- Sample Size: 1,600 ultrasound studies (950 breast cases, 650 thyroid cases). These studies contained one or more ROIs.
- Annotation Protocol: The tests measured identification of image regions corresponding to query screenshots, and the quality of the resulting registration using the DICE coefficient for successful matches.
Auto-populate Size and Position using Optical Character Recognition (OCR) Testing:
- Sample Size: 1910 ultrasound B-Scans (mix of thyroid and breast images); a subset of 1226 images from supported machines was used for the test.
- Data Source: A variety of machines.
- Annotation Protocol: Images were manually annotated. Accuracy (percent correct) was measured for structured fields, with false positives, false negatives, and misread text fields counting against accuracy.

Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)

Breast Engine Standalone Testing:

Study Type: Bench testing (standalone performance)
Sample Size: 900 cases (900 different patients, 1800 images). Expanded validation set of 1014 cases for dataset drift.
Key Results:
- AUC: 0.945 [0.932, 0.959]
- Sensitivity: 0.976 [0.960, 0.992]
- Specificity: 0.632 [0.588, 0.676]
- Direct comparison with predicate Koios DS v3.0 breast engine: significant increase in AUC (1.6%), significant increase in sensitivity (0.6%), and significant increase (2.2%) in specificity.
- Bench testing demonstrated the system exceeds physician performance in AUC, sensitivity, and specificity.
- Performance requirements were met or exceeded.

Thyroid Engine Standalone Testing:

Study Type: Bench testing (standalone performance)
Sample Size: 500 lesions from 500 different patients (1000 images).
Key Results:
- When applied to ACR TI-RADS guidelines (with AI Adapter and descriptor predictors): AUC = 79.8% (significant increase over average physician AUC).
- For biopsy recommendation (TI-RADS): sensitivity = 0.644 [0.545, 0.744], specificity = 0.612 [0.566, 0.658].
- For follow-up recommendation (TI-RADS): sensitivity = 0.879 [0.812, 0.946], specificity = 0.495 [0.446, 0.544].
- For ATA classifications (with AI Adapter): physician AUC shows a significant increase of 9.135% [5.975, 12.294]. Sensitivity shows a non-significant increase of 0.511% [-5.182, 6.204], while specificity shows a significant increase of 18.741% [9.885, 27.596].
- Bench testing demonstrated that application of the Koios DS AI Adapter exceeds physician performance as measured by AUC, sensitivity, and specificity. Performance requirements were met or exceeded.

Thyroid Smart Click:

Study Type: Bench testing (non-inferiority testing, segmentation metrics)
Sample Size: 650 nodules.
Key Results:
- Non-inferiority demonstrated for sensitivity (-0.009 [-0.036, 0.018]), specificity (-0.018 [-0.041, 0.005]), and AUC (-0.012 [-0.029, 0.006]) when compared to physician-selected calipers.
- Dice Similarity Coefficient (DICE): 0.913 +/- 0.075, demonstrating precise approximation to physician-selected ROIs.
- Non-inferiority demonstrated for descriptor agreement (Composition, Echogenicity, Shape, Margin, Echogenic Foci) when using Smart Click ROIs compared to manual ROIs.

Image Registration and Matching:

Study Type: Bench testing
Sample Size: 1600 ultrasound studies (950 breast, 650 thyroid).
Key Results:
- No Match Rate = 0.32%.
- Average Match Time = 0.22 +/- 0.12 seconds (for image matching).
- Breast Image Matching Outcomes: Successful Match: 99.5%, No Match: 0.5%. Incorrect Match/Image: 0%.
- Breast Image Matching DICE Coefficient: 0.995 +/- 0.005.
- Thyroid Image Matching Outcomes: Successful Match: 100%. No Match/Incorrect Match/Image: 0%.
- Thyroid Image Matching DICE Coefficient: 0.996 +/- 0.004.

Auto-populate Size and Position using Optical Character Recognition (OCR):

Study Type: Bench testing
Sample Size: 1226 images.
Key Results:
- Breast Freetext Identification: accuracies ranged from 0.926 to 0.983.
- Thyroid Freetext Identification: accuracies ranged from 0.965 to 0.998.
- Measurement Text Identification: Measurement Description: 0.943, Measurement Value: 0.948, Unit of Measurement: 0.967.

Breast Clinical Study (Previously executed for K190442, provided for reference):

Study Type: Multi-Reader, Multi-Case (MRMC) retrospective clinical trial.
Sample Size: 900 patient cases (totaling 1800 cases analyzed per reader, for 15 readers).
MRMC: Yes.
Key Results:
- Change in average AUC with Koios DS (USE + DS) compared to USE Alone: +0.0370 (0.030, 0.044) at α = .05.
- Inter-operator variability (Kendall Tau-B): A significant increase in the metric from average USE Alone (.5404) to USE + DS (.6797) with 95% CI.
- Intra-operator variability: Statistically significant reduction in intra-reader variability when using USE + DS (class switching rate 10.8% vs. 13.6% for USE Alone, p = 0.042).

Thyroid Clinical Study (CRRS-3):

Study Type: Observational case-controlled, Multi-Reader, Multi-Case (MRMC) retrospective clinical trial.
Sample Size: 650 retrospectively collected cases (1300 total cases per reader, for 15 readers).
MRMC: Yes.
Key Results:
- Primary Endpoint 1 (Change in average AUC with Koios DS, all readers, all data): +0.083 [0.066, 0.099] (parametric), +0.079 [0.062, 0.096] (non-parametric).
- Primary Endpoint 2 (Change in average AUC with Koios DS, US readers, US data): +0.074 [0.051, 0.098] (parametric), +0.073 [0.049, 0.096] (non-parametric).
- Interpretation time: -23.6% (all readers, all data); -22.7% (US readers, US data); -32.4% (EU Readers, EU Data).
- Inter-Reader Variability: 40.7% (all readers, all data); 37.4% (US readers, US data); 49.7% (EU Readers, EU Data).
- Successfully met primary endpoints, demonstrating a statistically significant improvement of 0.074 [0.051, 0.098] in overall reader performance of US-based readers when utilizing Koios DS for thyroid ultrasound interpretations.

Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)

Breast Engine Standalone Testing:

Malignancy Risk Classifier AUC: 0.945 [0.932, 0.959]
Categorical Output:
- Sensitivity: 0.976 [0.960, 0.992]
- Specificity: 0.632 [0.588, 0.676]
Operating Point:
- PLR: 2.661 [2.338, 2.984]
- NLR: 0.039 [0.013, 0.064]
- PPV: 0.708 [0.672, 0.743]
- NPV: 0.966 [0.944, 0.988]

Thyroid Engine Standalone Testing (ACR TI-RADS guidelines, AI Adapter and descriptor predictors):

AUC: 79.8%
Biopsy Recommendation:
- Sensitivity: 0.644 [0.545, 0.744]
- Specificity: 0.612 [0.566, 0.658]
Follow-up Recommendation:
- Sensitivity: 0.879 [0.812, 0.946]
- Specificity: 0.495 [0.446, 0.544]

Thyroid Smart Click Engine Test (Non-inferiority):

Sensitivity: Difference = -0.009 [-0.036, 0.018]
Specificity: Difference = -0.018 [-0.041, 0.005]
AUC: Difference = -0.012 [-0.029, 0.006]
Detection DICE Coefficient: DICE= 0.913 +/- 0.075

Breast Clinical Study (K190442):

Change in average AUC (USE+DS vs USE Alone): 0.0370 (0.030, 0.044)

Thyroid Clinical Study (CRRS-3):

FNA with Koios DS (all readers, all data):
- Sensitivity: +0.084 [0.054, 0.113]
- Specificity: +0.140 [0.125, 0.155]
Follow-up with Koios DS (all readers, all data):
- Sensitivity: +0.060 [0.040, 0.080]
- Specificity: +0.206 [0.192, 0.219]

Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.

K212616

Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.

Not Found

Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).

Not Found

Regulation Number and Section

§ 892.2060 Radiological computer-assisted diagnostic software for lesions suspicious of cancer.

(a)
Identification. A radiological computer-assisted diagnostic software for lesions suspicious of cancer is an image processing prescription device intended to aid in the characterization of lesions as suspicious for cancer identified on acquired medical images such as magnetic resonance, mammography, radiography, or computed tomography. The device characterizes lesions based on features or information extracted from the images and provides information about the lesion(s) to the user. Diagnostic and patient management decisions are made by the clinical user.(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 algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended.
(iii) Results from performance testing protocols that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, 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) Standalone 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; and description of verification and validation activities including system level test protocol, pass/fail criteria, results, and cybersecurity).(2) Labeling must include:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the intended reading protocol.
(iii) A detailed description of the intended user and recommended user training.
(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, including 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) Detailed instructions for use.
(viii) 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 (
e.g., lesion and organ characteristics, disease stages, and imaging equipment).

Summary

0

November 15, 2024

Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo features the letters 'FDA' in a blue square, followed by the words 'U.S. FOOD & DRUG ADMINISTRATION' in blue text.

Koios Medical, Inc. % Michael Bocchinfuso Director of Regulatory Compliance and Quality 242 West 38th Street 14th Floor New York, NY 10018

Re: K242130

Trade/Device Name: Koios DS Regulation Number: 21 CFR 892.2060 Regulation Name: Radiological computer-assisted diagnostic software for lesions suspicious of cancer Regulatory Class: Class II Product Code: POK, QIH Dated: August 13, 2024 Received: August 19, 2024

Dear Michael Bocchinfuso:

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.

1

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 OS 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 Rue"). 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.

2

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.

YANNA S. KANG -S

Yanna Kang, Ph.D. Assistant Director Mammography and Ultrasound Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

3

Indications for Use

510(k) Number (if known) K242130

Device Name Koios DS

Indications for Use (Describe)

Koios Decision Support (DS) is an artificial intelligence (AI)/machine learning (ML)-based computer-aided diagnosis (CADx) software device intended for use as an adjunct to diagnostic ultrasound examinations of lesions or nodules suspicious for breast or thyroid cancer.

Koios DS allows the user to select or confirm regions of interest (ROIs) within an image representing a single lesion or nodule to be analyzed. The software then automatically characterizes the selected image data to generate an AI/MLderived cancer risk assessment and selects applicable lexicon-based to improve overall diagnostic accuracy as well as reduce interpreting physician variability.

Koios DS software may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and out unto a structured report.

Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. When utilized by an interpreting physician who has completed the prescribed training, this device provides information that may be useful in recommending appropriate clinical management.

Limitations:

· Patient management decisions should not be made solely on the results of the Koios DS analysis.

· Koios DS software is not to be used for the evaluation of normal tissue, on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them.

· Koios DS software is not intended for use on portable handheld devices (e.g. smartphones or tablets) or as a primary diagnostic viewer of mammography images.

· The software does not predict the presence of the thyroid nodule margin descriptor, extra-thyroidal extension. In the event that this condition is present, the user may select this category manually from the margin descriptor list.

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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Image /page/4/Picture/1 description: The image shows the logo for Koios. The logo consists of a stylized owl head on the left and the word "koios" in lowercase letters on the right. The owl head is dark gray with light blue eyes, and the word "koios" is light blue.

510(k) Summary of Safety and Effectiveness

This 510(k) summary of safety and effectiveness information is submitted as part of the Premarket Notification in accordance with the requirements of 21 CFR Part 807, Subpart E and Section 807.92.

1. Identification of Submitter:

Submitter:	Koios Medical Inc.
Address:	242 West 38th Street, 14th Floor
New York, NY 10018
Phone:	732-529-5755
Fax:	732-529-5757
Contact:	Michael Bocchinfuso
Title:	Director of Regulatory Compliance and Quality
Phone:	732-529-5755
Fax:	732-529-5757
Summary Date:	October 18, 2024

2. Identification of Product:

| Device Name: | Koios DS
Version 3.6 |
|-----------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------|
| Device Common Name:
Device Classification: | Radiological Computer-Assisted Diagnostic Software
21 CFR 892.2060, Class II, POK (primary)
21 CFR 892.2050, Class II, QIH (secondary) |
| Classification Name: | Radiological Computer-Assisted Diagnostic Software (CADx) for
Lesions Suspicious for Cancer |
| Manufacturer: | Koios Medical, Inc. |

3. Marketed Devices

In terms of safety and performance, this software medical device is substantially equivalent to the devices listed below:

Predicate device:	Koios DS
Manufacturer:	Koios Medical, Inc.

510(k) Summary

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510(k) Number: K212616

4. Device Description

Koios Decision Support (DS) is a software application designed to assist trained interpreting physicians in analyzing breast and thyroid ultrasound images. The software device is a web application that is deployed to a Microsoft IIS web server and accessed by a user through a compatible client. Once logged in and granted access to the Koios DS application, the user examines selected breast or thyroid ultrasound DICOM images. The user selects Regions of Interest (ROIs) of orthogonal views of a breast lesion or thyroid nodule for processing by Koios DS. The ROI(s) are transmitted electronically to the Koios DS server for image processing and the results are returned to the user for review.

Breast Functionality:

Koios DS software automatically classifies breast lesions suspicious for cancer based on image data into one of four ACR BI-RADS® Atlas2 or European U1-U5 Classification System-aligned categories (Benign, Probably Benign, Suspicious or Indeterminate, or Probably Malignant) and also displays a continuous graphical Confidence Level Indicator depicting where the lesion falls within its respective category and its relation to neighboring categories. The software automatically classifies the shape (Round, Oval, Irregular) and orientation (Parallel) of the selected lesion.

Thyroid Functionality:

Koios DS is a software medical device used to analyze ultrasound data to classify user-selected regions containing thyroid nodules suspicious for cancer. The software generates a set of usereditable sonographic nodule descriptor recommendations (Composition, Echogenicity, Shape, Margin, Echogenic Foci) along with an optional, deep-learning derived cancer risk assessment of the suspected nodule from two orthogonal views. Nodule descriptor recommendations are subsequently mapped to a categorical assessment and risk level rating via the ACR TI-RADS™ ATLAS or American Thyroid Association (ATA) risk stratification systems (RSSs) based on user preference. The software's direct, non-descriptor-based cancer risk assessment is presented as the Koios "AI Adapter" that, when used in conjunction with the ACR TI-RADS or ATA guidelines for nodule risk stratification, is shown to improve overall diagnostic performance of both systems. The Al Adapter operates as an optional lexicon-specific input used to modify the final categorization in the ACR TI-RADS and ATA RSSs. The AI adapter positively impacts performance through either a point-based modification (either positive or negative) or a risk-shift modification (either positive or negative) for ACR TI-RADS and the ATA systems, respectively.

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This process creates an Al-augmented categorization that is meant to be used with no other modifications to the decision-making pathway of either RSS. A trained interpreting physician may choose to incorporate or exclude the Koios AI Adapter from the overall assessment when finalizing their diagnostic interpretation.

Koios DS enables the following functionality:

Breast and Thyroid Diagnostic Core AI Engines enabled by state-of-the-art computer . vision and machine learning techniques capable of reading, interpreting, analyzing, classifying and generating findings from ultrasound image data resulting in an automated risk assessment for breast lesions and thyroid nodules suspicious for cancer.
. Automatic classification of thyroid nodule TI-RADS and ATA Descriptors of: Composition, Echogenicity, Shape, Margin, and Echogenic Foci based on user-selected regions of interest (ROIs).
. Automatic classification of breast lesion BI-RADS and U1-U5 Descriptors Shape and Orientation based on user-selected or confirmed regions of interest (ROIs).
. Annotation and description of ultrasound images based on ACR BI-RADS Breast Imaging Atlas and U1-U5 for Koios DS Breast and ACR TI-RADS Atlas for thyroid lexicon classification forms and ATA classification guidelines for Koios DS Thyroid.
. Reporting forms for breast lesion or thyroid nodule identification and tracking in the Electronic Health Record.
. Smart Calipers - extraction of user-supplied ROI data (alternately referred to as Calipers) embedded in DICOM SR files from the ultrasound modality.
. Smart Click - for streamlining the manual ROI selection process. The Smart Click functionality enables the user to click on the center of a lesion in order to activate a system-generated region of interest surrounding the selected lesion for the user.
. Image Registration and Matching - allows users to select images and regions of interest through their own image viewers when interacting with Koios DS Breast and Koios DS Thyroid, and facilitates a flexible viewer agnostic workflow. When the Image Matching Engine is given a screenshot of a medical image with coordinates for a region of interest, it identifies the original full quality image and translates the coordinates to its frame of reference.
. Automatic Size and Position population using Optical Character Recognition (OCR) - the Koios DS Optical Character Recognition engine uses machine learning and rulebased methods to create a system which is capable of retrieving fast, accurate transcriptions of the text overlaid on ultrasound images. Given an ultrasound image that has been annotated by a radiologist or technician, the OCR function identifies all text in the image and extracts relevant information to the documentation of lesions or nodules.

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This allows users to quickly interpret and transcribe the locations and measurements of ultrasound findings.

. Remote analysis interface to generate and view results within compatible software (e.g. ultrasound equipment or PACS workstation software).
Installer and Configuration Wizard.
Single Sign-on (SSO) Windows and LDAP Authentication. ●
Operating system and platform-agnostic usage.
. Zero-footprint web-based HTML5 DICOM image viewer with image manipulation and annotation tools.
Ability to save findings to PACS.
Ability to export findings to reporting software. ●

User Profile:

Koios DS is for use by trained professionals only. Koios DS is not for use by patients. Users must have appropriate medical professional competence, such as trained sonographers and interpreting physicians.

Use Environment:

Koios DS is a software application for use within the healthcare setting (in a clinic or hospital) for the examination and assessment of breast lesions or thyroid nodules using ultrasound. It is a platform-agnostic web application that queries and accepts DICOM compliant digital medical files from any compliant device subject to the specified DICOM Conformance Statement for Koios DS. Processing of the image(s) occurs in conjunction with a trained interpreting physician's typical diagnostic case read. The output of the system is a digital display to be used as a concurrent read and report input that may be added as an addendum to the DICOM series selected for processing or exported directly into a patient's draft report.

Operating Principle:

Koios DS is an ASP.NET web application deployed to a web server inside a Windows operating system environment accessed by a user through a compatible client. The application provides image-derived data via web triggering and remote analysis.

Once logged in and granted access to the Koios DS application, the user examines selected breast and thyroid ultrasound DICOM images. For breast functionality, the user selects or confirms up to two ROIs, from up to two orthogonal views that represent a single breast lesion for processing by the system. For thyroid functionality, two ROIs are required for processing by the system. The first ROI must be drawn on the transverse view, with the second on the

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longitudinal view of the nodule. For breast functionality, bench testing has verified a single ROI does not significantly decrease system AUC performance. The ROI(s) are transmitted electronically to the Koios DS server by the Koios DS Breast or Thyroid software for image processing and the results are returned to the user for review in the respective interface. Images and data can be stored, communicated, processed, and displayed within the system and/or across computer networks at distributed locations.

The Koios DS Client is an optional workflow enhancement tool installed as a desktop application on the user workstation that enables a user to draw ROIs natively within their image viewing software. The Koios DS Client captures a screenshot of the ROI selected by the user instead of being directly drawn on and captured with DICOM data. The ROI screenshot is transmitted electronically to the Image Matching Engine within the Koios DS Server. The Image Matching Engine processes the ROI screenshot and data, identifying and matching the correct DICOM image, and overlaying the ROI on that image. Once matched, the ROIs are returned to the user for review in the Koios DS Breast or Thyroid interface.

The software does not require any specialized hardware to return a diagnostic output, but the time to process ROIs will vary depending on the hardware specifications.

Koios DS contains two distinct Al/ML engines to characterize breast lesions and thyroid nodules. Based on the structured data that exists within the DICOM header for a patient study, the Koios DS system calls the corresponding engine for analysis of the identified lesion or nodule. Each system uses computer vision and machine learning techniques embedded within an engine capable of reading, interpreting, and generating findings from ultrasound data. The underlying Breast and Thyroid engines draw upon knowledge learned from a large database of known cases, tying image features to their eventual diagnosis, to form a predictive model.

Koios DS results can be saved or transferred in three separate ways: in-transmission, saving to Picture Archiving and Communication System (PACS), and exporting results to thirdparty reporting software. In-transit transmission may be utilized when users wish to share analyses across viewing workstations. Results can be stored in in-transit memory for a preset period of time defined by a system administrator. After that preset period of time, all results are wiped from the local memory. Another method of saving is storing a report in the patient study on the PACS. After single or multiple lesion or nodule analyses have been performed and ultimately accepted by a trained interpreting physician, Koios DS can export a summary report to PACS as an addendum to the DICOM study that was selected for processing. This report serves as future reference and aid in the comparison of cases requiring follow up. This functionality is strictly reserved for approved users and must be configured by a site administrator.

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Koios DS also supports exporting results to third-party reporting software to facilitate the reporting process. Saving or exporting preferences can be configured by the system administrator and user.

5. Indications for Use

Koios Decision Support (DS) is an artificial intelligence (AI)/machine learning (ML)-based computer-aided diagnosis (CADx) software device intended for use as an adjunct to diagnostic ultrasound examinations of lesions or nodules suspicious for breast or thyroid cancer.

Koios DS allows the user to select or confirm regions of interest (ROIs) within an image representing a single lesion or nodule to be analyzed. The software then automatically characterizes the selected image data to generate an Al/ML-derived cancer risk assessment and selects applicable lexicon-based descriptors designed to improve overall diagnostic accuracy as well as reduce interpreting physician variability.

Koios DS software may also be used as an image viewer of multi-modality digital images, including ultrasound and mammography. The software includes tools that allow users to adjust, measure and document images, and output into a structured report.

Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. When utilized by an interpreting physician who has completed the prescribed training, this device provides information that may be useful in recommending appropriate clinical management.

Limitations:

• Patient management decisions should not be made solely on the results of the Koios DS analysis.

Koios DS software is not to be used for the evaluation of normal tissue, on sites of postsurgical excision, or images with doppler, elastography, or other overlays present in them.
Koios DS software is not intended for use on portable handheld devices (e.g. smartphones or tablets) or as a primary diagnostic viewer of mammography images.

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• The software does not predict the presence of the thyroid nodule margin descriptor, extrathyroidal extension. In the event that this condition is present, the user may select this category manually from the margin descriptor list.

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6. Substantial Equivalence Chart

| Product | Koios DS 3.0
(K212616) | Koios DS 3.6
(subject device) |
|------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Physical
Characteristics | Software Package
Operates on off-the-shelf hardware | Software Package
Operates on off-the-shelf hardware |
| Storage | Storage not supported | Storage not supported |
| Image Input | DICOM | DICOM |
| Characteristics | Decision support device used to assist
in the assessment and
characterization of breast lesions and
thyroid nodules using US image data. | Decision support device used to assist in
the assessment and characterization of
breast lesions and thyroid nodules using US
image data. |
| Intended
Use/Indications
for Use | Koios Decision Support (DS) is an
artificial intelligence (AI)/machine
learning (ML)-based computer-aided
diagnosis (CADx) software device
intended for use as an adjunct to
diagnostic ultrasound examinations of
lesions or nodules suspicious for
breast or thyroid cancer. | Koios Decision Support (DS) is an artificial
intelligence (AI)/machine learning (ML)- based computer-aided diagnosis (CADx)
software device intended for use as an
adjunct to diagnostic ultrasound
examinations of lesions or nodules
suspicious for breast or thyroid cancer.

Koios DS allows the user to select or
confirm regions of interest (ROIs) within an
image representing a single lesion or
nodule to be analyzed. The software then
automatically characterizes the selected
image data to generate an AI/ML-derived
cancer risk assessment and selects
applicable lexicon-based descriptors
designed to improve overall diagnostic
accuracy as well as reduce interpreting
physician variability.

Koios DS software may also be used as an
image viewer of multi-modality digital
images, including ultrasound and
mammography. The software includes
tools that allow users to adjust, measure |
| | | |
| | includes tools that allow users to adjust, measure and document images, and output into a structured report. | structured report.
Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. When utilized by an interpreting physician who has completed the prescribed training, this device provides information that may be useful in recommending appropriate clinical management. |
| Target Population
(subset of above
for comparison
purposes) | Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. | Koios DS software is designed to assist trained interpreting physicians in analyzing the breast ultrasound images of adult (>= 22 years) female patients with soft tissue breast lesions and/or thyroid ultrasounds of all adult (>= 22 years) patients with thyroid nodules suspicious for cancer. |
| Limitations for Use
(subset of above
for comparison
purposes) | Limitations:
• Patient management decisions should not be made solely on the results of the Koios DS analysis.
• Koios DS software is not to be used for the evaluation of normal tissue, on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them. | Limitations:
• Patient management decisions should not be made solely on the results of the Koios DS analysis.
• Koios DS software is not to be used for the evaluation of normal tissue, on sites of post-surgical excision, or images with doppler, elastography, or other overlays present in them. |
| | | |
| | for use on portable handheld devices | on portable handheld devices (e.g. |
| | (e.g. smartphones or tablets) or as a | smartphones or tablets) or as a primary |
| | primary diagnostic viewer of | diagnostic viewer of mammography |
| | mammography images. | images. |
| | • The software does not predict the | • The software does not predict the |
| | presence of the thyroid nodule | presence of the thyroid nodule margin |
| | margin descriptor, extra-thyroidal | descriptor, extra-thyroidal extension. In |
| | extension. In the event that this | the event that this condition is present, the |
| | condition is present, the user may | user may select this category manually |
| | select this category manually from the | from the margin descriptor list. |
| | margin descriptor list. | |
| Modality Used for | Breast Ultrasound Data | Breast Ultrasound Data |
| Analysis | Thyroid Ultrasound Data | Thyroid Ultrasound Data |
| Input | Medical images provided in a DICOM | Medical images provided in a DICOM |
| | format | format |
| ROI | Breast | Breast |
| Requirements | The software requires a user to select | The software requires a user to select up to |
| | up to two ROIs, from up to two | two ROIs, from up to two orthogonal |
| | orthogonal views, that represent a | views, that represent a single lesion to be |
| | single lesion to be selected and | selected and processed. |
| | processed. | |
| | | Thyroid |
| | Thyroid | Two ROIs that represent a single lesion to |
| | Two ROIs that represent a single | be selected and processed are required for |
| | lesion to be selected and processed | analysis. |
| | are required for analysis. | |
| | | The first ROI is drawn on the transverse |
| | The first ROI is drawn on the | view of the nodule. The second is drawn on |
| | transverse view of the nodule. The | the longitudinal view. |
| | second is drawn on the longitudinal | |
| | view. | |
| Output (Breast) | Koios defined categorical and | Koios defined categorical and continuous |
| | continuous outputs (confidence level | outputs (confidence level indicator) that |
| | indicator) that align to BI-RADS, U1- | align to BI-RADS, U1-U5, and auto- |
| | U5, and auto-classified shape and | classified shape and orientation. |
| | orientation. | |
| Output (Thyroid) | Koios DS software automatically | Koios DS software automatically classifies |
| | classifies thyroid nodules suspicious | thyroid nodules suspicious for cancer |
| | | |
| | for cancer based on image data
generating an output aligned to either
the TI-RADS or ATA classification
guidelines. The system automatically
generates user-modifiable nodule
descriptors (Composition,
Echogenicity, Shape, Margin,
Echogenic Foci) and a direct, image-
derived cancer risk assessment that is
translated into an optional lexicon-
specific modifier. | based on image data generating an output
aligned to either the TI-RADS or ATA
classification guidelines. The system
automatically generates user-modifiable
nodule descriptors (Composition,
Echogenicity, Shape, Margin, Echogenic
Foci) and a direct, image-derived cancer
risk assessment that is translated into an
optional lexicon-specific modifier. |
| Comparative
Clinical
Performance
Testing (Breast) | Metric: AUC
Cases: 900
Readers: 15 | Metric: AUC
Cases: 900
Readers: 15 |
| Comparative
Clinical
Performance
Testing (Thyroid) | Metric: AUC
Cases: 650
Readers: 15 | Metric: AUC
Cases: 650
Readers: 15 |

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7. Description of Similarities and/or Differences

Intended Use/Indications for Use (IFU)

The IFU of the subject and predicate devices are the same.

Target Patient Population

Both the Koios DS predicate and the subject device are software applications designed to assist trained interpreting physicians in analyzing the ultrasound images of patients with soft tissue lesions who are being referred for further diagnostic ultrasound examination.

Technological Characteristics

Modality

Koios DS shares the ultrasound modality requirements of Koios DS (K212616)

Technological Characteristics Comparison Table

	Koios DS 3.0 K212616	Koios DS 3.6 K242130
Diagnostic Engine	Breast (v 1.1.0)
Thyroid (v2.2.0)	Breast (v. 3.0.0)
Thyroid (v. 2.2.0)
Workflow Enhancements	Breast Smart Click	Breast Smart Click
	Breast Smart Calipers	Breast Smart Calipers
	Thyroid Smart Calipers	Thyroid Smart Click
		Thyroid Smart Calipers
		Image Registration and
Matching

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| | | OCR Automatic Size and
Position Population |
|--|--|-----------------------------------------------|
| | | |
| | | |
| | | |

Input

Per the respective device descriptions of Koios DS 3.6 and Koios DS (K212616), the following technical characteristics are included in the Koios DS 3.6 (K242130) version:

Thyroid Smart Click

If using this feature, the user can specify a Region of Interest with a single click near the center of the nodule. The Thyroid Smart Click Engine, based on this click, estimates the coordinates of the intended ROI in order to present these to the user. The Thyroid Smart Click engine produces an ROI which best matches the user's Smart Click selection. The resulting ROI contains coordinates for the region. The user can adjust or overrule the resulting ROI if they deem it necessary, and the ROI can then be used as an input to the thyroid diagnostic engine.

Image Registration and Matching

If using this optional feature, the user draws ROIs on images within their image viewing software, which generates a screenshot. The Koios DS Client transmits the ROI screenshot to the Koios DS Server Image Matching Engine to generate the ROI on the underlying DICOM image and displays it to the user for review. The ROI and associated DICOM data are sent to the Koios DS Server for processing by the appropriate Diagnostic Engine.

The screenshots (or image region replicas) utilized during an image matching query are not saved. Image region replicas are captured by the software and stored as uncompressed bitmap data in memory and processed by the Image Matching engine as such. This process is agnostic to the underlying body part and functions generically for any breast and/or thyroid image selection.

The image matching engine performs its search independently for each query region based purely and solely on image data, without external information pertaining to modality or other orthogonal views. The search candidates are limited in scope to database images from within the same study as the search query. As a result, if a search query is created from a thyroid image

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from a specified thyroid study, only that specific thyroid study is searched for potential matching image regions. The same search query process is utilized for breast images; only the specific breast study is searched for matching image regions.

Auto-populate Size and Position using Optical Character Recognition (OCR)

If using this feature, the user provides an ultrasound image which has previously been annotated by a radiologist or technician. The OCR engine then identifies all the image and parses out text which is relevant to the study of lesions or nodules. This functionality allows the user to quickly interpret and transcribe the locations and measurements of ultrasound findings.

Output

When comparing breast functionality, the subject Koios DS Breast Engine demonstrates a significant categorical output performance increase in AUC (1.6%), a significant increase in sensitivity (0.6%), and a significant increase (2.2%) in specificity. Koios DS retains the identical descriptor outputs and performance for the assessment of shape and orientation.

The Koios DS thyroid performance is unchanged from the predicate device including when accounting for new technological characteristics. Performance testing outlined in the next section demonstrates equivalence and non-inferiority for each new technical characteristic.

Auto-populate Size and Position using Optical Character Recognition (OCR)

The output of the OCR Engine consists of two categories - measurements and text. The measurements output consists of three elements: description (e.g. the length, height, or width of a nodule), value (a field representing the numerical value of measurement), and unit (the modifier to the value specifying a unit of measurement, e.g. cm or mm). The text output consists of two elements: a dictionary (with fields corresponding to each possible category of information present in the findings text), and values (corresponding to the predicted output for each field).

8. Performance Testing - Standalone Testing

Breast Engine

To compare the performance of the subject device to the predicate device (K212616, product codes POK, QIH), the subject device's updated breast classification engine was compared to the

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predicate device on the same 900 case validation set. Bench testing was performed on the Koios DS 3.6 breast engine to ascertain the degree of concordance with trained interpreting physicians. Ground truth for malignancy risk classification was determined by pathology or 1year follow-up for cases that were not biopsied. The system was analyzed on 900 lesions from 900 different patients set aside from the system's training data for the purpose of validating performance. Each lesion was represented by two orthogonal images (e.g. radial and antiradial), providing a total of 1800 images. An expanded validation set of 1014, including these 900 and an additional 114 cases is used to test for dataset drift. System performance on the 900 cases reported an AUC of 94.5%, with a Sensitivity of 0.976 [0.960, 0.992] and a Specificity of 0.632 [0.588, 0.676].

The table below provides a detailed evaluation of the breast diagnostic engine across key performance metrics. Direct comparison with the Koios DS v3.0 breast engine's performance determined there is a significant increase in AUC (1.6%), a significant increase in sensitivity (0.6%), and a significant increase (2.2%) in specificity.

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| Koios DS Engine (Breast) Test | Engine Version 1.1.0 (Previous)
| Engine Version 3.0.0
(Current)
|
|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|
| 1: Malignancy Risk Classifier AUC | 0.929 [0.913, 0.945] | 0.945 [0.932, 0.959] |
| 2: Categorical Output | | |
| Sensitivity | 0.97 [0.96, 0.99] | 0.976 [0.960, 0.992] |
| Specificity | 0.61 [0.57, 0.66] | 0.632 [0.588, 0.676] |
| 3: Sensitivity to Region of Interest | 0.019 | 0.012 |
| 4. Sensitivity to Transducer
Frequency | High frequency (>=15MHz),
AUC = 0.940 [0.907, 0.974]

Low frequency (=15MHz),
AUC = 0.948 [0.917, 0.978]

Low frequency (4 Data were obtained from the Breast Cancer Surveillance Consortium, funded by the National Cancer Institute (HHSN261201100031C). From the Breast Cancer Surveillance Consortium website, http://www.bcsc-research.org/

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Image /page/34/Figure/0 description: The image contains two bar charts. The first bar chart shows the distribution of tumor sizes, with the x-axis representing size in millimeters and the y-axis representing the count. The tumor sizes are grouped into bins: n/a (7, 0.8%), 0 for all reader pairs. The standard error for USE + DS and USE Alone was computed to assess if the shifts in the metric were significant. The average Kendall Tau-B of USE Alone was .5404 (.5301, .5507) and the average Kendall Tau-B of USE + DS was .6797 (.6653, .6941) with 95% Cl demonstrating a significant increase in the metric (α = .05).

Also assessed was the effect of Koios DS on intra-operator variability leveraging 150 reads that did not switch from USE Alone to USE + DS across the washout session in the reader study (75 each). USE Alone class switching rate was 13.6% and the USE + DS class switching rate was 10.8% (p = 0.042), demonstrating a statistically significant reduction in intra-reader variability when using USE + DS.

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Thyroid

An observational case-controlled, Multi-Reader, Multi-Case (MRMC) retrospective clinical trial (CRRS-3) was executed to determine the effect of Koios DS Thyroid on reader performance.

Effect on performance was defined by measuring the area under the Receiver Operating Characteristic (ROC) Curve (AUC) when Koios DS and an ultrasound examination were combined (USE + DS), compared to unassisted TI-RADS based Reader performance (USE Alone). All data analysis cases consisted of USE Alone and USE + DS image readings in patients that presented with a thyroid abnormality through any form of imaging or physical examination and were referred for diagnostic ultrasound where a nodule was subsequently discovered.

Data analysis in the CRRS-3 study was based on 650 retrospectively collected cases that were assigned a TI-RADS Assessment Category 1 through 5 at the of initial review at study entry based upon the interpreting physician of the ultrasound evaluation. The study consisted of 15 readers reviewing and interpreting 650 cases twice (1300 total cases per reader). All data analysis was based on two randomized evaluations of each case with and without the assistance of Koios DS software with a 1-month washout period between corresponding presentations of the case and interpretations by physicians.

The study design called for a mixed population of physician readers (11/15 or 73% US based) and cases (500 or 77% US based) coming from both the US and Europe. Readers with a current medical license who met inclusion criteria and completed the study training protocol were considered trained interpreting physicians for study purposes. Readers possessed varying levels of training and experience, as detailed below:

Reader ID	Reader Category	Experience (post-residency)
R1	Domestic Endocrinologist (End)