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ProductsKoios DS

Vendor source

Reviewed 30 Jun 2026

Completeness Comprehensive (86%)

Koios DS is a software application designed to assist trained interpreting physicians in analyzing breast and thyroid ultrasound images. Koios DS software automatically classifies breast lesions suspicious for cancer based on image data into one of four ACR BI-RADS or European U1-U5 Classification System-aligned categories. Koios DS also categorizes thyroid nodules via the ACR TI-RADS or American Thyroid Association (ATA) risk stratification systems (RSSs) along with a cancer risk assessment presented as the Koios "AI Adapter."

Supported by 16 papers

Koios DS
Koios DS
Koios DS
Koios DS
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Key Features

  • AI independent risk assessment
  • Alignment to diagnostic classification systems
  • Auto-population of lesion/nodule descriptors
  • Lesion/nodule classification
  • Lesion/nodule segmentation

General Information

Product name

Koios DS

Modality

Ultrasound

Main task

Not specified

Subspeciality

Breast, Thyroid

Disease targeted

Breast cancer, thyroid cancer

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIb

Verified by Health AI Register

FDA Certification

Pathway:

510(k) cleared

Class:

Class II

FDA Number:

K190442

View FDA record

Verified by Health AI Register

Other certifications

Not specified

Evidence & Research

Peer-Reviewed Papers

Training AI to Improve Distinction of Triple-Negative Invasive Breast Cancer from Cysts and Fibroadenomas on Ultrasound

Diagnostics · Apr 2026

Performance of AI-Based software in predicting malignancy risk in breast lesions identified on targeted ultrasound

European Journal of Radiology · Jul 2025

Integrating Artificial Intelligence in Thyroid Nodule Management: Clinical Outcomes and Cost-effectiveness Analysis

The Journal of Clinical Endocrinology & Metabolism · Jul 2025

Preliminary analysis of AI-based thyroid nodule evaluation in a non-subspecialist endocrinology setting

Endocrine · Jun 2025

Impact of an artificial intelligence decision support system among radiologists with different levels of experience in breast ultrasound: A prospective study in a tertiary center

European Journal of Radiology · Feb 2025

Artificial Intelligence in Diagnostic Breast Ultrasound: A Comparative Analysis of Decision Support Among Radiologists With Various Levels of Expertise

European Journal of Breast Health · Jan 2025

Investigation of artificial intelligence–based clinical decision support system’s performance in reducing the fine needle aspiration rate of thyroid nodules: A pilot study

Ultrasound · Dec 2024

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound

Thyroid® · Oct 2024

Avoidable biopsies? Validating artificial intelligence–based decision support software in indeterminate thyroid nodules

Surgery · Oct 2024

Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation

Journal of Digital Imaging · Aug 2023

A role for breast ultrasound Artificial Intelligence decision support in the evaluation of small invasive lobular carcinomas

Clinical Imaging · May 2023

Toward AI-supported US Triage of Women with Palpable Breast Lumps in a Low-Resource Setting

Radiology · May 2023

Impact of Original and Artificially Improved Artificial Intelligence–based Computer-aided Diagnosis on Breast US Interpretation

Journal of Breast Imaging · Apr 2021

Can an Artificial Intelligence Decision Aid Decrease False-Positive Breast Biopsies?

Ultrasound Quarterly · Dec 2020

Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment

American Journal of Roentgenology · Apr 2020

Impact of Data Presentation on Physician Performance Utilizing Artificial Intelligence-Based Computer-Aided Diagnosis and Decision Support Systems

Journal of Digital Imaging · Oct 2018

Technical Specifications

Patient population age

Not specified

Input

2 orthogonal still ultrasound views of either breast lesions or thyroid nodules

Input format

DICOM

Output

BI-RADS/TI-RADS Categorization and Classifiers, Shape, Orientation, Confidence Level Indicator, Position, Size

Output format

DICOM Client such as a scanner, DICOM Secondary Capture and/or DICOM Structured Report, Selected pacs integrations

Integration

Integration CIS (Clinical Information System), Integration in standard reading environment (PACS), Stand-alone third party application, Stand-alone webbased

Deployment

Locally virtualized (virtual machine, Docker)

Trigger for analysis

Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload

Processing time

< 10 seconds

Population

Adult (>= 22 years) female patients with soft tissue breast lesions and/or adult (>= 22 years) patients with thyroid nodules suspicious for cancer.

Market Presence

On market since

12-2021

AI Platforms

Alma Health Platform, CARPL.AI, GE Healthcare, Harrison.ai, HCKonnect

Resellers

Amplify SDK, RMS Medical Devices

Countries present

25

Paying clinical customers

>80

Research/test users

>50

Pricing Information

Pricing model

Subscription

Based on

Number of analyses, Number of users

Source: Vendor | First published: Oct 24, 2023 | Last updated: Jun 30, 2026