Koios DS

Koios Medical, Inc.
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.”
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Information source: Vendor
Last updated: November 19, 2024

General Information

General
Product name Koios DS
Company Koios Medical, Inc.
Subspeciality Breast, Thyroid
Modality Ultrasound
Disease targeted Breast cancer, thyroid cancer
Key-features Lesion/nodule segmentation, lesion/nodule classification, auto-population of lesion/nodule descriptors, AI independent risk assessment, alignment to diagnostic classification systems
Suggested use During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion
After: diagnosis verification

Technical Specifications

Data characteristics
Population Adult (>= 22 years) female patients with soft tissue breast lesions and/or adult (>= 22 years) patients with thyroid nodules suspicious for cancer.
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 Secondary Capture and/or DICOM Structured Report, DICOM Client such as a scanner, selected PACS integrations
Technology
Integration Integration in standard reading environment (PACS), Integration CIS (Clinical Information System), 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, etc.
Processing time < 3 sec

Regulatory

Certification
CE
Certified, Class IIb , MDR
FDA 510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE) Koios 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 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.

Market

Market presence
On market since 12-2021
Distribution channels GE Healthcare, RMS Medical Devices, Amplify SDK, Alma AI MARKETPLACE
Countries present (clinical, non-research use) 25
Paying clinical customers (institutes) >80
Research/test users (institutes) >50
Pricing
Pricing model Subscription
Based on Number of users, Number of analyses

Evidence

Evidence
Peer reviewed papers on performance

  • Avoidable biopsies? Validating artificial intelligence-based decision support software in indeterminate thyroid nodules (read)

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

  • Improving the Efficacy of ACR TI‑RADS Through Deep Learning‑Based Descriptor Augmentation (read)

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

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

  • Can an Artificial Intelligence Decision Aid Decrease False-Positive Breast Biopsies? (read)

  • Impact of Original and Artificially Improved Artificial Intelligence-based Computer-Aided Diagnosis on Breast US Interpretation (read)

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

  • Should we ignore, follow, or biopsy? Impact of artificial intelligence decision support on breast ultrasound lesion assessment (read)
Non-peer reviewed papers on performance

  • SIIM2018 - Artificial Intelligence in Breast Ultrasound: Moving from Standalone Performance to the Physician-system Interface (read)

  • SPMB 2016 - Decision Quality Support in Diagnostic Breast Ultrasound through Artificial Intelligence (read)

  • Artificial intelligence in low- And middle-income countries: Innovating global health radiology (read)

  • RSNA 2020 - Mango - Decreasing Benign Breast Ultrasound Biopsies: Prospective Use of AI Decision Support (read)

  • SIIM 2021 - Barinov - Improving the Efficacy of TI-RADS Through Artificial Intelligence (read)

  • SBI ACR Breast Imaging Symposium 2020 - Cavallo - AI Analysis of Ultrasound Images Could Decrease Benign Breast Biopsies (read)
Other relevant papers