IB Lab FLAMINGO

ImageBiopsy Lab

IB Lab FLAMINGO provides detection and labeling of vertebral fractures on a variety of CT imaging indications. The software provides prompt detection of vertebral fractures as secondary radiological findings. Furthermore, IB Lab FLAMINGO enables standardized detection of vertebral fractures as secondary radiological findings.

Information source: Vendor
Last updated: Dec. 19, 2023

General Information

General
Product name	IB Lab FLAMINGO
Company	ImageBiopsy Lab
Subspeciality	MSK
Modality	CT
Disease targeted	Vertebral compression fractures, osteoporosis
Key-features	Vertebral labeling of thoracic and lumbar spine, vertebral fracture detection on subject and vertebral level (absence/presence)
Suggested use	Opportunistically scanned for vertebral compression fractures during CT examination conducted for various other reasons

Technical Specifications

Data characteristics
Population	The usage of IB Lab FLAMINGO is limited to use in adults aged 50 years and older.
Input	Any spine containing CT, max. slice thickness: 3 mm, patient age >50, contrast, non-contrast, all reconstruction kernels
Input format	DICOM
Output	Vertebral label as overlay on image, list of vertebra with fracture detected, graphical summary of findings, subject level abesence/presence of fracture
Output format	DICOM, PDF
Technology
Integration	Integration in standard reading environment (PACS)
Deployment	Locally on dedicated hardware, 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	1 - 10 minutes

Regulatory

Certification
CE	Certified, Class IIa , MDR
FDA	No or not yet
Intended Use Statements
Intended use (according to CE)

Market

Market presence
On market since	11-2023
Distribution channels
Countries present (clinical, non-research use)	EU
Paying clinical customers (institutes)
Research/test users (institutes)	1
Pricing
Pricing model	Subscription
Based on	Number of analyses

Evidence

Evidence
Peer reviewed papers on performance
Non-peer reviewed papers on performance
Other relevant papers	External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans (read) Towards Improved Identification of Vertebral Fractures in Routine Computed Tomography (CT) Scans: Development and External Validation of a Machine Learning Algorithm (read)