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Key Features

  • Vertebral fracture detection on subject and vertebral level (absence/presence)
  • Vertebral labeling of thoracic and lumbar spine

General Information

Product name

IB Lab FLAMINGO

Subspeciality

MSK

Modality

CT

Disease targeted

Vertebral compression fractures, osteoporosis

Main task

Not specified

Technical Specifications

Population

The usage of IB Lab FLAMINGO is limited to use in adults aged 50 years and older.

Patient population age

Not specified

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

Integration

Integration in standard reading environment (PACS)

Deployment

Locally on dedicated hardware, Locally virtualized (virtual machine, Docker)

Trigger for analysis

Automatically, Etc., Image upload, On demand, Right after the image acquisition, Triggered by a user through e.g. a button click

Processing time

1 - 10 minutes

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIa

Verified by Health AI Register

Other certifications

Not specified

Market Presence

On market since

11-2023

AI Platforms

Blackford Analysis, Sectra

Resellers

Not specified

Countries present

EU

Paying clinical customers

Not specified

Research/test users

1

Pricing Information

Pricing model

Subscription

Based on

Number of analyses

Evidence & Research

Other Articles

Other

View

Breaking the silence: AI’s contribution to detecting vertebral fractures in opportunistic CT scans in the elderly—a validation study

Other

View

External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans

Other

View

Towards Improved Identification of Vertebral Fractures in Routine Computed Tomography (CT) Scans: Development and External Validation of a Machine Learning Algorithm

Source: vendor | First published: Dec 19, 2023 | Last updated: Jul 9, 2025