IB Lab LAMA

ImageBiopsy Lab
IB Lab’s diagnostic support tool LAMA uses deep learning technology for automated measuring of leg geometry to evaluate lower limb deformities, such as leg or lower extremity length discrepancy and knee alignment deformities.
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Information source: Vendor
Last updated: July 14, 2024

General Information

General
Product name IB Lab LAMA
Company ImageBiopsy Lab
Subspeciality MSK
Modality X-ray
Disease targeted Genu varum, genu valgum, lower extremity length discrepancy
Key-features 12 radiological findings and measurements: genu varum, genu valgum, lower extremity length discrepancy, anatomical angles according to Paley, mechanical angles according to Paley, mechanical axis deviation (MAD), hip knee angle, joint line convergence angle, femur length, tibia length, AMA angle
Suggested use Before: flagging acute findings
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 humans with suspected deformities of the lower extremities among them functional limitations, bio-mechanical and cosmetic indications
Input 2D radiograph
Input format DICOM
Output overlay, visual output report, text report in RIS (RIS-templates can be pre-defined)
Output format DICOM Secondary Capture, pdf, structured report
Technology
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based, Hybrid solution
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 I , MDD
FDA 510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE) IB Lab LAMA is a radiological fully-automated image processing software device of either computed (CR) or directly digital (DX) images intended to aid medical professionals in the measurement of lower limbgeometry, assessment of leg alignment and leg length difference. The system is to be used by trained medical professionals including, but not limited to, orthopedists and radiologists. It should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis.

Market

Market presence
On market since 10-2020
Distribution channels GE Healthcare, Siemens, Philips Healthcare, Nuance PIN, Ambra Health, Visus, Incepto, CARPL.ai, Osimis, Eureka Clinical AI
Countries present (clinical, non-research use) 5
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Pay-per-use
Based on Number of analyses

Evidence

Evidence
Peer reviewed papers on performance

  • Deep learning generated lower extremity radiographic measurements are adequate for quick assessment of knee angular alignment and leg length determination (read)

  • Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities (read)

  •  Artificial intelligence-based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility (read)

  •  Deep learning generated lower extremity radiographic measurements are adequate for quick assessment of knee angular alignment and leg length determination (read)

  • Artificial intelligence enables reliable and standardized measurements of implant alignment in long leg radiographs with total knee arthroplasties (read)

  • Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study (read)
Non-peer reviewed papers on performance
Other relevant papers

  • Body height estimation from automated length measurements on standing long leg radiographs using artificial intelligence (read)