IB Lab KOALATM
Knee Osteoarthritis Labeling Assistant
Osteoarthritis is a paralyzing joint disease that can lead to joint replacement. Early detection as well as therapy can prevent unnecessary surgeries. Our first FDA cleared and CE-marked module KOALA supports physicians in detecting signs of knee osteoarthritis based on standard joint parameters and OARSI criteria of standing radiographs of the knee.
IB Lab KOALATM
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IB Lab KOALATM
Knee Osteoarthritis Labeling Assistant
For measuring radiographic signs of knee osteoarthritis.
5 radiological findings and measurements including:
Kellgren & Lawrence grade, minimum joint space width,
joint space narrowing, sclerosis, osteophytosis
This product is CE-certified.
FDA-cleared version is available.
Knee osteoarthritis is a paralyzing joint disease that can lead to joint replacement. Knee osteoarthritis has a lifetime risk as high as 45% , with the risk becoming even more severe due to two major risk factors: aging and obesity [1, 2] . Knee osteoarthritis affects over 200 million patients worldwide , resulting in approximately 100 million knee radiographs taken in the EU alone in 2020 . Consistent tracking of radiographic damage over time could help early diagnosis and prevention of disease progression. However, diagnosing the loss of cartilage, the hallmark feature of knee osteoarthritis, is difficult to do consistently in practice, and especially for non-experts. Radiologists read an average of 10 knee radiographs per day, amounting to approximately 40 minutes of the daily workload .
IB Lab’s diagnostic support tool KOALA uses deep learning technology for detecting radiographic signs of knee osteoarthritis and augments the reporting workflow. The AI-driven software KOALA scores the stage of the osteoarthritis according to the Kellgren & Lawrence grading system. KOALA provides precise and automated measurements of the minimum joint space width, assessment of the severity of joint space narrowing, osteophytosis and sclerosis based OARSI criteria for these parameters.
KOALA highlights relevant clinical findings by applying the latest international medical standards to enable timely and accurate decision making. The findings are summarized in a visual output report, attached to the original x-ray image and saved automatically in the PACS system. The AI-results are fed as text into the predefined RIS-template for accelerated reporting. KOALA facilitates monitoring of disease progression by facilitating comparison of radiographic disease parameters over time.
- Saves time on routine tasks when assessing knee radiographs
- Enables instant, verifiable decision making in difficult cases
- Facilitates monitoring of knee osteoarthritis progression
- Empowers non-specialists to perform at specialist level in detecting radiographic signs of knee osteoarthritis
Training & Validation
- Deep learning algorithms trained on over 35,000 individual knee radiographs
- Data from a longitudinal study with centers across the United States
- Each image was consensus-graded by board certified radiologists following OARSI criteria and the Kellgren and Lawrence scale
- The AI follows the established radiological workflow: measurement of anatomical distances and angles, recognition of disease symptoms, standardized classification and reporting
- Validated on over 10.000 knees
- CE-certified (similar FDA-cleared version available)
Automated AI report
KOALA’s report suggests slight signs of
sclerosis of the left knee, as indicated by the OARSI score, which might point to early OA due to bone remodelling.
Tighter monitoring and preservative treatment of this patient might allow to to conserve cartilage before resorting to surgeries and knee replacements.
 Paixao, T., et al.: A novel quantitative metric for joint space width: data from the Osteoarthritis Initiative (OAI). Osteoarthritis and Cartilage (2020).
 Nehrer, S, Ljuhar, R, Steindl, P, Simon, R, Maurer, D, Ljuhar, D, et al.: Automated knee osteoarthritis assessment increases physicians’ agreement rate and accuracy: data from the osteoarthritis initiative Cartilage, Epub 2019, Nov 24.
 Ljuhar R, Tobias Haftner, Benjamin Norman, Davul Ljuhar, Astrid Fahrleitner-Pammer, Hans-Peter Dimai, Stefan Nehrer: A Novel Method For Identifying Radiographic Baseline Risk Of Osteoarthritis Using An Anisotropy-Based Texture Analysis Algorithm: Data From The Osteoarthritis Initiative, OARSI World Congress on Osteoarthritis, Las Vegas, 2017.
 Bertalan Z, Ljuhar R, Nehrer S, Norman B, Ljuhar D, Fahrleitner-Pammer A, Dimai HP: Combining fractal- and entropy-based bone texture analysis for the prediction of Osteoarthritis: data from the Multicenter Osteoarthritis study (MOST), ASBMR Annual Meeting, Denver, USA, 2017.
 Ljuhar R, Norman B, Ljuhar D, Haftner T, Hladuvka J, Bui Thi Mai P, Canhão H, Branco J, Rodrigues A, Gouveia N, Nehrer S, Fahrleitner-Pammer A, Dimai HP: A novel feature selection algorithm based on bone micro architecture analysis to identify osteoarthritis, WCO-ICO-ESCO World Congress, Malaga, Spain, 2016.
 Ljuhar R, Norman B, Ljuhar D, Haftner T, Hladuvka J, Bui Thi Mai P, Canhão H, Branco J,Rodrigues A, Gouveia N, Nehrer S, Fahrleitner-Pammer A, Dimai HP: A clinical study to examine thresholds of joint space width and joint space area for identification of knee osteoarthritis, OARSI World Congress on Osteoarthritis, Amsterdam, Netherlands, 2016.
 Ljuhar R: Basierend auf Analysen der subchondralen Knochenmikroarchitektur: Ein neuer Algorithmus für die Bewertung von Osteoarthritis, Fakten der Rheumatologie, Februar 2016.
 Ljuhar R, Norman B, Ljuhar D, Haftner T, Hladuvka J, Bui Thi Mai P, Canhão H, Branco J, Rodrigues A, Gouveia N, Nehrer S, Fahrleitner-Pammer A, Dimai HP: A novel combination of bone micro architecture descriptors and selected ROIs for the identification of osteoarthritis, ASBMR Annual Meeting, Atlanta, USA, 2016.
 Ljuhar R , Norman B, Canhão H, Branco J, Rodrigues A, Gouveia N, Hladuvka J, Fahrleitner-Pammer A, Dimai HP: A novel method for the assessment of joint space width and subchondral bone texture, ASBMR Annual Meeting, Seattle, USA, 2015.
 Ljuhar R, Norman B, Ljuhar D, Nehrer S, Riedl M, Hladuvka J, Stiassny F, Wetzl C, Westhauser C: A computer-assisted diagnosis and monitoring of degenerative bone diseases, X ORTOMED Congress – Società Italiana di Ortopedia e Medicina, Florence, Italy, 2015.
- Murphy et al: Lifetime risk of symptomatic knee osteoarthritis, Arthritis Care & Research, 2008.
- Losina et al.: Lifetime risk and age of diagnosis of symptomatic knee osteoarthritis in the US, Arthritis Care & Research, 2013.
- Global, Regional, and National Incidence, Prevalence, and Years Lived with Disability for 310 Diseases and Injuries, 1990–2015: A Systematic Analysis for the Global Burden of Disease Study 2015, The Lancet, October 2016.
- IB Lab Market Survey 2020.