IB Lab KOALA™ uses deep learning technology for detecting radiographic signs of knee osteoarthritis and augments the reporting workflow. The software application detects the absence or presence of osteoarthritis according to the Kellgren & Lawrence grading system. It also provides precise and automated measurements of the minimum joint space width, as well as assessment of the severity of joint space narrowing, osteophytosis and sclerosis based on OARSI criteria.
IB Lab 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. IB Lab KOALA™ facilitates monitoring of disease progression by facilitating comparison of radiographic disease parameters over time.
Radiological findings and measurements: absence or presence of radiographic OA based on Kellgren & Lawrence grade, minimum joint space width, absence or presence of joint space narrowing, sclerosis and osteophytosis based on OARSI criteria.
- Enables instant, verifiable decision making in difficult cases
- Facilitates monitoring of knee osteoarthritis progression
- Enhances diagnosing and reporting knee osteoarthritis according to the latest clinical guidelines
- Reduces the risk of inter-rater variability for evaluating knee radiographs in a standardized manner
IB Lab KOALA™ 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 minimum joint space width; the assessment of the presence or absence of sclerosis, joint space narrowing, and osteophytes based OARSI criteria for these parameters; and, the presence or absence of radiographic knee OA based on Kellgren & Lawrence Grading of standing, fixed-flexion radiographs of the knee.
It should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. The system is to be used by trained professionals including, but not limited to, radiologists, orthopedics, physicians and medical technicians.
Training and 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 & 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
- Lifetime risk of symptomatic knee osteoarthritis, Murphyet al. 2008, Arthritis Care & Research
- Lifetime risk and age of diagnosis of symptomatic knee osteoarthritis in the US, Losina et al., 2013, Arthritis Care & Research
- 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: October 2016, The Lancet
- IB Lab Market Survey 2020