Search Results

IB Lab KOALA is a radiological fully-automated image processing software 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.

The Knee OsteoArthritis Labeling Assistant (KOALA) software provides metric measurements of the ioint space width and indicators for presence of radiographic features of osteoarthritis (OA) on posterior-anterior-posterior (PA/AP) knee X-ray images. The outputs aid clinical professionals who are interested in the analysis of knee OA in adult patients, either suffering from knee OA or having an elevated risk of developing the disease.

Outputs are summarized in a KOALA report that can be viewed on any FDA approved DICOM viewer workstation. KOALA operates in a Linux environment and can be deployed to be compatible with any operating system supporting the third-party software Docker. The integration environment has to support KOALA data input and output requirements. The device does not interact with the patient directly, nor does it control any life-sustaining devices.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Note: The document states that the "analysis supports good agreement between the two sets of measurements" for JSW, indicating these values meet the unstated acceptance criteria for agreement. The sensitivity and specificity values are direct reported performance against implied thresholds for clinical utility.

Study Details

1. Sample size used for the test set and the data provenance:

   • Sample Size: 6597 radiographs.
   • Data Provenance: From a large longitudinal US study, the Osteoarthritis Initiative (OAI) study. The data is retrospective.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Number of Experts: Three physicians.
   • Qualifications: The document does not specify their exact qualifications (e.g., number of years of experience, specific specialty like "radiologist"), only that they are "physicians."

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • The ground truth was established by three physicians "following adjudication procedures for discrepancies." This implies a form of consensus-based adjudication, likely majority rule or discussion to resolve disagreements. The specific method (e.g., 2+1 meaning if two agree, that's the truth, otherwise a third arbitrates) is not explicitly detailed.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance was not reported in this section. The study described is a standalone performance validation of the AI system.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

   • Yes, a standalone clinical performance validation was done. The reported sensitivities, specificities, and JSW measurements are for the KOALA algorithm's performance alone.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Expert Consensus: The ground truth was established by three physicians based on Kellgren-Lawrence grades and OARSI guidelines for osteophyte, sclerosis, and joint space narrowing grades, following adjudication procedures. This is a form of expert consensus derived from image interpretation.

7. The sample size for the training set:

   • The document does not specify the sample size for the training set. It only mentions that the machine-learning algorithms were "trained on medical images."

8. How the ground truth for the training set was established:

   • The document does not specify how the ground truth for the training set was established. It only implies that the algorithms were "trained on medical images" with sufficient ground truth to achieve high accuracy.

Ask a specific question about this device