TY - JOUR AU - Boesen, Mikael Ploug AB -

Background: Radiographic evaluation of knee osteoarthritis (KOA) commonly supports clinical findings. Ground truth is difficult to establish and concerns exist on the inter- and intrarater agreement of the findings. RBknee™ is a CE-marked and FDA-cleared AI tool for automatic assessment and reporting of radiographic KOA on standard projection radiographs. Objectives: To investigate how the use of an AI tool affects the accuracy among human readers across three European hospitals in grading the severity of osteoarthritis and associated individual radiographic features. In addition, the performance of the AI tool will also be compared to reference standards established by experts in a stand-alone validation. Methods: In this retrospective multicenter, fully-crossed, multi-reader, multi-case (MRMC) study, the AI support tool RBknee is introduced as a diagnostic intervention. Four Index Readers from each site (two orthopaedic surgeons and two radiologists) will read all studies twice in two sessions separated by a washout period of at least four weeks. In both sessions, the experiment will be arranged so that the AI-aid will be available for half of the images in the first session and for the second half of the images in the second session. The order of the images will be randomised in order to minimise temporal effects and biases. The primary endpoint is the difference in diagnostic test accuracy for radiographic KOA grading without and with the aid of the AI tool and will be measured as the ordinal weighted accuracy. Data: The data includes radiographic images from 225 studies (unique patients, retrospective data) with weight-bearing bilateral PA/AP and LAT projections of the symptomatic knee(s). Each site contributes to the cohort with 75 studies of which 70 will be consecutive and 5 will be selected to balance the prevalence of radiographic KOA severity. Reference standard: The reference standard will be established based on independent grading by three KOA Reference Experts and adjudicated by majority vote. In the case of disagreement, adjudication will be established by consensus. Index test, AI tool (stand-alone validation): The diagnostic accuracy of RBknee will be tested against the reference standard. Index test, Index Readers: The 12 readers will grade KL on the PA/AP projection and patellar osteophytes on the lateral projection.

TI - Protocol for the AutoRayValid-RBknee Study: a Retrospective, Multicenter, Fully-crossed, Multi-reader, Multi-case Study Investigating the Effect of a Knee Osteoarthritis Severity Classification Model on Reader Diagnostic Accuracy JF - medRxiv DO - 10.1101/2022.08.29.22279328 DA - 2022-08-30 UR - https://www.deepdyve.com/lp/medrxiv/protocol-for-the-autorayvalid-rbknee-study-a-retrospective-multicenter-HjVDwUv2dw SP - 22279328 DP - DeepDyve ER -