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

BackgroundRadiographic 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. ObjectivesTo 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. MethodsIn 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 runs separated by a washout period of at least four weeks. In both runs, 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. DataThe 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 standardThe reference standard will be established based on independent grading by three KOA Reference Experts and adjudicated by majority vote. Where impossible to resolve by majority voting, 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 ReadersThe 12 readers will grade KL on the PA/AP projection and patellar osteophytes on the lateral projection. Administrative informationO_ST_ABSTitleC_ST_ABSThe trial is titled "AutoRayValid-RBknee". Protocol versionRevision History O_TBL View this table: org.highwire.dtl.DTLVardef@eb3b3borg.highwire.dtl.DTLVardef@17ddc82org.highwire.dtl.DTLVardef@17e8e7org.highwire.dtl.DTLVardef@1bbc907org.highwire.dtl.DTLVardef@18b1aa4_HPS_FORMAT_FIGEXP M_TBL C_TBL FundingThis project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 954221 for the EIC SME Instrument project AutoRay. The work only reflects the authors view and the European Commission is not responsible for any use that may be made from the information it contains. Roles and responsibilitiesO_ST_ABSAuthors contributionsC_ST_ABSMWB, MB, EHGO, JV, and KGH initiated the study design and AL, JUN, KZ and HR helped with implementation. MWB, LE and MJL provided statistical expertise in clinical study design and MWB is conducting the primary statistical analysis. MWB, MJL and LE prepared the manuscript and all authors contributed to its refinement and approved the final manuscript. Sponsor Contact informationTrial Sponsor: Radiobotics ApS Contact name: Liv Egnell Address: Esplanaden 8C, 1263 Copenhagen K, Denmark Email: liv@radiobotics.com