Artificial-Intelligence-Aided Radiographic Diagnostic of Knee Osteoarthritis Leads to a Higher Association of Clinical Findings with Diagnostic Ratings

Markus Neubauer, Lukas Moser, Johannes Neugebauer, Marcus Raudner, Barbara Wondrasch, Magdalena Führer, Robert Emprechtinger, Dietmar Dammerer, Richard Ljuhar, Christoph Salzlechner, Stefan Nehrer

Publikation: Beitrag in Fachzeitschrift (peer-reviewed)Artikel in Fachzeitschrift

2 Zitate (Scopus)

Abstract

BACKGROUND: Radiographic knee osteoarthritis (OA) severity and clinical severity are often dissociated. Artificial intelligence (AI) aid was shown to increase inter-rater reliability in radiographic OA diagnosis. Thus, AI-aided radiographic diagnoses were compared against AI-unaided diagnoses with regard to their correlations with clinical severity.

METHODS: Seventy-one DICOMs (m/f = 27:42, mean age: 27.86 ± 6.5) (X-ray format) were used for AI analysis (KOALA software, IB Lab GmbH). Subjects were recruited from a physiotherapy trial (MLKOA). At baseline, each subject received (i) a knee X-ray and (ii) an assessment of five main scores (Tegner Scale (TAS); Knee Injury and Osteoarthritis Outcome Score (KOOS); International Physical Activity Questionnaire; Star Excursion Balance Test; Six-Minute Walk Test). Clinical assessments were repeated three times (weeks 6, 12 and 24). Three physicians analyzed the presented X-rays both with and without AI via KL grading. Analyses of the (i) inter-rater reliability (IRR) and (ii) Spearman's Correlation Test for the overall KL score for each individual rater with clinical score were performed.

RESULTS: We found that AI-aided diagnostic ratings had a higher association with the overall KL score and the KOOS. The amount of improvement due to AI depended on the individual rater.

CONCLUSION: AI-guided systems can improve the ratings of knee radiographs and show a stronger association with clinical severity. These results were shown to be influenced by individual readers. Thus, AI training amongst physicians might need to be increased. KL might be insufficient as a single tool for knee OA diagnosis.

OriginalspracheEnglisch
Aufsatznummer744
FachzeitschriftJournal of Clinical Medicine
Jahrgang12
Ausgabenummer3
DOIs
PublikationsstatusVeröffentlicht - Feb. 2023

Fingerprint

Untersuchen Sie die Forschungsthemen von „Artificial-Intelligence-Aided Radiographic Diagnostic of Knee Osteoarthritis Leads to a Higher Association of Clinical Findings with Diagnostic Ratings“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren