Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Smolle, MA; Goetz, C; Maurer, D; Vielgut, I; Novak, M; Zier, G; Leithner, A; Nehrer, S; Paixao, T; Ljuhar, R; Sadoghi, P.
Artificial intelligence-based computer-aided system for knee osteoarthritis assessment increases experienced orthopaedic surgeons' agreement rate and accuracy.
Knee Surg Sports Traumatol Arthrosc. 2023; 31(3):1053-1062
Doi: 10.1007/s00167-022-07220-y
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- Führende Autor*innen der Med Uni Graz
- Sadoghi Patrick
- Smolle Maria Anna
- Co-Autor*innen der Med Uni Graz
- Leithner Andreas
- Novak Michael
- Vielgut Ines
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- Abstract:
- PURPOSE: The aims of this study were to (1) analyze the impact of an artificial intelligence (AI)-based computer system on the accuracy and agreement rate of board-certified orthopaedic surgeons (= senior readers) to detect X-ray features indicative of knee OA in comparison to unaided assessment and (2) compare the results to those of senior residents (= junior readers). METHODS: One hundred and twenty-four unilateral knee X-rays from the OAI study were analyzed regarding Kellgren-Lawrence grade, joint space narrowing (JSN), sclerosis and osteophyte OARSI grade by computerized methods. Images were rated for these parameters by three senior readers using two modalities: plain X-ray (unaided) and X-ray presented alongside reports from a computer-assisted detection system (aided). After exclusion of nine images with incomplete annotation, intraclass correlations between readers were calculated for both modalities among 115 images, and reader performance was compared to ground truth (OAI consensus). Accuracy, sensitivity and specificity were also calculated and the results were compared to those from a previous study on junior readers. RESULTS: With the aided modality, senior reader agreement rates for KL grade (2.0-fold), sclerosis (1.42-fold), JSN (1.37-fold) and osteophyte OARSI grades (3.33-fold) improved significantly. Reader specificity and accuracy increased significantly for all features when using the aided modality compared to the gold standard. On the other hand, sensitivity only increased for OA diagnosis, whereas it decreased (without statistical significance) for all other features. With aided analysis, senior readers reached similar agreement and accuracy rates as junior readers, with both surpassing AI performance. CONCLUSION: The introduction of AI-based computer-aided assessment systems can increase the agreement rate and overall accuracy for knee OA diagnosis among board-certified orthopaedic surgeons. Thus, use of this software may improve the standard of care for knee OA detection and diagnosis in the future. LEVEL OF EVIDENCE: Level II.
- Find related publications in this database (using NLM MeSH Indexing)
- Humans - administration & dosage
- Osteoarthritis, Knee - pathology
- Artificial Intelligence - administration & dosage
- Osteophyte - administration & dosage
- Orthopedic Surgeons - administration & dosage
- Sclerosis - pathology
- Knee Joint - pathology
- Computers - administration & dosage
- Find related publications in this database (Keywords)
- Knee osteoarthritis
- Artificial intelligence
- Computer aided detection
- Reader study