TY - JOUR

T1 - Constructing a clinical radiographic knee osteoarthritis database using artificial intelligence tools with limited human labor

T2 - A proof of principle

AU - Lenskjold, Anders

AU - Brejnebøl, Mathias W.

AU - Nybing, Janus U.

AU - Rose, Martin H.

AU - Gudbergsen, Henrik

AU - Troelsen, Anders

AU - Moller, Anne

AU - Raaschou, Henriette

AU - Boesen, Mikael

N1 - Publisher Copyright: © 2023 The Authors

PY - 2024

Y1 - 2024

N2 - Objective: To create a scalable and feasible retrospective consecutive knee osteoarthritis (OA) radiographic database with limited human labor using commercial and custom-built artificial intelligence (AI) tools. Methods: We applied four AI tools, two commercially available and two custom-built tools, to analyze 6 years of clinical consecutive knee radiographs from patients aged 35–79 at the University of Copenhagen Hospital, Bispebjerg-Frederiksberg Hospital, Denmark. The tools provided Kellgren-Lawrence (KL) grades, joint space widths, patella osteophyte detection, radiographic view detection, knee joint implant detection, and radiographic marker detection. Results: In total, 25,778 knee radiographs from 8575 patients were included in the database after excluding inapplicable radiographs, and 92.5% of the knees had a complete OA dataset. Using the four AI tools, we saved about 800 hours of radiologist reading time and only manually reviewed 16.0% of the images in the database. Conclusions: This study shows that clinical knee OA databases can be built using AI with limited human reading time for uniform grading and measurements. The concept is scalable temporally and across geographic regions and could help diversify further OA research by efficiently including radiographic knee OA data from different populations globally. We can prevent data dredging and overfitting OA theories on existing trite cohorts by including various gene pools and continuous expansion of new clinical cohorts. Furthermore, the suggested tools and applied approaches provide an ability to retest previous hypotheses and test new hypotheses on real-life clinical data with current disease prevalence and trends.

AB - Objective: To create a scalable and feasible retrospective consecutive knee osteoarthritis (OA) radiographic database with limited human labor using commercial and custom-built artificial intelligence (AI) tools. Methods: We applied four AI tools, two commercially available and two custom-built tools, to analyze 6 years of clinical consecutive knee radiographs from patients aged 35–79 at the University of Copenhagen Hospital, Bispebjerg-Frederiksberg Hospital, Denmark. The tools provided Kellgren-Lawrence (KL) grades, joint space widths, patella osteophyte detection, radiographic view detection, knee joint implant detection, and radiographic marker detection. Results: In total, 25,778 knee radiographs from 8575 patients were included in the database after excluding inapplicable radiographs, and 92.5% of the knees had a complete OA dataset. Using the four AI tools, we saved about 800 hours of radiologist reading time and only manually reviewed 16.0% of the images in the database. Conclusions: This study shows that clinical knee OA databases can be built using AI with limited human reading time for uniform grading and measurements. The concept is scalable temporally and across geographic regions and could help diversify further OA research by efficiently including radiographic knee OA data from different populations globally. We can prevent data dredging and overfitting OA theories on existing trite cohorts by including various gene pools and continuous expansion of new clinical cohorts. Furthermore, the suggested tools and applied approaches provide an ability to retest previous hypotheses and test new hypotheses on real-life clinical data with current disease prevalence and trends.

KW - Artificial intelligence

KW - Data diversity

KW - Database creation

KW - Feasibility study

KW - Knee OA

KW - Proof of concept

U2 - 10.1016/j.joca.2023.11.014

DO - 10.1016/j.joca.2023.11.014

M3 - Journal article

C2 - 38043857

AN - SCOPUS:85179821464

VL - 32

SP - 310

EP - 318

JO - Osteoarthritis and Cartilage

JF - Osteoarthritis and Cartilage

SN - 1063-4584

IS - 3

ER -