Westbury, Leo, Fuggle, Nicholas, Pereira, Diogo, Mahmoodi, Sasan, Niranjan, Mahesan and Dennison, Elaine , (2023) Machine learning as an adjunct to expert observation in classification of radiographic knee osteoarthritis: findings from the Hertfordshire Cohort Study. Aging Clinical and Experimental Research, 35 (7), 1449-1457. (doi:10.1007/s40520-023-02428-5).
Abstract
Background: Osteoarthritis is the most prevalent type of arthritis. Many approaches exist for characterising radiographic knee OA, including machine learning (ML).
Aims: To examine Kellgren and Lawrence (K&L) scores from ML and expert observation, minimum joint space and osteophyte in relation to pain and function.
Methods:Participants from the Hertfordshire Cohort Study, comprising individuals born in Hertfordshire from 1931-1939, were analysed. Radiographs were assessed by clinicians and ML (convolutional neural networks) for K&L scoring. Medial minimum joint space and osteophyte area were ascertained using the knee OA computer-aided diagnosis (KOACAD) program. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was administered. Receiver operating characteristic analysis was implemented for minimum joint space, osteophyte, and observer- and ML-derived K&L scores in relation to pain (WOMAC pain score > 0) and impaired function (WOMAC function score > 0).
Results:359 participants (aged 71-80) were analysed. Among both sexes, discriminative capacity regarding pain and function was fairly high for observer-derived K&L scores (area under curve (AUC): 0.65 (95% CI: 0.57,0.72) to 0.70 (0.63,0.77)); results were similar among women for ML-derived K&L scores. Discriminative capacity was moderate among men for minimum joint space in relation to pain (0.60 (0.51,0.67)) and function (0.62 (0.54,0.69)). AUC<0.60 for other sex-specific associations.
Discussion: Observer-derived K&L scores had higher discriminative capacity regarding pain and function compared to minimum joint space and osteophyte. Among women, discriminative capacity was similar for observer- and ML-derived K&L scores.
Conclusion: ML as an adjunct to expert observation for K&L scoring may be beneficial due to the efficiency and objectivity of ML.
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