Machine learning as an adjunct to expert observation in classification of radiographic knee osteoarthritis: findings from the Hertfordshire Cohort Study
Machine learning as an adjunct to expert observation in classification of radiographic knee osteoarthritis: findings from the Hertfordshire Cohort Study
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.
, Artificial intelligence, Epidemiology, Kellgren and Lawrence, Musculoskeletal, Sasan, mahmoodi
1449-1457
Westbury, Leo
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Fuggle, Nicholas
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Pereira, Diogo
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Oka, Hiroyuki
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Yoshimura, Noriko
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Oe, Noriyuki
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Mahmoodi, Sasan
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Niranjan, Mahesan
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Dennison, Elaine
ee647287-edb4-4392-8361-e59fd505b1d1
Cooper, Cyrus
cbbb15cb-f2b5-446a-92cd-b8d48c7f30a0
July 2023
Westbury, Leo
74411e83-e3ee-48ca-a6d0-4d4888f7bdd5
Fuggle, Nicholas
9ab0c81a-ac67-41c4-8860-23e0fdb1a900
Pereira, Diogo
f9b434e8-1c7f-4d61-a005-654f719e2022
Oka, Hiroyuki
a7e2d904-78eb-445c-a5a6-0f2461f94dd2
Yoshimura, Noriko
00436389-57b3-444c-b69d-0dc934d8e0d5
Oe, Noriyuki
2836cf20-aee5-411f-ab5a-89d8e45fa526
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Dennison, Elaine
ee647287-edb4-4392-8361-e59fd505b1d1
Cooper, Cyrus
cbbb15cb-f2b5-446a-92cd-b8d48c7f30a0
Westbury, Leo, Fuggle, Nicholas, Pereira, Diogo, Mahmoodi, Sasan, Niranjan, Mahesan and Dennison, Elaine
,
et al.
(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), .
(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.
Text
Accepted manuscript (KOACAD and ML for knee OA in HCS)
- Accepted Manuscript
More information
Accepted/In Press date: 25 April 2023
e-pub ahead of print date: 19 May 2023
Published date: July 2023
Additional Information:
Funding Information:
The Hertfordshire Cohort Study was supported by the Medical Research Council University Unit Partnership grant number MRC_MC_UP_A620_1014. CC, EMD and LDW are supported by the UK Medical Research Council [MC_PC_21003; MC_PC_21001]. The funders had no role in the study design, collection, analysis and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. For the purpose of open access, the author has applied a Creative Commons attribution license (CC BY) to any Author Accepted Manuscript version arising from this submission.
Publisher Copyright:
© 2023, The Author(s).
Keywords:
, Artificial intelligence, Epidemiology, Kellgren and Lawrence, Musculoskeletal, Sasan, mahmoodi
Identifiers
Local EPrints ID: 476972
URI: http://eprints.soton.ac.uk/id/eprint/476972
ISSN: 1594-0667
PURE UUID: ae5232fd-3d90-42f8-b1f1-898d0a491f00
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Date deposited: 22 May 2023 17:10
Last modified: 17 Mar 2024 03:54
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Contributors
Author:
Leo Westbury
Author:
Diogo Pereira
Author:
Hiroyuki Oka
Author:
Noriko Yoshimura
Author:
Noriyuki Oe
Author:
Sasan Mahmoodi
Author:
Mahesan Niranjan
Author:
Cyrus Cooper
Corporate Author: et al.
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