Artificial intelligence in osteoarthritis detection: A systematic review and meta-analysis.
Osteoarthritis Cartilage
; 32(3): 241-253, 2024 Mar.
Article
in En
| MEDLINE
| ID: mdl-37863421
ABSTRACT
OBJECTIVES:
As an increasing number of studies apply artificial intelligence (AI) algorithms in osteoarthritis (OA) detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI, and to compare them with clinicians' performance. MATERIALS ANDMETHODS:
A search in PubMed and Scopus was performed to find studies published up to April 2022 that evaluated and/or validated an AI algorithm for the detection or classification of OA. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the involved joint and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Prediction Model Study Risk of Bias Assessment Tool reporting guidelines.RESULTS:
Of the 61 studies included, 27 studies with 91 contingency tables provided sufficient data to enter the meta-analysis. The pooled sensitivities for AI algorithms and clinicians on internal validation test sets were 88% (95% confidence interval [CI] 86,91) and 80% (95% CI 68,88) and pooled specificities were 81% (95% CI 75,85) and 79% (95% CI 80,85), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 94% (95% CI 90,97) and 91% (95% CI 77,97), respectively.CONCLUSION:
Although the results of this meta-analysis should be interpreted with caution due to the potential pitfalls in the included studies, the promising role of AI as a diagnostic adjunct to radiologists is indisputable.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Osteoarthritis
/
Artificial Intelligence
Type of study:
Systematic_reviews
Limits:
Humans
Language:
En
Journal:
Osteoarthritis Cartilage
Journal subject:
ORTOPEDIA
/
REUMATOLOGIA
Year:
2024
Document type:
Article