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Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of muscle disorders including adult and juvenile dermatomyositis, polymyositis, immune-mediated necrotising myopathy and sporadic inclusion body myositis, all of which present with variable symptoms and disease progression. The identification of effective biomarkers for IIMs has been challenging due to the heterogeneity between IIMs and within IIM subgroups, but recent advances in machine learning (ML) techniques have shown promises in identifying novel biomarkers. This paper reviews recent studies on potential biomarkers for IIM and evaluates their clinical utility. We also explore how data analytic tools and ML algorithms have been used to identify biomarkers, highlighting their potential to advance our understanding and diagnosis of IIM and improve patient outcomes. Overall, ML techniques have great potential to revolutionize biomarker discovery in IIMs and lead to more effective diagnosis and treatment., (© The Author(s) 2024. Published by Oxford University Press.)

Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of muscle disorders including adult and juvenile dermatomyositis, polymyositis, immune-mediated necrotising myopathy and sporadic inclusion body myositis, all of which present with variable symptoms and disease progression. The identification of effective biomarkers for IIMs has been challenging due to the heterogeneity between IIMs and within IIM subgroups, but recent advances in machine learning (ML) techniques have shown promises in identifying novel biomarkers. This paper reviews recent studies on potential biomarkers for IIM and evaluates their clinical utility. We also explore how data analytic tools and ML algorithms have been used to identify biomarkers, highlighting their potential to advance our understanding and diagnosis of IIM and improve patient outcomes. Overall, ML techniques have great potential to revolutionize biomarker discovery in IIMs and lead to more effective diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This article discusses the potential of machine learning (ML) models to improve the phenotypic stratification of idiopathic inflammatory myopathies (IIMs), with a focus on anti-MDA5-positive dermatomyositis (MDA5-DM). MDA5-DM is a subgroup of dermatomyositis that is commonly associated with comorbidities, particularly rapidly progressing interstitial lung disease (RP-ILD). However, the variability in patient cohort sizes, clinical characteristics, and lack of standardized diagnostic criteria for MDA5-DM pose challenges for ML models. The article emphasizes the need for unbiased and consistent data across studies to achieve rigorous replication of ML models and highlights the importance of integrating scientific insights with the development of practical and easily applicable clinical variables. [Extracted from the article]