About this Research Topic
Artificial Intelligence (AI) has gained a critical role in clinical practice and biomedical research. The use of Machine Learning (ML), a subset of AI, has increased exponentially in the last five years and it will continue to grow in the near future, helping clinicians, researchers, and, most of all, patients. ML has been broadly applied in investigating a wide range of pathological conditions, including rare and autoimmune diseases, as Systemic Lupus Erythematosus (SLE).
ML algorithms have been tested in the diagnostic process of SLE, in predicting response to therapy, risk of flare, severity of the diseases and long-term complications. ML has been used also to address challenges of specific and severe organ-involvements in SLE, such as Lupus Nephritis. Patients affected by Lupus Nephritis still suffer from high risk of morbidity and mortality, despite significant advances in diagnosis, classification and treatment of Lupus Nephritis, and the development of Chronic Kidney Disease (CKD) represents a heavy burden of disease for affected individuals, their families and Healthcare systems.
ML has the potential to become a vital tool in future clinical practice to early diagnose SLE and to discriminate clusters of patients who could benefit from different therapeutic approaches. The use of ML may also represent a turning point in understanding the complex mechanisms underlying the pathogenesis of this multifaceted condition, it can be applied in biomarker discovery and developing innovative target therapies. ML may change the future of SLE.
This Research Topic aims at deepening our knowledge on application of ML in SLE and Lupus Nephritis. We want to explore the use of ML in elucidating pathogenetic mechanisms and role of genetic mutations in these conditions. Moreover, we want to investigate the potential of ML in developing diagnostic and prognostic tools and in identifying new biomarkers for SLE and LN, and to gain insight in therapeutic perspectives.
In this Research Topic, we want to collect a variety of contributions, including, but not limited to original research articles, brief reports and reviews on the use of ML in SLE and Lupus Nephritis.
ML algorithms have been tested in the diagnostic process of SLE, in predicting response to therapy, risk of flare, severity of the diseases and long-term complications. ML has been used also to address challenges of specific and severe organ-involvements in SLE, such as Lupus Nephritis. Patients affected by Lupus Nephritis still suffer from high risk of morbidity and mortality, despite significant advances in diagnosis, classification and treatment of Lupus Nephritis, and the development of Chronic Kidney Disease (CKD) represents a heavy burden of disease for affected individuals, their families and Healthcare systems.
ML has the potential to become a vital tool in future clinical practice to early diagnose SLE and to discriminate clusters of patients who could benefit from different therapeutic approaches. The use of ML may also represent a turning point in understanding the complex mechanisms underlying the pathogenesis of this multifaceted condition, it can be applied in biomarker discovery and developing innovative target therapies. ML may change the future of SLE.
This Research Topic aims at deepening our knowledge on application of ML in SLE and Lupus Nephritis. We want to explore the use of ML in elucidating pathogenetic mechanisms and role of genetic mutations in these conditions. Moreover, we want to investigate the potential of ML in developing diagnostic and prognostic tools and in identifying new biomarkers for SLE and LN, and to gain insight in therapeutic perspectives.
In this Research Topic, we want to collect a variety of contributions, including, but not limited to original research articles, brief reports and reviews on the use of ML in SLE and Lupus Nephritis.
Keywords: Machine Learning, Artificial Intelligence, Systemic Lupus Erythematosus, Lupus Nephritis, Diagnostic algorithm, Prognostic algorithm
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