The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review

Abstract

Background: Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes. Methods: Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context. Results: Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response. Conclusion: Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.

Artificial intelligence in rare disease diagnosis and treatment

“…Detecting and aptly reading the diagnostic image is a necessity that is increasingly leading to the recognition of ML in AI for RDs 1 . ML, for example, has recently been utilized to identify which patients with systemic sclerosis are at a high risk of severe complications, early detection of organ involvement, and more 3 …”

“…ML is able to detect pulmonary involvement better than high‐resolution computerized tomography (HRCT) and pulmonary function tests (PFTs), which currently serve as the standard diagnostic methods. Although HRCTs tend to be used when PFTs indicate a decline in respiratory function, ML algorithms may detect pulmonary involvement prior to the onset of deterioration resulting in improved survival rates and reduced health costs 3 . Deep learning (DL) represents a subset of ML algorithms involved in image recognition.…”

“…By leveraging these technologies, healthcare providers can diagnose and treat patients more accurately and efficiently. Additionally, AI and ML can help physicians optimize and accelerate the timeline for the diagnosis, treatment, and management of diseases 3 . As these technologies continue to evolve, more innovative solutions will emerge that will hold the capability of revolutionizing and improving healthcare for all.…”

“…Additionally, AI and ML can help physicians optimize and accelerate the timeline for the diagnosis, treatment, and management of diseases. 3 As these technologies continue to evolve, more innovative solutions will emerge that will hold the capability of revolutionizing and improving healthcare for all. Figure 1 provides an overview of some emerging types of AI.…”

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

“…A narrative review of ML in RMDs for clinicians and researchers was published by Nelson AE et al [43]. Furthermore, another narrative review [44] covered the applications of ML in systemic sclerosis (SS). A review of AI and deep learning (DL) [45] attempted to highlight the relevance of these techniques in the near future of the field of rheumatology.…”