Indolent systemic mastocytosis (ISM) presents diagnostic challenges that require differentiation from advanced SM variants to guide appropriate therapeutic intervention. World Health Organization classification relies on complex B- and C-findings; the multifaceted and varied nature of symptom presentation across organ systems leads to challenges in clinical recognition.1,2 Recent advances in machine learning technologies offer potential solutions to enhance diagnostic precision and support clinical decision-making.2
An experimental machine learning predictive model utilizing random forest algorithms has demonstrated the capacity to distinguish advanced SM from indolent SM with high accuracy based on readily available peripheral blood parameters.3,4 This computational approach analyzes objective laboratory values such as age, alkaline phosphatase, tryptase levels, total bilirubin, albumin, absolute monocyte count, and absolute lymphocyte count.4 The early exploration of this model highlights a recognized clinical challenge in distinguishing SM subtypes, potentially resulting in inappropriate treatment selection.
Emerging natural language processing (NLP) approaches offer the potential to further extend artificial intelligence applications in SM by enabling automated extraction of disease-related symptoms from unstructured electronic health record narratives.2 In a large real-world dataset, a rule-based NLP algorithm demonstrated high accuracy in identifying SM-associated multisystem symptom patterns, highlighting the potential for earlier recognition and more comprehensive phenotyping of indolent disease beyond structured laboratory data.2
The diagnostic complexity of SM stems from pathologists identifying the presence of mastocytosis while clinicians must subsequently categorize disease severity using multifaceted clinical criteria. Machine learning frameworks offer standardization of this classification process through quantitative analysis of routinely collected biomarkers, potentially reducing subjective interpretation, and supporting consistent diagnostic categorization across practice settings.2,4 By enabling early and consistent stratification of disease severity, these approaches may support earlier therapeutic decision-making and reduce delays in initiating appropriate symptom-directed or disease-modifying interventions.
While these artificial intelligence applications demonstrate emerging promise in research settings, practical integration into routine clinical workflows remains in developmental phases. Validation across diverse patient populations, assessment of generalizability beyond clinical trial cohorts, and determination of optimal implementation strategies represent ongoing areas of investigation.
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