Artificial Intelligence and Machine Learning Applications in Fibromuscular Dysplasia: Transforming Diagnosis, Risk...

Area of Science:

• Vascular Medicine
• Medical Imaging
• Artificial Intelligence

Background:

• Fibromuscular dysplasia (FMD) is a complex vascular condition requiring expert diagnosis.
• Current diagnostic and management approaches for FMD rely heavily on imaging and clinical acumen.
• The heterogeneous nature of FMD presents challenges in consistent care delivery.

Purpose of the Study:

• To review the transformative impact of AI and ML on the diagnosis and management of FMD.
• To explore AI-driven advancements in FMD imaging analysis and risk stratification.
• To discuss the potential of AI in personalizing FMD treatment and predicting outcomes.

Main Methods:

• Review of current literature on AI and ML applications in FMD.
• Analysis of AI-enhanced imaging techniques, including convolutional neural networks.
• Examination of ML models for risk stratification and complication prediction using integrated data.
• Discussion of AI-driven clinical decision support systems and personalized medicine.

Main Results:

• AI significantly enhances the detection of characteristic FMD imaging patterns (e.g., "string-of-beads").
• ML models show promise in stratifying FMD patient risk and predicting disease progression and complications like aneurysms and stroke.
• AI facilitates personalized treatment selection and supports interventions through pharmacogenomics and robotics.

Conclusions:

• AI and ML are pivotal in advancing FMD diagnosis, risk assessment, and personalized management.
• Addressing challenges such as data scarcity, bias, and integration is crucial for widespread AI adoption in FMD care.
• Future developments in explainable AI and digital health integration will drive predictive, patient-centered FMD management.