aiDIVA – hybrid AI for rare disease diagnostics using evidence-based, machine learning and language models
TL;DR
aiDIVA is presented, an ensemble-AI combining statistical and machine learning models trained on genomic and phenotypic data to identify causal variants among tens of thousands per patient, and applies a random forest model to classify pathogenicity and generates evidence-based scores for dominant and recessive diseases.
Abstract
Genome sequencing enables accurate detection of genetic variants and is transforming rare disease diagnostics. While data generation is scalable, prioritization and clinical interpretation remain challenging, often requiring expert manual classification. AI-driven decision support systems are therefore needed to assist in causal variant identification or to fully automate large-scale re-analysis of unsolved cases. Existing tools often estimate variant impact on protein function, but few integrate genomic, phenotypic, and clinical annotation data for diagnosis. We present aiDIVA, an ensemble-AI combining statistical and machine learning models trained on genomic and phenotypic data to identify causal variants among tens of thousands per patient. aiDIVA applies a random forest model to classify pathogenicity and generates evidence-based scores for dominant and recessive diseases. These predictions are integrated with clinical metadata to prioritize the most likely causal variants. Large language models further refine and explain results. The aiDIVA-meta model consolidates all scores into a ranked list. aiDIVA-meta reported the causal variant among the top-3 candidates in 97.4% of a pre-training collected cohort with prior evidence in ClinVar or HGMD, and in 93.3% of a post-training collected cohort of previously unreported variants.
