DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects
arXiv:2606.24779v1 Announce Type: cross Abstract: Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability. In the subset with suspected genetic etiologies, exome and genome sequencing have moved many cases from variant detection to post-sequencing interpretation: clinicians must rank patient-specific candidate variants under incomplete fetal or infant phenotypes and heterogeneous evidence from population genetics, variant-effect prediction, gene-disease validity, phenotype ontologies, cellular and pathway context, protein structure and clinical literature. We
The continuous advancements in AI and genomics are converging, making sophisticated variant prioritization for complex genetic conditions increasingly feasible at this moment.
This development signifies a leap in genetic diagnosis capabilities, potentially transforming early diagnosis and intervention for birth defects and impacting healthcare economics and patient outcomes.
The ability to accurately rank patient-specific genetic variants using AI will streamline diagnosis processes, reduce diagnostic odysseys, and enable more targeted clinical decisions.
- · Genomic sequencing companies
- · Pediatric healthcare providers
- · AI-driven diagnostic platforms
- · Patients with genetic birth defects
- · Traditional manual variant interpretation services
- · Clinical workflows heavily reliant on human expert consensus without AI tools
Faster and more accurate diagnosis of genetic birth defects will become standard clinical practice.
The demand for comprehensive genetic sequencing for newborns and fetuses will increase, driving down costs and improving accessibility.
This could lead to a 'precision pediatric medicine' paradigm, where early genetic insights inform lifelong health management and personalized preventative care.
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Read at arXiv cs.AI