FDNA

FDNA

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Private Company

Total funding raised: $33M

Overview

FDNA has established itself as a global leader in AI-powered phenotypic analysis for rare and genetic diseases. Its core technology, centered on facial gestalt analysis and multi-modal algorithms, assists clinicians in flagging potential disorders, thereby aiming to shorten diagnostic odysseys and improve patient pathways. The company operates a scalable platform model, generating real-world data and facilitating connections across the healthcare ecosystem, from early detection by parents to therapy development by pharma. With widespread clinical adoption and a growing database, FDNA is positioned at the intersection of digital health, genomics, and AI-driven diagnostics.

Developmental DisordersGenetic DisordersRare Diseases

Technology Platform

Multi-modal AI framework integrating image analysis (facial gestalt), natural language processing (NLP) of clinical notes, video/voice analysis, and structured symptom assessments to map phenotypic data to potential genetic syndromes and support clinical decision-making.

Funding History

2
Total raised:$33M
Series B$25M
Series A$8M

Opportunities

The massive unmet need in rare disease diagnosis (long diagnostic odysseys) creates a large addressable market for tools that expedite identification.
The growing rare disease pharmaceutical R&D sector provides a lucrative B2B opportunity for patient matching and real-world data services.
Expansion into adjacent areas like developmental pediatrics and neurology could broaden the platform's utility.

Risk Factors

Regulatory evolution for AI-based clinical software poses compliance uncertainty.
Data privacy and security for sensitive health and facial image data is a critical, perpetual risk.
Algorithmic bias if training data lacks diversity could reduce accuracy and equity, damaging trust and adoption.

Competitive Landscape

FDNA is a recognized first-mover in AI-based facial phenotyping. Potential competitors include other AI startups focusing on medical image analysis, large EHR companies integrating basic decision support, and genomic testing companies developing their own phenotypic correlation tools. Its deep, specialized database and clinical community adoption create significant barriers to entry.