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CASE STUDY

AI-Powered Support for Early Genetic Syndrome Diagnosisn

AI-powered tool that helps clinicians identify rare genetic syndromes earlier. The solution accelerates diagnosis, supports non-specialist teams, and expands access to care worldwide.

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Industry

Healthcare

Deployment

AI-powered clinical support app

Scope

Early triage and diagnostic assistance

Core KPI

Faster identification of rare genetic syndromes

Overview

Rare genetic syndromes are often diagnosed late, since facial features are subtle and vary by age and ethnicity. Shortages of genetic specialists further delay exams, diagnosis, and treatment, especially in underserved regions.

FDNA set out to create an AI-driven solution to assist clinicians in early triage, providing actionable insights to accelerate confirmatory testing and treatment.

The Challenge

  • Rare syndromes are hard to detect early due to subtle, variable facial features.

  • Diagnoses depend on scarce genetic specialists.

  • Long delays in exams and diagnosis slowed access to treatment.

  • Needed a solution usable by hospitals, clinics, and research centers globally.

Our Solution

We built an AI-powered application that supports early triage of rare syndromes:

  • Guided photo capture – standardized patient images via mobile app.

  • AI analysis – computer vision identifies facial landmarks and patterns, producing a ranked list of possible syndromes with confidence scores.

  • Explainability – highlights regions of the face influencing the AI’s suggestion.

  • Clinical workflow integration – simple flow: capture → instant analysis → report with next-step recommendations.

Business Benefits

  • Earlier triage – supports diagnosis even in young children.
  • Decision support – empowers non-specialist teams with standardized analysis.
  • Equitable access – lightweight tool for regions with limited genetic expertise.
  • Continuous learning – each case enriches the system to better capture subtle signals.

How It Works

Photo capture

Clinician takes standardized patient photos.

Computer vision

System detects proportions and facial landmarks.

AI analysis

Trained models suggest possible syndromes with confidence scores.

Report generation

Structured output with highlighted facial regions and recommendations.

Technical Highlights

  • Computer vision to detect and measure facial features.

  • Supervised learning trained with clinically validated cases.

  • Bias calibration by age, ethnicity, and angle for fairer results.

  • Structured reports with regions of attention and patient history.

Key Results

Provides early triage of genetic syndromes for clinicians.

Reduces reliance on scarce genetic specialists.

Expands access to quality diagnostics in underserved areas.

Creates structured, explainable reports supporting medical decision-making.

See It in Action

  • Mobile app for photo capture and triage.
  • AI-powered facial analysis for syndrome detection.
  • Reports with syndrome probabilities and regions of interest.
  • Pilot deployments with hospitals, clinics, and research centers.

Supporting Clinicians in Early Rare Disease Detection

Discover how AI can assist healthcare teams, accelerate diagnosis, and expand access to genetic expertise.

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