About the Institution

The IRCCS Burlo Garofolo is a Scientific Institute for Research, Hospitalization and Healthcare focused on the entire spectrum of paediatrics specialties and recognized as a national referral center for autoimmune-immune-mediated disorders and rare diseases, prenatal diagnosis and registries of inherited diseases.

The laboratory of Medical Genetics provides a comprehensive genetics service, offering diagnostic tests to identify complex genetic variations associated with Mendelian disorders and professional genetic counseling through highly specialized clinical geneticists.

The laboratory is a national reference center and uses the state of art technologies for DNA analysis in pathologies such as Cystic Fibrosis, Thalassemia, Hereditary Thrombophilia, Hemochromatosis, Gilbert’s Syndrome, Y chromosome infertility caused by microdeletions, Hereditary Deafness, Usher Syndrome, Intellectual disabilities and Panhypopituitarism.

The laboratory pairs state-of-the-art DNA sequencing technologies with specialized clinical geneticists who offer professional genetic counseling.

To efficiently analyze complex genetic variations across these diverse conditions, the laboratory integrated eVai, the AI-driven variant interpretation platform developed by enGenome, fundamentally transforming their clinical workflow.

Real-World Use Cases: AI-Driven Precision in Practice

The practical impact of eVai’s workflow transformation is best demonstrated through three distinct clinical scenarios handled by Prof. Girotto’s team, spanning different inheritance patterns and phenotypic complexities:

Use Case 1: Proactive Management in Usher Syndrome 

“We examined two kids from Family R (with autosomal recessive inheritance) who demonstrated a rapidly progressive sensorineural hearing loss (moderate-to-severe) with no other symptoms or clinical signs. After targeted re-sequencing with a panel of hearing loss genes, we filtered the list of variants using eVai software. Thanks to eVai’s automated genomic variant prioritization, we were able to consider a stop-gain mutation in USH2A gene (highlighted variant by eVai) as a cause of pathology. The result was extremely interesting in terms of planning an effective and preventive strategy for the patients. In particular, since both patients, most likely, will develop retinitis pigmentosa in the next years, they should undergo a frequent ophthalmological evaluation."

Use Case 2: Resolving Suspected Pendred Syndrome

"Afterwards Family M came to our attention for suspected Pendred syndrome and autosomal recessive inheritance. The radiology imaging displayed Enlarged Vestibular Aqueduct (EVA) and Mondini’s dysplasia. The vast majority of patients with these phenotypic characteristics show mutation in SLC26A4 gene. In the next step of the analysis we conducted targeted re-sequencing using a panel of hearing loss genes. Eventually, eVai correctly classified the SLC26A4 gene mutant as a pathogenic variant on the top of the list of prioritized variants."

Use Case 3: Untangling Dominant Traits via Segregation 

"Furthermore we studied the Patient A suspected to have an autosomal dominant form of a progressive sensorineural hearing loss. Initially, the routine analysis highlighted two candidate genes as a probable cause of the disease. However, eVai immediately prioritized the right gene (TECTA) which was consistent with a segregation analysis in the family (three generation family)."

In Their Own Words

"We currently use eVai because of its high accuracy. In the majority of cases analyzed, eVai successfully indicates the pathogenic variant among the high-priority variants on the top of the list. The software significantly reduces the time spent on the analysis of thousands of variants in a very intuitive way.” - Prof. Giorgia Girotto, Laboratory of Medical Genetics, IRCCS Burlo Garofolo

Key Takeaways

• Drastic Time Savings: eVai’s automated variant prioritization reduced the time spent reviewing thousands of variants to a fraction of what manual analysis required, maximizing lab productivity.
• Actionable Clinical Insights: The AI-driven classification surfaced critical findings that directly informed long-term patient management decisions, allowing clinicians to anticipate future symptoms (like retinitis pigmentosa) well beyond the initial diagnosis.
• Versatile & Reliable Architecture: eVai supported consistent, accurate interpretation across diverse cases with distinct inheritance patterns (autosomal dominant and recessive) and varying levels of clinical complexity.