Automatic Detection of Genetic Diseases in Pediatric Age Using Pupillometry

Abstract: Inherited retinal diseases in children can lead to blindness and diagnosing them is difficult due to the many possible causes. Current diagnostic methods are complex and sometimes invasive, making them unsuitable for young children. This research introduces a new system to help diagnose these diseases using a technique called Chromatic Pupillometry, which measures how the pupil reacts to different colours of light. The new system combines a special pupillometer device with a computer program that uses machine learning. The program analyses the pupillometry data and helps doctors determine if a child has an inherited retinal disease. Specifically, they tested the system on Retinitis Pigmentosa, a type of inherited retinal disease. The results were promising, showing good accuracy, sensitivity (correctly identifying those with the disease), and specificity (correctly identifying those without the disease). This is the first time machine learning has been used with pupillometry to diagnose a genetic disease in children.

Keywords: Machine Learning, Clinical decision support system, Python, Pupillometry, Retinopathy, Support Vector Machine, ELM (Ensemble Extreme Learning Machine), pigmentosa.