Nigerian Student Invents Procedure That Could Revolutionise Retinal Disease Diagnosis

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Vincent Obia

A Nigerian doctoral student at Brunel University London’s Department of Computer Science, Bashir Dodo, has won the “Best Student Paper” award at the industry-leading BIOIMAGING 2018 conference in Portugal.

A statement yesterday by Senior Media Relations Officer at Brunel University, London, Tim Pilgrim, said Dodo won the prize for discovering a new technique for identifying and diagnosing damage to the human retina.

He demonstrated a new algorithm for OCT (Optical Coherence Tomography) equipment, which can automatically segment images of the retina into distinct layers.
It is hoped that the new procedure, which can separate the retina into seven distinct layers, would improve the accuracy and speed of diagnosis, and help save the sight of patients by identifying damage early.

Inspired by the psychological concept of similarity, Dodo used the ideas of continuity and discontinuity to develop an OCT algorithm that can identify where one layer of the retina transitions to the next.

OCT machines are used by ophthalmologists to produce non-invasive three-dimensional photographs of a patient’s retina, which are used in the diagnosis of eye-related disease.
Dodo said, “Layer segmentation is one of the early processes of OCT retina image analysis, and already plays an important role in clinics.

“For example, the thickness profile of the Retinal Nerve Fibre Layer – which can be calculated directly from the segment layer – is used in the diagnosis of glaucoma, which is one of the most common causes of sight-loss worldwide.

“Automatically segmenting the layers could provide critical information for abnormality detection by comparing them to the average population, and monitoring the progress of disease against previous scans.”

While doctors are currently able to identify the layers manually from OCT images, Dodo’s new technique automatically segments images of the retina, allowing specialists to spot abnormalities quicker and better track the progress of medication.

“It is evident that prior knowledge plays an important role in diagnosis,” said Dodo. “Therefore, using automated methods to look back through medical records or ophthalmology literature has great potential to influence how this field progresses.”