AI-powered Neocam device could enhance newborn eye screening

This innovation aims to further improve the accuracy of diagnosing congenital cataracts – the leading cause of avoidable childhood blindness worldwide – when babies are examined in maternity wards shortly after their birth.

A prototype of the Neocam ophthalmic imaging device is currently being evaluated in a multi-centre clinical trial funded by the National Institute for Health and Care Research (NIHR) as part of the Digital Imaging versus Ophthalmoscopy (DIvO) study. 

This five-year study aims to determine whether Neocam’s digital imaging technology can improve the detection of congenital cataracts compared with the red-reflex test, a standard ophthalmoscope test that uses a bright visible light.

The test can detect congenital cataracts and retinoblastoma by assessing light reflections from the back of the eye.

Challenges can occur in newborns and babies with darker eyes where pigmentation affects how the reflex appears, leading to misdiagnosis or missed cases.

Dr Louise Allen, consultant paediatric ophthalmologist at Addenbrooke’s Hospital, was frustrated by the number of cataracts being missed at screening or misdiagnosed, which led to her to develop Neocam.

Neocam uses infrared (IR) light to provide more consistent, high-quality reflections from the back of the eye for more reliable diagnosis. IR is not affected by eye pigmentation, which reduces false positive in babies from ethnic groups with darker eyes. The device also captures permanent digital images allowing specialists to review cases remotely.

Early findings

The final study outcomes are not due to be reported until 2027, but the team has noted some early positive findings; several babies have been diagnosed with rare but significant visual conditions that were missed by the standard screening tests being done simultaneously.

The new AI feature will enable Neocam to immediately assess the quality of images as they are taken, providing instant feedback to maternity staff on whether a captured image is clear enough for accurate evaluation.  If an image does not meet the quality required, users can retake it. 

Training data

The software engineering team at 42T will use 46,000 de-identified images from the DIvO study to train the machine learning model.  The aim is to integrate the new edge AI algorithms into the first commercially available eye screening units so the device can analyse images using its existing processing capability without added costs, needing any hardware redesign or impacting device performance.

The AI development project is being funded jointly by 42T and with an innovation grant from Addenbrooke’s Charitable Trust (ACT), which helped to fund early development and testing of CatCam, the first prototype device.

In a statement, Dr Allen said: “This novel eye screening technology has been designed to be an affordable, easy-to-use tool to improve the accuracy of diagnosing congenital cataracts in babies. The new added AI feature will build on 42 Technology’s previous design and development work, while ensuring the device is even easier for midwives and GPs to use when it is launched commercially.”