Machine-learning system can identify more than 50 different eye diseases and could speed up diagnosis and treatment A new machine-learning system is as good as the best human experts at detecting eye problems and referring patients for treatment, say scientists. The groundbreaking artificial intelligence system, developed by the AI-outfit DeepMind with Moorfields eye hospital NHS foundation trust and University College London, was capable of correctly referring patients with more than 50 different eye diseases for further treatment with 94% accuracy, matching or beating world-leading eye specialists.

“The results of this pioneering research with DeepMind are very exciting and demonstrate the potential sight-saving impact AI could have for patients,” said Prof Sir Peng Tee Khaw, the director of the NIHR Biomedical Research Centre at Moorfields eye hospital and the UCL Institute of Ophthalmology. The two-stage AI system takes a more human-like and intelligible approach to analysing the highly complex optical coherence tomography (OCT) scans of patient retinas.

These are commonly used to triage patients with sight problems into four clinical categories: urgent, semi-urgent, routine and observation only. Five separate machine-learning systems, trained using 877 clinical OCT scans, first create maps of the OCT scans.

The five maps are then analysed by a second series of five machine-learning systems, trained on maps created from 14,884 OCT scans from 7,621 patients, which interpret the maps and each give a referral decision. The referral decisions are combined into one result, with a confidence rating expressed as a percentage.

The maps and any differing or ambiguous results can be shown visually to a clinician for their own interpretation and explanation of the referral result. Most other AI-based systems essentially appear as a black box; data is fed in one end and the result is outputted from the other, with no way to check how the system came to its decision. Read more from theguardian.com…

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