Table 4 Diagnosis of Diabetic Neuropathy- artificial intelligence (AI) meets precision medicine

Precision medicine aims to deploy biomarkers to achieve rapid diagnostic and prognostic capability to enable targeted and more efficacious treatment.

Diabetes affects ~ 20% of the Qatari population causing diabetic neuropathy (DN) in ~ 35% of the cases and remains undiagnosed in ~ 80% of these patients. Researches from WCMQ, QU and HMC have pioneered corneal confocal microscopy (CCM) diagnostic, a non-invasive, ophthalmic imaging technique to identify early subclinical nerve degeneration and regeneration after therapeutic intervention. They used machine learning technology to achieve rapid automated quantification of corneal nerve fibers to clinically classify patients with high sensitivity and specificity.

This work translates the AI outcome into a clinically meaningful outcome for rapid objective diagnosis of early diabetic neuropathy enabling risk factor reduction to prevent progression of DN to foot ulceration and amputation. It also has a wide range of other diagnostic and prognostic applications in neurology as CCM can identify neurodegeneration in several neurodegenerative conditions.