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Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. An Author Correction to this article was published on 01 August It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation.
Mild cognitive impairment MCI is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. The proposed framework integrated longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.
AD is the most common form of dementia with no validated disease modifying treatment. An estimated 5. By , this number is projected to rise to nearly 14 million 1. Current available treatments decelerate only the progression of AD and no treatment developed so far can cure a patient who is already in AD. Thus, it is of fundamental importance for timely treatment and progression delay to develop strategies for detection of AD at early stages before clinical manifestation.
As a result, the concept of mild cognitive impairment MCI was introduced. MCI, a prodromal form of AD, is defined to describe people who have mild symptoms of brain malfunction but can still perform everyday tasks. Patients in the phase of MCI have an increased risk of progressing to dementia 1 , 2 , 3 , 4. Some patients in their MCI stages are converted to AD within a limit of the time window after baseline, while some are not.
