One algorithm efficiently sorts through large numbers of brain images and picks out those that include characteristics of Alzheimer’s. A second machine-learning method identifies important structural features of the brain — an effort that could eventually help scientists to spot new signs of Alzheimer’s in brain scans.
AI techniques have found a wide range of applications in the clinical and biomedical fields, to automate, standardize, and improve the accuracy of early prediction (regression task), classification of patients (classification task), or the stratification of subjects, based on the processing of specific data (see glossary). In a classification task, the algorithm is trained to associate a label (e.g., “AD diagnosis”) to a given set of features (e.g., clinical parameters, cognitive status, genotypes, biochemical markers, imaging, and so on), in order to be able to generate predictions. Once the model is ready, it is able to predict a class, defined by a label (e.g., “AD” or “control”), by analyzing the set of features of a new given example. The regression task instead involves predicting the value of a variable (e.g., “hippocampus volume” or “biomarker level”) measured on a continuous scale.
Despite ample research effort, we still do not have a cure capable of modifying and/or halting the course of the disease. Some clinical trials are ongoing, especially with the use of monoclonal antibodies targeting Aβ peptides, modified Aβ species, and monomeric as well as aggregated oligomers, which have been shown to be safe and have clinical efficacy in AD patients. However, AI pipelines can be applied in automatic compound synthesis to analyze the literature and high-throughput compound screening data, to perform an initial molecular screening and automated chemical synthesis. By updating the AI model after cell- or organoid-based experiments, AI can be used to propose a new molecular optimization plan and new bioassays can be conducted to evaluate the biological effects of the compound, thus enabling an automated drug development cycle based on AI design and high-throughput bioassay, greatly accelerating the development of new drugs. AI technology can be used to repurpose known drugs for treatment of Alzheimer’s disease. This is a fast, low-cost drug development pathway, in which AI is used to predict drug repurposing by analyzing large-scale transcriptomics, molecular structure data, and clinical databases. Finally, AI can be exploited to simplify clinical trials too, both in the design and implementation phase. Participant selection can be optimized by using AI algorithms on genetic and clinical data, thus predicting which subset of the population may be sensitive to new drugs. Notably, coupling AI with data from wearables enables almost real-time non-invasive diagnostics, potentially preventing drop-out at subject level. Although promising and rapidly growing, only a few of these AI applications have made it to the clinical application stage; nonetheless, AI represents a promising technology to support research and, finally, to develop novel effective therapies.
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**Information for this article was researched at the National Library of Medicine