New e-health challenges: the potential of Big Data and AI for neurodegenerative diseases

The benefits of using innovative technologies in neurodegenerative diseases: early diagnosis, more accurate remote monitoring, more effective disease management, improved quality of life for patients and reduced burden on healthcare systems.

One of the most important challenges for the near future in the field of innovative e-health solutions is certainly the prevention, treatment and control of neurodegenerative diseases. 

Dementia is the term generally used to refer to a series of mostly progressive pathologies that affect the memory and cognitive abilities of sufferers, effectively altering the normal course of daily lives. Alzheimer’s disease is the most common type (60-70% of total cases).

The need to experiment with new approaches is driven by the daunting forecasts on the increase of the elderly population, especially in developed countries like ours. This translates into a truly critical scenario for healthcare systems: an individual with dementia costs the healthcare system three times more than an individual of the same age without the disease. 

According to data from the World Health Organisation (WHO), there are approximately 55 million people suffering from dementia, but this is estimated to increase to 75 million in 2030 and again to 132 million in 2050.

How can new technologies mitigate the impact of these diseases on the quality of life of citizens and improve the level of services in the healthcare system?

The world of healthcare has often been slow to embrace technological innovations, but progress has been made in recent years, especially with the implementation of electronic health records.

The complete switch to digital will allow the generation of a huge amount of health data, a very large and valuable source of information resources for the world of scientific research. The future of healthcare operations will therefore be shaped by the development of innovative and interoperable technologies and the possibility of processing and generating new knowledge from large masses of heterogeneous data.

Improving diagnostic accuracy by harnessing the predictive power of AI and the knowledge base of big data is one of the crucial goals for all diseases, but it is even more important for neurodegenerative ones: studying the preclinical conditions of dementia to make an ‘early diagnosis‘ may in fact be decisive in mitigating or slowing down the progressive degeneration of this disease.

By increasing the ability to detect the initial signs of the disease early, it is possible to reduce the impact of dementia by improving the effectiveness of therapeutic interventions precisely because they are applied at an early stage.

Using Big Data technologies and artificial intelligence techniques, work is being done to build CDS (Clinical Decision Systems) capable of systematising and properly exploiting all the clinical-anamnestic and instrumental information, both at the level of the individual patient and for the entire patient population.

Once integrated, clinical, omics, imaging data and data from wearable sensors can be processed using machine learning and deep learning techniques to identify new causal variables of cognitive decline at an early stage and make predictions on the possible evolution of the disease.

The aim is to foster the generation of useful strategies for implementation in clinical practice, introducing new models for early diagnosis and treatment evaluation through follow-ups, and at systemic level, improving the quality of treatment and management capacities also in terms of screening, thereby also reducing costs.

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