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Introducing project DAIsy

Developing AI ecosystems improving diagnosis and care of mental diseases.

Harnessing the new era in advanced AI to help solve mental disorders.

The burden of mental diseases, including Major Depressive Disorder (MDD) and Eating Disorders (ED), is huge and continues to increase with large impacts and major social and economic consequences worldwide.
In DAIsy we have brought together a strong consortium including care givers, academic medical researchers, device industry, data specialists, and multiple AI experts to collectively work on a number of societal and economical urgent use cases.

Advanced models

Advancing state-of-the art AI models to create breakthrough algorithms supporting diagnosis and treatment.


AI to support complex multifactorial data collection, integration and analyses.


AI to support treatment selections and success assessment from scattered data.

Our approach

Integrated value chains

With DAIsy’s integrated approach, including the full functional and technical values chains, we foresee an improvement in diagnosis accuracy and treatment selection with an apparent modest 5 percent, however, this initial 5 percent improvement has huge societal, economic and personal effects, which allows an important role of the DAIsy consortium partners in this emerging market.

Project rationale

Our use cases


The Dutch use case focuses on two main mental diseases and their interplay: Major Depressive Disorder (MDD) and Eating Disorder (ED). For both diseases, DAISY will develop, research, and validate AI-based tooling for the diagnosis, treatment decision support, and prognosis by the analyses of multiple sources of data assessing the status of patients.

The aim of the Swedish use case is to develop an AI service for the time-consuming task of collecting and synthesizing diagnostic information to enable clinicians to focus on other tasks, e.g.,treatment. Compared to routine clinical practice within CAP services, the AI service will provide enhanced diagnostic accuracy and thus lead to both faster and better informed decisions on treatment.

The first Turkish use case focuses on Major Depressive Disorders such as melancholia, and catatonic, seasonal, and psychotic depression. Approximately 322 million people have depression. These depression rates have continued to increase over time, especially in low-to-middle income nations. This makes it one of the leading causes of disability around the world, along with anxiety.

Depression ranks 4th among all medical diseases in terms of disability due to its high prevalence, chronicity, job and social losses it causes, and negativities in current activities. It is also a disease with high mortality due to the high risk of suicide. It is not easy to identify the subtypes of depression and predict the response to treatment. This poses a significant clinical challenge, as treatments vary significantly depending on the initial diagnosis. Incorrect and late diagnosis increase mortality and morbidity rates as it brings with it wrong and delayed interventions.

Neurofeedback is a computer-supported therapy procedure for clinical use, in which selected parameters of the patient’s own brain activity are made perceptible. For this purpose, brain activity is measured using e.g. Electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS) or functional magnetic resonance imaging (fMRI) in real time on the surface of the head (neuro), which influences the audio, visual and/or tactile feedback animation.

For a light and continuous support during day-to-day life, the project aims to design and deploy a virtual therapy assistant that supports patients in addition to other means of therapy. The agent takes the role of a daily companion who collects and provides behavioral data such as social interactions, contextual parameters or in-situ questionnaires to enable self-reflection about behavioral patterns or the influence of others on situations that can increase the chance of a depressed phase.

Major Depressive Disorder is a psychiatric disorder that affects 10% of the population worldwide. Artificial Intelligence based tools arise as one of the valuable new developments on MDD treatment and monitoring. The use of auto-reported inputs related to depressive symptoms gathered by daily questionnaires filled in by the patient on a mobile app has the potential to help to better manage this chronic and serious disorder. Another area of interest where AI can play an important role, lies in social media monitoring of the patients that suffer from MDD.

Gülçin OZANIRK, Senior Project Engineer, ARD Grup
Dutch coordinator: B. Stalknecht SemLab
Overall Coordinator: Arda Ödemiş ARD GROUP, Türkiye
Portugese Coordinator: ISEP
Our team

The foundation of our project

Our exceptional consortium is the heartbeat of our project, and the driving force behind our success. As a diverse group of best of breed organizations, we understand that true innovation stems from collaboration, creativity, and expertise.

Worldwide, over 280M people suffer from MDD, leading to suicide in 700k people and societal cost of €1 trillion each year. Using AI, we foresee an improvement in diagnostic accuracy and treatment selection with 30%, and a speed up of treatment initiation of 60%.

prof dr Henk Marquering
Translational AI at Amsterdam University Medical Centers
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