Challenge G

„Scaling the Cure for Neurological Development Disorders“

Proposed by Thomas Weidauer

Background

Neurological development disorders such as ADHD (attention deficit hyperactive disorder), ASS (autism spectrum syndrome), depression, tics, learning disorders etc., are one of the most under-recognised and severe pandemics in the world.

Studies such as the one published by the WHO (1) show that up to 1/3 of the global population is at least partly affected.

https://www.who.int/news/item/14-03-2024-over-1-in-3-people-affected-by-neurological-conditions--the-leading-cause-of-illness-and-disability-worldwide

I.e. Persons with ADHD have even measurable shorter lifespans.

ADHD Statistics
Shared genetic architecture between attention-deficit/hyperactivity disorder and lifespan - Neuropsychopharmacology

Problem

In Germany, there are over 500,000 children with an ADHD diagnosis, in addition to other neurological disorders such as Asperger's syndrome, Tourette's, tics, learning disorders, etc. To date, in classic medicine, ADHD is not considered to be curable.

Those affected only have the choice between years of behavioural therapies or medication (often methylphenidate-based), which can temporarily alleviate the symptoms but do not target the root cause.

Currently, multimodal therapies with medication and behavioral therapy, etc., are recommended, but again, these only address the symptoms and do not deal with the root cause.

In addition to enormous school problems and difficulties in social behaviour, the everyday family life of affected families is often extremely stressful, heavily burdened and characterised by high levels of suffering.

Actual Challenge

In the US, a medical-free method has been developed that uses a neuroplasticity-based approach to mature underdeveloped and under-integrated brain regions.

This method is based on medical-free sensory and movement training that has been proven to alleviate the symptoms of inadequate brain development. The training is usually conducted in 1:1 sessions between specialised neuro therapists and the patient and is thus not easily scalable or available for patients far away from these therapists.

Training successes and improvements can be medically measured (with the help of recognised test procedures) and have a lasting positive effect on the children's everyday lives.

https://www.mentalhealthjournal.org/articles/a-retrospective-review-of-parent-reported-anxiety-and-emotional-functioning-in-children-with-developmental-challenges-after-participation-in-the-brain-balance-program.html

As screen time for children is considered to have a negative impact on normal brain development, the challenge is to develop a scalable computer-based version for this training, even if most stimuli for the brain have to be analogue.

https://onlinelibrary.wiley.com/doi/abs/10.1111/apa.14176

https://jamanetwork.com/journals/jamapediatrics/article-abstract/2722666

2024-05-21T13:10:12+02:00

Background
Arjan, a 58-year-old teacher, has no symptoms but worries about his risk of prostate cancer due to his father’s history with the disease. His doctor recommends screening, but Arjan is unsure if he really needs it. Meanwhile, Blerim, a 65-year-old retired mechanic, was diagnosed at a late stage, despite having regular check-ups. Advances in AI and data science offer the potential to change these stories—by identifying high-risk individuals earlier and ensuring timely interventions.

Problem
Despite medical advancements, many men still face late diagnoses due to limited screening accessibility, resource constraints, and gaps in existing risk prediction models. Some models lack accuracy across diverse populations, while others fail to integrate smoothly into clinical workflows, making them less effective in real-world settings. A more inclusive, data-driven solution could help bridge these gaps and improve early detection efforts.

The Challenge
How can AI-driven risk prediction improve prostate cancer detection and prevention? Your task is to develop a robust and inclusive machine-learning model that analyzes patient data—such as genetic profiles, lifestyle habits, and clinical records—to identify individuals at risk. The model should prioritize accuracy, fairness, and usability while ensuring patient privacy. Can your solution seamlessly integrate into clinical settings, helping doctors make better-informed decisions? Design a tool that brings early detection within reach for more people, improving health outcomes one step at a time.