Here are all the challenges you can work on with your team during the hackathon. Regardless of your location, you can participate in any international challenge.


Brain development premature babies

The Neonatal Intensive Care Unit (NICU) department is exploring the possibility of using specific patient-related variables and daily measurements to predict the development of post-hemorrhagic ventricle dilation in premature infants. The objective is to enhance predictive accuracy, reduce unnecessary scans, and minimize the need for invasive procedures.

Predicting troubleshooting for medical devices

When a medical device malfunctions, swift clinical readiness is vital. Technicians possess varying expertise levels, necessitating support. Data is logged and protocol-driven, with a desire to provide predictive feedback to technicians and identify non-technical issues.

Availability of clinically medical devices

To ensure uninterrupted care, medical devices receive preventive maintenance to prevent sudden failures and patient risks, complying with legal requirements. Location technology optimizes processes and enhances patient safety.


AI for ECG-Based Cardiac Assessment

In the medical field, echocardiograms are crucial for heart assessment, but they’re resource-intensive. The challenge at this hackathon is to create AI models that predict echocardiogram parameters from ECG data, enhancing cardiac care accessibility and quality. Your innovation can save lives and global healthcare.

Determine the biological age with ECG data

Your goal is to determine age from biological signals, requiring unique solutions. Steps include data inspection, preparation, AI model creation, testing, and results presentation.

Predictive Modeling for Menstrual Cycle

Empowering women and promoting gender equality is paramount. In this hackathon, create a predictive model for menstrual cycle intervals, aiding women in health management, family planning, and career choices. Drive innovation for women’s well-being.

Specific Biomarkers for Emotional Disorders

Harness AI for emotional disorder biomarker discovery in EEG data. Analyze patterns to enhance early diagnosis and treatment. Contribute to mental health progress through innovative EEG diagnostics.

Developing an Evidenced Counseling Chatbot with LLMs

Participants aim to create an Evidenced Counseling Chatbot that provides empathetic, well-referenced responses.

Detecting Depression through Eye Movements offers an innovative platform, combining eye-tracking and questionnaires to assess depression levels, enabling proactive mental health interventions and predictive modeling.



Welcome to the “SeeMHeart” challenge, where we invite you to create a machine learning tool that can classify cardiac MR (CMR) sequences. This challenge is a unique opportunity to merge the worlds of medicine and artificial intelligence, improving the diagnosis and treatment of heart diseases.


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