Neurodiagnoses: AI-Powered CNS Diagnosis Framework

Neurodiagnoses is an open-source AI diagnostic framework for complex central nervous system (CNS) disorders. It integrates multi-modal biomarkers, neuroimaging, and AI-based annotation to improve precision diagnostics and advance research into the underlying pathophysiology of neurological diseases.

Ecosystem & Integrations

Neurodiagnoses AI operates across three core platforms:

GitHub: Stores all scripts, pipelines, and model training workflows.
EBRAINS: Provides HPC resources, neuroimaging, EEG, and biomarker data for AI training.
Hugging Face: Hosts pre-trained AI models & datasets for easy deployment.

By utilizing these platforms, we ensure seamless integration of AI models, datasets, and neuroimaging resources.

AI-Assisted Diagnosis Approaches

Neurodiagnoses offers two complementary AI-powered diagnostic models:

Probabilistic Diagnosis (Differential Diagnosis):
- Generates multiple possible diagnoses, each with an associated probability percentage.
- Useful for differential diagnosis and ranking possible conditions.
- Example Output:
  - 80% Prion Disease
  - 15% Autoimmune Encephalitis
  - 5% Neurodegenerative Disorder
Tridimensional Diagnosis (Structured):
- Provides structured diagnostic outputs based on three axes:
  - Axis 1: Etiology (e.g., genetic, autoimmune, prion, vascular, toxic, inflammatory)
  - Axis 2: Molecular Markers (e.g., CSF biomarkers, PET findings, EEG patterns, MRI signatures)
  - Axis 3: Neuroanatomoclinical Correlations (e.g., regional atrophy, functional impairment, metabolic alterations)
- Enhances precision medicine and biomarker-guided diagnosis.

Research Applications: CNS Computational Modeling

Multi-omics Integration: Combines proteomics, genomics, lipidomics, and transcriptomics data.
Personalized Simulations: Models disease progression tailored to individual patients.
Computational Modeling: Aids in biomarker discovery and therapeutic target identification.

Project Components

Data Processing (EBRAINS):
- Stores and processes raw EEG, MRI, and biomarker data.
- Feature extraction pipelines convert raw data into structured datasets.
- Employs federated learning for secure multi-center AI training.
AI Model Training & Hosting (Hugging Face):
- Fine-tunes pre-trained models using Hugging Face resources.
- Stores model artifacts on the Hugging Face Model Hub.
- Provides access to models via the Hugging Face API.
Codebase & Pipelines (GitHub):
- Hosts all scripts and training workflows.
- Implements continuous integration (CI/CD) for automated model updates and deployment.

Getting Started

To set up the project locally:

# Clone the repository
git clone https://github.com/Fundacion-de-Neurociencias/neurodiagnoses.git
cd neurodiagnoses

# Install dependencies
pip install -r requirements.txt

# Connect to Hugging Face
huggingface-cli login

# Train a Model
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

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