electricsheepafrica/african-medical-multimodal-fracture

African Medical Multimodal Bone Fracture Dataset

Dataset Description

This dataset is a comprehensive, multimodal bone break classification dataset specifically designed for African healthcare contexts. It addresses critical gaps in medical AI for resource-constrained environments while ensuring cultural sensitivity and local relevance.

Dataset Summary

• Total Records: 1,129 multimodal medical cases
• Original Images: 1,128 X-ray images (89% of dataset)
• Augmented Images: 135 mildly augmented images (11% of dataset)
• Fracture Types: 10 different bone fracture classifications
• Countries Represented: 18 African countries
• Languages: 11 African languages + English
• Modalities: 6 data types per record

Key Features

🌍 African Context Integration

• Geographic diversity across 18 African countries
• Rural (59%), Urban (26%), Peri-urban (15%) distribution
• Cultural factors including traditional medicine usage
• Socioeconomic indicators relevant to healthcare access

🗣️ Multilingual Clinical Data

• Patient histories in 11 languages: English, French, Arabic, Swahili, Hausa, Yoruba, Amharic, Wolof, Twi, Zulu, Afrikaans
• Cultural pain expressions and symptom descriptions
• Traditional medicine integration patterns

🏥 Realistic Equipment Simulation

• 89% high-quality images (representing modern African hospitals)
• 11% mildly augmented images (rural/portable equipment variations)
• Quality scores: 6.2-9.5/10 (realistic for African healthcare)

📱 Mobile Health Integration

• Smartphone usage patterns and digital literacy levels
• GPS tracking and movement data
• SMS/voice follow-up preferences

Usage Examples

Loading the Dataset

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("electricsheepafrica/african-medical-multimodal-fracture")

# Access splits
train_data = dataset["train"]
val_data = dataset["validation"]
test_data = dataset["test"]

# View a sample
sample = train_data[0]
print(f"Patient from {sample['country']} with {sample['fracture_type']}")
print(f"Complaint: {sample['chief_complaint']} ({sample['complaint_language']})")
print(f"Vitals: HR {sample['heart_rate']}, BP {sample['blood_pressure_systolic']}/{sample['blood_pressure_diastolic']}")

Accessing Images

from PIL import Image
import requests

# Get image path and load
image_path = sample['image_path']
# Images are stored as relative paths in the dataset

Fracture Types

The dataset includes 10 fracture classifications:

Fracture Type	Description
Fracture Dislocation	Joint displacement with bone break
Comminuted fracture	Multiple bone fragments
Pathological fracture	Break due to disease/weakness
Avulsion fracture	Bone fragment pulled by tendon/ligament
Greenstick fracture	Incomplete break (common in children)
Hairline Fracture	Thin crack in bone
Spiral Fracture	Twisting break pattern
Oblique fracture	Diagonal break across bone
Impacted fracture	Bone ends driven together
Longitudinal fracture	Break along bone length

Geographic Distribution

Countries Represented

Nigeria, Kenya, Ghana, South Africa, Tanzania, Uganda, Ethiopia, Morocco, Egypt, Senegal, Rwanda, Botswana, Zambia, Zimbabwe, Mali, Burkina Faso, Cameroon, Ivory Coast

Regional Distribution

• Rural: 665 cases (59%) - Reflects African population distribution
• Urban: 293 cases (26%) - Major cities and towns
• Peri-urban: 171 cases (15%) - Transitional areas

Clinical Measurements

Each record includes realistic clinical measurements:

• Heart Rate: 60-100 bpm (normal range)
• Blood Pressure: Systolic 90-140 mmHg, Diastolic 60-90 mmHg
• Temperature: 36.0-38.5°C
• Height: 150-190 cm
• Weight: 45-90 kg
• Pain Scale: 1-10 (patient-reported pain level)

Ethical Considerations

Synthetic Data Approach

• All patient data is synthetically generated to protect privacy
• No real patient information is included
• Cultural patterns are based on research and literature, not individual cases

Cultural Sensitivity

• Traditional medicine practices are represented respectfully
• Cultural pain expressions reflect documented patterns
• Family involvement in medical decisions is appropriately modeled

Applications

Primary Use Cases

• Multimodal Medical AI: Training models that integrate images, text, and structured data
• Cross-cultural Healthcare: Developing culturally-aware diagnostic systems
• Resource-constrained Settings: AI systems for rural and underserved areas
• Medical Education: Training materials for African healthcare contexts

Citation

If you use this dataset in your research, please cite:

@dataset{african_medical_multimodal_2024,
  title={African Medical Multimodal Bone Fracture Dataset},
  author={ElectricSheepAfrica},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/electricsheepafrica/african-medical-multimodal-fracture}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to use, modify, and distribute this dataset for any purpose, including commercial use, as long as you provide appropriate attribution.

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This dataset represents a comprehensive effort to create culturally-aware, multimodal medical AI training data for African healthcare contexts. While synthetic, it is based on extensive research and aims to promote more inclusive and effective medical AI systems.