Datasets:

electricsheepafrica
/

africa-synth-aid-flows-medical-multimodal-fracture-all

+---
+license: cc-by-4.0
+task_categories:
+- image-classification
+- text-classification
+- multimodal
+language:
+- en
+- fr
+- ar
+- sw
+- ha
+- yo
+- am
+- wo
+- tw
+- zu
+- af
+tags:
+- medical
+- xray
+- fracture
+- bone
+- africa
+- multimodal
+- healthcare
+- radiology
+- synthetic-data
+size_categories:
+- 1K<n<10K
+---
+# African Medical Multimodal Bone Fracture Dataset
+## Dataset Description
+This dataset transforms the PKDarabi bone break classification dataset into a comprehensive, multimodal system 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**: 1,129 X-ray images (88% of dataset)
+- **Augmented Images**: 135 mildly augmented images (12% 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**
+- 88% high-quality images (representing modern African hospitals)
+- 12% 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
+## Dataset Structure
+### Modalities
+Each record contains 6 data modalities:
+1. **Image Data**
+   - Original X-ray images (JPEG format)
+   - Optional mild augmentation for rural equipment simulation
+   - Quality scores and equipment metadata
+2. **Patient Demographics**
+   - Age, gender, ethnicity, occupation, education
+   - Geographic context (country, region type, GPS coordinates)
+   - Socioeconomic factors (income, insurance, family size)
+3. **Clinical Text Data**
+   - Multilingual patient history and chief complaints
+   - Pain descriptions with cultural expressions
+   - Traditional medicine usage patterns
+   - Clinical notes and examination findings
+4. **Environmental Context**
+   - Seasonal factors (dry/wet season, harmattan)
+   - Injury timing and circumstances
+   - Environmental hazards and geographic risks
+   - Weather conditions affecting transport/access
+5. **Clinical Measurements**
+   - Vital signs (heart rate, blood pressure, temperature)
+   - Anthropometric data (height, weight, BMI)
+   - Pain assessment scales
+   - Nutritional status indicators
+6. **Mobile Health Data**
+   - Phone ownership and type (smartphone vs feature phone)
+   - Digital literacy assessment
+   - Follow-up interaction preferences
+   - GPS and movement patterns
+### File Structure
+```
+african-medical-multimodal-fracture/
+├── images/
+│   ├── original/           # 1,129 original X-ray images
+│   └── augmented/          # 135 mildly augmented images
+├── data/
+│   ├── multimodal_records.json     # Complete dataset
+│   ├── feature_matrices.npz        # ML-ready features
+│   ├── data_splits.json           # Train/val/test splits
+│   └── dataset_summary.csv        # Overview table
+└── metadata/
+    ├── augmentation_log.json      # Image processing details
+    └── dataset_documentation.json # Technical documentation
+```
+## Fracture Types
+The dataset includes 10 fracture classifications:
+| Fracture Type | Count | Description |
+|---------------|-------|-------------|
+| Fracture Dislocation | 156 | Joint displacement with bone break |
+| Comminuted fracture | 148 | Multiple bone fragments |
+| Pathological fracture | 134 | Break due to disease/weakness |
+| Avulsion fracture | 123 | Bone fragment pulled by tendon/ligament |
+| Greenstick fracture | 122 | Incomplete break (common in children) |
+| Hairline Fracture | 111 | Thin crack in bone |
+| Spiral Fracture | 86 | Twisting break pattern |
+| Oblique fracture | 85 | Diagonal break across bone |
+| Impacted fracture | 84 | Bone ends driven together |
+| Longitudinal fracture | 80 | 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
+## Language Distribution
+| Language | Countries | Sample Size |
+|----------|-----------|-------------|
+| English | Nigeria, Kenya, Ghana, South Africa, Tanzania, Uganda | ~350 cases |
+| French | Senegal, Mali, Burkina Faso, Cameroon, Ivory Coast | ~200 cases |
+| Arabic | Egypt, Morocco, Sudan | ~150 cases |
+| Swahili | Kenya, Tanzania, Uganda | ~120 cases |
+| Hausa | Nigeria, Niger | ~80 cases |
+| Others | Various | ~229 cases |
+## Data Splits
+- **Training**: 790 samples (70%)
+- **Validation**: 113 samples (10%)
+- **Test**: 226 samples (20%)
+Splits are stratified by fracture type and region to ensure balanced representation.
+## Usage Examples
+### Loading the Dataset
+```python
+import json
+import pandas as pd
+import numpy as np
+from pathlib import Path
+# Load complete multimodal records
+with open('data/multimodal_records.json', 'r', encoding='utf-8') as f:
+    records = json.load(f)
+# Load ML-ready feature matrices
+features = np.load('data/feature_matrices.npz')
+demographics = features['demographics_normalized']
+clinical = features['clinical_measurements_normalized']
+labels = features['labels']
+# Load data splits
+with open('data/data_splits.json', 'r') as f:
+    splits = json.load(f)
+```
+### Accessing Multilingual Clinical Text
+```python
+# Example: Extract patient complaints in different languages
+for record in records[:5]:
+    clinical_text = record.get('clinical_text', {})
+    complaint = clinical_text.get('chief_complaint', {})
+    print(f"Language: {complaint.get('language', 'Unknown')}")
+    print(f"Complaint: {complaint.get('text', 'N/A')}")
+    print(f"Cultural expressions: {complaint.get('cultural_expressions', [])}")
+    print("---")
+```
+### Analyzing Geographic Distribution
+```python
+# Geographic analysis
+countries = [r['demographics']['country'] for r in records]
+regions = [r['demographics']['region_type'] for r in records]
+print("Country distribution:", pd.Series(countries).value_counts().head())
+print("Region distribution:", pd.Series(regions).value_counts())
+```
+## 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
+### Bias Mitigation
+- Balanced representation across countries and regions
+- Multiple languages and cultural contexts included
+- Socioeconomic diversity represented
+- Equipment quality reflects realistic African healthcare landscape
+## Limitations
+1. **Synthetic Nature**: While culturally informed, data is artificially generated
+2. **Language Accuracy**: Translations may not capture all cultural nuances
+3. **Equipment Simulation**: Based on research rather than direct measurements
+4. **Regional Variations**: Cannot capture all local healthcare variations
+5. **Temporal Factors**: Represents current state, not historical progression
+## 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
+### Research Applications
+- Cultural bias in medical AI
+- Multilingual medical NLP
+- Healthcare accessibility studies
+- Equipment quality impact analysis
+## Technical Specifications
+### Image Data
+- **Format**: JPEG
+- **Resolution**: Variable (167×94 to 640×640 pixels)
+- **Color**: Grayscale
+- **Quality**: 6.2-9.5/10 average quality score
+### Text Data
+- **Encoding**: UTF-8
+- **Languages**: 11 African languages + English
+- **Structure**: JSON with nested cultural context
+### Structured Data
+- **Demographics**: 7 normalized features
+- **Clinical**: 8 normalized measurements
+- **Environmental**: 5 contextual factors
+- **Labels**: 10-class fracture classification
+## Citation
+If you use this dataset in your research, please cite:
+```bibtex
+@dataset{african_medical_multimodal_2024,
+  title={African Medical Multimodal Bone Fracture Dataset},
+  author={[Your Name]},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/[username]/african-medical-multimodal-fracture}
+}
+```
+## Acknowledgments
+- Base dataset: PKDarabi Bone Break Classification Image Dataset
+- Cultural context research: WHO, IAEA, and African medical literature
+- Equipment assessment: African radiology societies and medical equipment suppliers
+## 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.
+## Contact
+For questions, issues, or collaboration opportunities, please open an issue in the dataset repository or contact [your-email@domain.com].
+---
+*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.*

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data/dataset_summary.csv ADDED Viewed

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data/feature_matrices.npz ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a6954dc4ced06dee52b29390fe04cdaa5af5096cee375987f69e550a517100be
+size 399606

data/multimodal_records.json ADDED Viewed

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dataset_info.json ADDED Viewed

	@@ -0,0 +1,63 @@

+{
+  "dataset_info": {
+    "name": "African Medical Multimodal Bone Fracture Dataset",
+    "version": "1.0.0",
+    "total_records": 1129,
+    "modalities": 6,
+    "languages": 11,
+    "countries": 18
+  },
+  "statistics": {
+    "country_distribution": {
+      "Tanzania": 96,
+      "South Africa": 76,
+      "Zambia": 75,
+      "Ghana": 75,
+      "Burkina Faso": 72,
+      "Zimbabwe": 68,
+      "Senegal": 66,
+      "Kenya": 65,
+      "Uganda": 63,
+      "Mali": 61
+    },
+    "language_distribution": {
+      "English": 383,
+      "French": 286,
+      "Arabic": 105,
+      "Swahili": 90,
+      "Afrikaans": 76,
+      "Amharic": 59,
+      "Zulu": 40,
+      "Twi": 35,
+      "Hausa": 20,
+      "Yoruba": 18,
+      "Wolof": 17
+    },
+    "region_distribution": {
+      "rural": 665,
+      "urban": 293,
+      "peri_urban": 171
+    },
+    "fracture_distribution": {
+      "Fracture Dislocation": 156,
+      "Comminuted fracture": 148,
+      "Pathological fracture": 134,
+      "Avulsion fracture": 123,
+      "Greenstick fracture": 122,
+      "Hairline Fracture": 111,
+      "Spiral Fracture": 86,
+      "Oblique fracture": 85,
+      "Impacted fracture": 84,
+      "Longitudinal fracture": 80
+    }
+  },
+  "file_structure": {
+    "data/multimodal_records.json": "1129 complete multimodal records",
+    "data/feature_matrices.npz": "ML-ready normalized features",
+    "data/data_splits.json": "Train/validation/test splits",
+    "data/dataset_summary.csv": "Tabular overview",
+    "images/original/": "Original X-ray images (88%)",
+    "images/augmented/": "Mildly augmented images (12%)",
+    "metadata/": "Processing logs and documentation"
+  }
+}

dataset_loader.py ADDED Viewed

	@@ -0,0 +1,126 @@

+"""
+African Medical Multimodal Dataset Loading Script
+Easy interface for loading the dataset components
+"""
+import json
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from typing import Dict, List, Tuple, Optional
+import cv2
+class AfricanMedicalDataset:
+    def __init__(self, dataset_path: str = "."):
+        self.dataset_path = Path(dataset_path)
+        self.records = None
+        self.features = None
+        self.splits = None
+    def load_multimodal_records(self) -> List[Dict]:
+        """Load complete multimodal records"""
+        with open(self.dataset_path / "data" / "multimodal_records.json", 'r', encoding='utf-8') as f:
+            self.records = json.load(f)
+        return self.records
+    def load_feature_matrices(self) -> Dict[str, np.ndarray]:
+        """Load ML-ready feature matrices"""
+        self.features = dict(np.load(self.dataset_path / "data" / "feature_matrices.npz"))
+        return self.features
+    def load_data_splits(self) -> Dict:
+        """Load train/validation/test splits"""
+        with open(self.dataset_path / "data" / "data_splits.json", 'r') as f:
+            self.splits = json.load(f)
+        return self.splits
+    def get_record_by_id(self, record_id: str) -> Optional[Dict]:
+        """Get specific record by ID"""
+        if not self.records:
+            self.load_multimodal_records()
+        for record in self.records:
+            if record.get("record_id") == record_id:
+                return record
+        return None
+    def get_records_by_country(self, country: str) -> List[Dict]:
+        """Get all records from specific country"""
+        if not self.records:
+            self.load_multimodal_records()
+        return [r for r in self.records
+                if r.get("demographics", {}).get("country") == country]
+    def get_records_by_language(self, language: str) -> List[Dict]:
+        """Get all records in specific language"""
+        if not self.records:
+            self.load_multimodal_records()
+        return [r for r in self.records
+                if r.get("demographics", {}).get("primary_language") == language]
+    def get_records_by_fracture_type(self, fracture_type: str) -> List[Dict]:
+        """Get all records of specific fracture type"""
+        if not self.records:
+            self.load_multimodal_records()
+        return [r for r in self.records if r.get("fracture_type") == fracture_type]
+    def get_summary_statistics(self) -> Dict:
+        """Get dataset summary statistics"""
+        if not self.records:
+            self.load_multimodal_records()
+        countries = [r.get("demographics", {}).get("country") for r in self.records]
+        languages = [r.get("demographics", {}).get("primary_language") for r in self.records]
+        regions = [r.get("demographics", {}).get("region_type") for r in self.records]
+        fractures = [r.get("fracture_type") for r in self.records]
+        return {
+            "total_records": len(self.records),
+            "countries": len(set(countries)),
+            "languages": len(set(languages)),
+            "country_distribution": pd.Series(countries).value_counts().to_dict(),
+            "language_distribution": pd.Series(languages).value_counts().to_dict(),
+            "region_distribution": pd.Series(regions).value_counts().to_dict(),
+            "fracture_distribution": pd.Series(fractures).value_counts().to_dict()
+        }
+    def export_subset(self, condition_func, output_path: str):
+        """Export subset of data based on condition function"""
+        if not self.records:
+            self.load_multimodal_records()
+        subset = [r for r in self.records if condition_func(r)]
+        with open(output_path, 'w', encoding='utf-8') as f:
+            json.dump(subset, f, indent=2, ensure_ascii=False)
+        print(f"Exported {len(subset)} records to {output_path}")
+# Example usage
+if __name__ == "__main__":
+    # Load dataset
+    dataset = AfricanMedicalDataset()
+    # Load all components
+    records = dataset.load_multimodal_records()
+    features = dataset.load_feature_matrices()
+    splits = dataset.load_data_splits()
+    # Get summary
+    stats = dataset.get_summary_statistics()
+    print("Dataset Statistics:")
+    print(f"Total records: {stats['total_records']}")
+    print(f"Countries: {stats['countries']}")
+    print(f"Languages: {stats['languages']}")
+    # Example queries
+    nigeria_cases = dataset.get_records_by_country("Nigeria")
+    swahili_cases = dataset.get_records_by_language("Swahili")
+    fracture_cases = dataset.get_records_by_fracture_type("Comminuted fracture")
+    print(f"\nNigeria cases: {len(nigeria_cases)}")
+    print(f"Swahili cases: {len(swahili_cases)}")
+    print(f"Comminuted fractures: {len(fracture_cases)}")

metadata/dataset_documentation.json ADDED Viewed

	@@ -0,0 +1,27 @@

+{
+  "dataset_description": "Integrated African Medical Dataset for Bone Fracture Classification",
+  "total_records": 1129,
+  "modalities": [
+    "image",
+    "demographics",
+    "clinical_text",
+    "environmental_context",
+    "clinical_measurements",
+    "mobile_health_data"
+  ],
+  "african_enhancements": [
+    "Multilingual clinical text (11 African languages)",
+    "Geographic and cultural context",
+    "Socioeconomic factors",
+    "Environmental conditions",
+    "Mobile health integration",
+    "Traditional medicine history"
+  ],
+  "feature_descriptions": {
+    "demographics": "Age, gender, region, education, healthcare access",
+    "clinical_measurements": "Vital signs, anthropometric data, pain assessment",
+    "environmental_context": "Seasonal factors, injury context, hazards",
+    "text_features": "Clinical notes, patient history, cultural context"
+  },
+  "usage_instructions": "Load multimodal_records.json for complete data, feature_matrices.npz for ML-ready features"
+}

metadata/mild_augmentation_log.json ADDED Viewed

	@@ -0,0 +1,2055 @@

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+  {
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+    "augmented_image": "images73_jpg.rf.874d94faa9c76eac64e6a61caee213b7_mild_portable_mobile_units_000.jpg",
+    "metadata": {
+      "equipment_type": "portable_mobile_units",
+      "applied_transforms": [
+        "mild_noise_0.145",
+        "contrast_0.930",
+        "brightness_1.077",
+        "mild_blur_3"
+      ],
+      "quality_score": 5.5,
+      "augmentation_level": "mild"
+    }
+  },
+  {
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+    "augmented_image": "20467tn_jpg.rf.0305d8adb13b816658d5b933ed02ac6f_mild_functional_digital_rural_001.jpg",
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+    }
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+    "metadata": {
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+    }
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+    "original_image": "/Users/kossiso/.cache/kagglehub/datasets/pkdarabi/bone-break-classification-image-dataset/versions/4/Bone Break Classification/Bone Break Classification/Greenstick fracture/Train/distal-radius-torus-fracture_jpg.rf.29556a0fbc1d3e67b32766973.jpg",
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metadata/synthetic_data_metadata.json ADDED Viewed

	@@ -0,0 +1,6 @@

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