Detailed analysis of our medical data annotation projects and their impact on healthcare AI development.
View Case StudiesWe are a specialized data annotation team focused on medical and healthcare datasets. Our mission is to help AI/ML teams build accurate and compliant models through high-quality labeled data.
Task: Annotated chest X-ray images with bounding boxes for pneumonia detection.
Annotation Type: Bounding boxes following predefined medical annotation guidelines.
Quality Assurance: Double-level QA with medical expert validation.
Task: Polygon-based segmentation for brain MRI scans to identify tumor regions.
Annotation Type: Pixel-level segmentation masks.
Quality Assurance: Reviewer validation for pixel-level accuracy.
Task: Named Entity Recognition on clinical notes to extract disease names, medications, dosage, and patient symptoms.
Annotation Type: Entity labeling and classification.
Quality Assurance: Validation sampling with expert review.
Client: Medical AI Startup
Challenge: Develop a training dataset for deep learning model to detect pneumonia in chest X-rays with high accuracy for clinical use.
Solution: Annotated 15,000 chest X-ray images with bounding boxes around pneumonia-affected areas. Each annotation followed RADLEX ontology standards.
Tools Used: Custom annotation platform with DICOM viewer integration, quality control dashboard.
Outcome: The annotated dataset enabled the client to train a model with 94.2% accuracy in pneumonia detection, which is now in clinical trials.
Client: Neurosurgery Research Department
Challenge: Create precise tumor boundary segmentation for treatment planning and volumetric analysis.
Solution: Pixel-level segmentation of 2,000 brain MRI scans using polygon annotation tools. Three-class segmentation: tumor core, edema, and healthy tissue.
Tools Used: 3D Slicer integration, custom segmentation tools, inter-rater reliability validation.
Outcome: Segmentation accuracy exceeded clinical requirements, reducing radiologist review time by 65% for tumor volumetry.
Client: Healthcare NLP Company
Challenge: Extract structured information from unstructured clinical notes for patient cohort identification.
Solution: Named Entity Recognition on 50,000 clinical notes with 15 entity types including medications, diagnoses, procedures, and temporal information.
Tools Used: Prodigy annotation platform, custom entity linking to UMLS terminology, consensus adjudication system.
Outcome: The annotated corpus enabled training of a BERT-based model with state-of-the-art performance on clinical NER tasks.
Our quality assurance process ensures the highest standards in medical data annotation:
This rigorous process ensures that our annotations meet clinical standards and are suitable for training diagnostic AI models.
Analysis & guideline creation
By trained medical annotators
Secondary quality assurance
Accuracy report & final delivery
"The annotated dataset provided by Infosortzailea was instrumental in achieving FDA submission readiness for our pneumonia detection algorithm. Their medical expertise and attention to detail were exceptional."
- clients
"Working with their team accelerated our research timeline significantly. The segmentation accuracy exceeded our expectations and has been cited in two peer-reviewed publications."
- Prof. Michael Rodriguez, Director of Neuroimaging Research
Specialized knowledge in medical terminology and imaging
Flexible pricing models suitable for all business sizes
Efficient workflows for timely delivery
HIPAA-compliant data handling and security protocols