Orthopedic Screw
Classification
A medical imaging AI system using Mask R-CNN to detect and classify orthopedic screws from X-ray images. Trained on NVIDIA V100 GPUs with a custom dataset of 2,000+ images, achieving 92.3% mAP for surgical hardware identification.
Detection Results
X-Ray Analysis
Classified Types:
mAP@0.5
92.3%
Inference Speed
180ms
Training Time
14 hours
Dataset Size
2,000+
Medical AI Innovation
State-of-the-art deep learning for surgical hardware detection in radiographic imaging.
Mask R-CNN Instance Segmentation
State-of-the-art deep learning architecture combining Region Proposal Network (RPN) with mask prediction for pixel-perfect screw localization. ResNet-101 backbone with Feature Pyramid Network (FPN) extracts multi-scale features for detecting screws of varying sizes in X-ray images.
GPU-Accelerated Training Pipeline
Distributed training on NVIDIA Tesla V100 GPUs using PyTorch with mixed precision (FP16/FP32) for 3x speedup. Implemented gradient accumulation and learning rate scheduling with cosine annealing. Training time reduced from 48 hours to 14 hours with multi-GPU setup.
Medical Dataset Curation
Custom-labeled dataset of 2,000+ orthopedic X-ray images annotated with COCO format bounding boxes and segmentation masks. Dataset includes 5 screw classes (cortical, cancellous, locking, cannulated, compression) with balanced class distribution and rigorous quality control.
Screw Type Detection
Multi-class classification across 5 orthopedic screw categories.
Cortical Screws
Fully threaded screws for cortical bone fixation
Cancellous Screws
Partially threaded screws for cancellous bone
Locking Screws
Screws that lock into plate holes
Cannulated Screws
Hollow screws for guidewire insertion
Compression Screws
Screws designed for interfragmentary compression
Training Pipeline
Dataset Preparation
Medical X-ray images collected and anonymized per HIPAA compliance. Expert radiologists annotated screw locations with polygon masks using LabelMe. Dataset split: 70% training (1,400 images), 20% validation (400), 10% test (200) with stratified sampling.
Data Augmentation
Albumentations library applies random transformations: rotation (±15°), brightness/contrast adjustment, Gaussian noise injection, and elastic deformation. Augmentations simulate real-world X-ray variability improving model robustness to imaging conditions.
Model Architecture
Mask R-CNN with ResNet-101-FPN backbone pre-trained on COCO dataset. Custom anchor sizes tuned for screw dimensions. RoI heads configured for 5-class classification plus background. Mask head outputs 28×28 binary masks for each detected instance.
GPU Training
Trained on 4x NVIDIA V100 GPUs (32GB VRAM each) using PyTorch DistributedDataParallel. Batch size: 8 images per GPU. Optimizer: SGD with momentum 0.9, weight decay 0.0001. Learning rate: 0.02 with warmup and cosine annealing over 24 epochs.
Inference & Evaluation
Model achieves 92.3% mAP@0.5 on test set. Inference speed: 180ms per image on single V100. Post-processing with NMS (IoU threshold 0.5) removes duplicate detections. Confidence threshold of 0.7 balances precision (94.1%) and recall (89.8%).
Model Performance
Comprehensive evaluation metrics demonstrating clinical-grade accuracy.
Detection Metrics
- Mean Average Precision (mAP@0.5): 92.3%
- Mean Average Precision (mAP@0.75): 87.6%
- Precision: 94.1%
- Recall: 89.8%
- F1-Score: 91.9%
- False Positive Rate: 5.9%
Segmentation Quality
- Mask IoU (Instance): 88.4%
- Boundary F1-Score: 85.2%
- Pixel Accuracy: 96.7%
- Mean Dice Coefficient: 90.1%
- Edge Alignment Score: 87.9%
- Segmentation Precision: 93.3%
Computational Performance
- Single Image Inference: 180ms (V100)
- Batch Inference (8 images): 950ms
- GPU Memory Usage: 6.2GB
- Model Parameters: 63.2M
- FLOPs: 275G per image
- Throughput: 5.5 images/sec
Technology Stack
Production-grade deep learning infrastructure for medical imaging.
Deep Learning Framework
- PyTorch 2.0
- Torchvision
- Detectron2
- CUDA 11.8
- cuDNN 8.6
Computer Vision
- Mask R-CNN
- ResNet-101
- Feature Pyramid Network
- RoI Align
- Non-Maximum Suppression
Data Processing
- OpenCV
- Pillow
- NumPy
- Albumentations
- COCO API
Infrastructure
- NVIDIA V100 GPU
- Docker
- TensorBoard
- MLflow
- Weights & Biases
Medical AI Impact
Surgical Planning
Automated hardware inventory for pre-operative assessment and surgical planning.
Quality Control
Verify correct screw type and placement in post-operative X-rays.
Research Analytics
Large-scale analysis of screw usage patterns and clinical outcomes.
This research demonstrates the application of advanced computer vision techniques to medical imaging challenges. By leveraging Mask R-CNN instance segmentation and GPU-accelerated training, the system achieves clinical-grade accuracy for automated orthopedic hardware detection, supporting radiologists and orthopedic surgeons in diagnostic workflows.