IMAGING INFORMATICS AND ARTIFICIAL INTELLIGENCE
Deep learning-based automatic measurement of the femoral head ossification center in healthy Korean children: development of a novel radiographic growth chart
This study introduces a deep learning (DL)-based algorithm for automated measurement of femoral head ossification center (FHOC) size in healthy Korean children, and establishes AI-derived growth charts. The algorithm demonstrated high agreement with radiologist measurements and provided robust, standardized growth curves, offering a valuable tool for early detection of pediatric hip joint abnormalities and enhancing diagnostic consistency.
Executive Impact: Revolutionizing Pediatric Hip Assessment
Our AI-powered solution automates the precise measurement of femoral head ossification center (FHOC) size, significantly improving the objectivity and efficiency of pediatric hip joint evaluations. This breakthrough enables standardized growth assessment and facilitates earlier detection of developmental issues, transforming clinical practice for musculoskeletal imaging.
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Average CCC (AI vs. Radiologist)
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Predictive Accuracy (Growth Charts)
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Measurement Error (mm)
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Workflow Efficiency Improvement
Deep Analysis & Enterprise Applications
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Imaging Informatics
Artificial Intelligence
This study utilizes a three-stage cascaded deep learning algorithm for robust FHOC measurement directly from standard AP pelvic radiographs, seamlessly integrating with existing PACS infrastructure. The automated measurement process, from ROI detection to landmark-based size computation, significantly reduces the manual workload and potential for human error, ensuring consistent and objective data capture critical for large-scale clinical and research applications. This approach leverages existing imaging modalities to enhance data utilization without requiring new acquisition protocols.
Our solution employs a sophisticated deep learning pipeline featuring dedicated networks for region-of-interest detection (YOLOv8x), FHOC segmentation (DualBranchFusion-Net), and a post-processing algorithm for landmark-based size computation. The algorithm achieved high agreement with radiologist measurements (CCC > 0.996) and robust predictive accuracy for growth charts (adjusted R² > 0.927). This demonstrates the AI's capability to provide precise, reproducible, and clinically relevant measurements for pediatric hip joint assessment.
High Agreement
DL algorithm with expert measurements
Enhanced Pediatric Hip Assessment
AI-derived FHOC growth charts provide objective, standardized references for pediatric hip joint development, potentially enabling earlier detection of growth abnormalities and improving diagnostic consistency in clinical practice.
Enterprise Process Flow
ROI Detection
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FHOC Segmentation
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Landmark Detection
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FHOC Size Measurement
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Quantify Your ROI: Projected Savings & Efficiency
Estimate the potential financial and operational benefits of integrating AI-powered medical image analysis into your enterprise workflow.
Projected Annual Savings
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Annual Hours Reclaimed
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AI Implementation Roadmap: Your Path to Advanced Imaging
A structured approach to integrating AI-driven FHOC measurement into your clinical workflows.
Phase 1: Initial Assessment & Customization
Evaluate current infrastructure, define integration points, and customize the DL algorithm for specific clinical protocols and image formats. Data anonymization and security protocols are established.
Phase 2: Data Integration & Model Fine-tuning
Integrate the AI solution with PACS, perform initial data calibration with a small subset of de-identified patient data, and fine-tune the model for optimal local performance and accuracy.
Phase 3: Validation & Pilot Deployment
Conduct thorough validation against radiologist measurements on a larger, independent dataset. Deploy the solution in a pilot program within a controlled clinical environment to gather user feedback and refine workflows.
Phase 4: Full-Scale Rollout & Ongoing Optimization
Expand deployment across relevant departments, provide comprehensive staff training, and establish continuous monitoring for performance. Implement regular updates and optimizations based on real-world usage and new research.
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Discuss how our deep learning solution for FHOC measurement can elevate your diagnostic capabilities and research initiatives.
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