ENTERPRISE AI ANALYSIS
Application and clinical utility assessment of natural language processing-based software for copy-number variants interpretation
Manual interpretation of copy-number variants (CNVs) is time-consuming and prone to inconsistencies. This study evaluates CNVisi, a natural language processing (NLP)-based software designed to automate CNV interpretation following ACMG-ClinGen guidelines. In a preliminary assessment of 1000 manually classified CNVs, CNVisi achieved 97.7% accuracy in distinguishing pathogenic/likely pathogenic CNVs. For clinical utility, analyzing 3384 CNVs from NGS-based samples, CNVisi demonstrated 99.6% accuracy. While some inconsistencies (23 out of 154 reported CNVs) arose, mainly due to overlaps with low-penetrance regions and literature-based scoring, CNVisi significantly reduced expert workload and improved interpretation efficiency. Its performance was superior to other tools like ClassifyCNV and AnnotSV, showing excellent clinical utility by providing structured information and integrating multiple databases, thus streamlining the evidence gathering process.
Key Performance Indicators
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Clinical Utility Accuracy
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Preliminary Accuracy
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Expert Consistency
Deep Analysis & Enterprise Applications
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The CNVisi software utilizes a three-step NLP method for automated CNV annotation and interpretation, generating reports aligned with ACMG-ClinGen guidelines. It integrates six knowledge bases, including public databases and an in-house historical labeled corpus derived from clinical reports. The NLP approach involves paragraph segmentation, CNV-paragraph matching, and corpus classification, leveraging models like GloVe and biLSTM+CRF. This structured information significantly reduces manual search time for genetic experts.
Preliminary assessment on 1000 manually classified CNVs showed CNVisi achieved 97.7% overall accuracy for pathogenic/likely pathogenic CNV distinction, with 94.4% recall and 99.5% specificity. In clinical utility assessment with 3384 NGS-based CNVs, accuracy was 99.6%. Discrepancies (23 of 154 reported CNVs) mainly stemmed from low-penetrance regions and literature-based evidence scoring, but CNVisi's comprehensive database integration improved interpretation efficiency.
CNVisi substantially reduces the burden on genetic experts by automating evidence gathering from multiple databases and historical reports. It accurately interpreted 96.9% of CNV-seq samples, surpassing ClassifyCNV and AnnotSV in overall F1 score (91.9%). Although expert supervision is still recommended for final adjustments, CNVisi provides a standardized, efficient, and consistent approach to CNV interpretation, addressing a critical need in clinical diagnostics.
99.6%
Accuracy in Clinical Utility Assessment
CNVisi Interpretation Workflow
CNV Data Input
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Annotation (6 Knowledge Bases)
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Automated CNV Interpretation (NLP Methods)
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Automated Reporting
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Expert Review
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Final Clinical Reports
| Metric | CNVisi | ClassifyCNV | AnnotSV |
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| Accuracy | 99.6% | 99.0% | 91.5% |
| Recall (P/LP) | 91.9% | 66.3% | 98.8% |
| Specificity (P/LP) | 99.8% | 99.8% | 91.3% |
| Precision (P/LP) | 91.9% | 91.9% | 22.8% |
| F1 Score (P/LP) | 91.9% | 77.0% | 37.1% |
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Efficiency Gain in CNV Interpretation
Before CNVisi, manual interpretation of CNVs was labor-intensive, requiring extensive literature searches and database queries. With CNVisi's automation and NLP-driven knowledge bases, genetic experts can now streamline the interpretation process, significantly reducing the time spent per case.
Outcome: The overall consistency between genetic experts and CNVisi for reporting rules was 98.6% (5781/5861), indicating accurate and efficient interpretation for 96.9% of samples. This translates to substantial time and resource savings in clinical diagnostics.
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Estimated Annual Cost Savings
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Your Path to Automated Variant Interpretation
A structured approach ensures successful integration and maximum benefit from AI-powered CNV interpretation software.
Phase 1: Discovery & Strategy Alignment
Initial consultation to understand current workflows, data infrastructure, and specific interpretation challenges. Define key performance indicators (KPIs) and tailor an implementation strategy.
Phase 2: Data Integration & Customization
Securely integrate existing historical clinical reports and genomic databases. Customize CNVisi's knowledge bases and NLP models to align with institutional guidelines and specific genetic panels.
Phase 3: Pilot Deployment & Validation
Deploy CNVisi in a controlled pilot environment. Conduct rigorous validation against manually interpreted cases and gather feedback from genetic experts to fine-tune accuracy and usability.
Phase 4: Full-Scale Rollout & Training
Implement CNVisi across the entire diagnostic workflow. Provide comprehensive training for geneticists and laboratory staff to ensure seamless adoption and optimal utilization of the software.
Phase 5: Continuous Optimization & Support
Establish ongoing monitoring of CNVisi's performance. Regular updates to knowledge bases, model retraining, and dedicated support to adapt to evolving guidelines and data, ensuring long-term utility.
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