Four will be presented through oral presentations and three as E-posters.
In a featured oral presentation led by Dr.
Specifically, the AI model with high sensitivity could be appropriate for emergency findings like pneumothorax. Meanwhile, a high specificity is preferable to triage low-risk studies for reporting without missing actionable pathology. This dual approach highlighted the adaptability of
In another oral presentation, a research team from
Among seven AI-powered algorithms, Lunit INSIGHT CXR achieved the highest AUC (Area Under the Curve) of 0.93 in lung nodule detection, outperforming human readers (mean AUC 0.81). The sensitivity of Lunit INSIGHT CXR reached 89%, outperforming human readers while maintaining specificity comparable to that of human readers at 80%. Four out of seven AI products, including Lunit INSIGHT CXR, showed superior performance compared to human readers. This validation provides crucial comparative performance data for AI algorithms, contributing to the ongoing discussions about the integration of AI in clinical practice.
Lunit INSIGHT MMG,
All three scenarios showed that AI-integrated screening can partially or fully replace one or both readers without affecting screening accuracy. Specifically, in Scenario 2, AI-assisted screening indicated significantly higher specificity (+0.6%) and positive predictive value (+4.7%). This groundbreaking research points towards a future where AI seamlessly integrates into double screening processes, enhancing efficiency, optimizing workflow, and maintaining stable accuracy.
Additionally,
'We are proud to present our latest research findings at this year's ECR, demonstrating the robust performance and versatility of our Lunit INSIGHT suite in various clinical settings and scenarios. Our studies also reveal that our AI can enhance the efficiency and accuracy of mammography double-reading settings, and even replace human readers in some cases,' said
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Oral presentations featuring Lunit INSIGHT at ECR 2024 include: 'The performance of a commercial artificial intelligence algorithm in an external quality assurance scheme regularly used by humans in the
'The Multi- Sixteen Thousand and Counting: Performance of an Artificial Intelligence Tool for Identifying Common Pathologies on Chest Radiographs and Report Prioritisation' (ACV 2024 Research Stage 1,
'Recent development in AI for lung nodule detection' (ACV 2024 Research Stage 2,
'Integration of artificial intelligence (AI) in double-read population-based mammography screening: simulated replacement of one reader and beyond' (ACV 2024 Research Stage 1,
Poster presentations featuring Lunit INSIGHT at ECR 2024 include: 'Can artificial intelligence decrease the time to histological diagnosis of lung cancer - a retrospective-cohort study' (EPOS Area -2 Level)
'Deep Learning for Chest Radiograph Evaluation in Children: Repurposed Use of a Commercially Available AI Tool Developed for Adults (EPOS Area -2 Level)
'Comparison of three AI breast density tools with a human reader' (EPOS Area -2 Level)
Contact:
Email: contact@lunit.io
Email: media@lunit.io
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