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Clinical Evaluation of a Novel Deep Learning-Based Auto-Segmentation Software: Utility and Potential Pitfalls

2026-01-11 radiology and imaging Title + abstract only
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BackgroundAccurate contouring of target volumes and organs at risk is critical for radiotherapy. While deep learning (DL) models offer efficient automation, their generalizability to real-world clinical cases containing anatomical variations and artifacts requires rigorous validation. PurposeTo evaluate the clinical accuracy and robustness of RatoGuide, a novel DL-based auto-segmentation software, using a dataset derived from routine clinical practice including atypical cases. MethodsThis sing...

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