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AI-MRI LV Volumetry Precision Index Calculator

Selected references (evidence used to design index): Davies RH et al., "Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning" (J Cardiovasc Magn Reson). This work shows automated algorithms can improve precision vs manual methods. Alabed S et al., "Quality of reporting in AI cardiac MRI segmentation studies" (Frontiers in Cardiovascular Medicine) — review

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Active structured worksheetActive local worksheet for structured input capture, review, copying, and export on this device. It deliberately makes no numerical or evidence claim without a reproducible primary source.
Clinical contextWhy this tool matters and how to interpret it

Understand the result,
not just the number.

Selected references (evidence used to design index): Davies RH et al., "Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning" (J Cardiovasc Magn Reson). This work shows automated algorithms can improve precision vs manual methods. Alabed S et al., "Quality of reporting in AI cardiac MRI segmentation studies" (Frontiers in Cardiovascular Medicine) — review describing Dice as the dominant segmentation metric and issues in reporting.

Bartoli A et al., "Deep Learning–based Automated Segmentation of Left ..." — example of high Dice values achievable with modern networks. Schilling M et al., "Assessment of deep learning segmentation for real-time ..." (Scientific Reports 2024) — assessment of DL methods for volumetric analysis and practical considerations. Kawel-Boehm N. et al., "Reference ranges for cardiovascular MRI" (JCMR) — guidance on volumetric measures and indexing.

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Active local worksheet for structured input capture, review, copying, and export on this device. It deliberately makes no numerical or evidence claim without a reproducible primary source.

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