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New algorithm reconstructs patient data from medical figures

By PulseAugur Editorial · [1 sources] ·

Researchers have developed StochIKM, a novel algorithm designed to reconstruct individual patient data from published Kaplan-Meier curves. This method utilizes a vision-capable language model to digitize figures and applies explicit fidelity constraints, ensuring the reconstructed data accurately reproduces risk tables, adheres to the digitized curve within image resolution, and respects printed median values, interval, and event counts. StochIKM guarantees a solution exists if the figure is read correctly, and in trials involving 79 arms from published oncology studies, it demonstrated superior accuracy in reproducing published risk tables and medians compared to the LLM-improved IPDfromKM alternative. AI

IMPACT This method could improve the accessibility and utility of published medical research by enabling more robust secondary analyses.

RANK_REASON The cluster contains a research paper detailing a new algorithm for data reconstruction from scientific figures. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm reconstructs patient data from medical figures

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The cluster contains a research paper detailing a new algorithm for data reconstruction from scientific figures. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Liangze Ke, Daniel Zelterman, Wei Wei, Guannan Gong ·

    StochIKM: Automated High-Fidelity Reconstruction of Individual Patient Data from Published Kaplan-Meier Curves

    arXiv:2610.29122v2 Announce Type: new Abstract: Individual patient data are the gold standard for secondary analysis of time-to-event evidence, but they are rarely released, and reconstruction from a published Kaplan-Meier figure could help. Existing methods all claim high fideli…

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