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<p>Despite advances for patients with acute leukemia health disparities limit access to diagnosis and treatment. Artificial Intelligence (AI) approaches may address some disparities. We retrospectively assemble a diverse, international cohort of 6206 leukemia patients from 20 centers to test an AI tool designed to support leukemia diagnosis using standard laboratory results. Executing the pretrained algorithm results in varying accuracy metrics. With confidence cutoff predictions, 2000-fold bootstrapped area under the curve (AUROC) metrics are 0.94 for acute myeloid leukemia (AML), 0.98 for the promyelocytic subtype and 0.84 for acute lymphoblastic leukemia. However, this cutoff excludes 70.8–92.5% of patients from predictions. We improve accuracy and robustness, while maintaining generalizability via an ensemble of Isolation Forest and Local Outlier Factor increasing AUROC for AML from 0.72 to 0.84 (hold-out test set, patients below confidence threshold), while excluding only 12.1% of patients. Furthermore, we retrain the algorithm for pediatric patients.</p>

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International testing and refinement of AI algorithms predicting acute leukemia subtypes from routine laboratory data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月20日

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  • Amin T. Turki,
  • Yi Fan,
  • Alberto Hernández-Sánchez,
  • Wellington Silva,
  • Shaun Fleming,
  • Koray Yalcin,
  • Catharina H.M.J. Van Elssen,
  • Yazan Madanat,
  • Magdalena Karasek,
  • Mahmoud Aljurf,
  • Matteo G. Della Porta,
  • Alexandra Martinez-Roca,
  • Luca Guarnera,
  • Katarina Steffen,
  • Evangelia Antoniou,
  • Maria M. Rivas,
  • Deepak K. Mishra,
  • Ansgar T. Blum,
  • Stephania Niry Manantsoa,
  • Adeniyi Adiat,
  • Amir Enshaei,
  • Felicitas Thol,
  • Maria Teresa Voso,
  • Jia Chen,
  • Tusneem Ahmed Elhassan,
  • Anthony V. Moorman,
  • María Belén Vidriales,
  • Nina R. Neuendorff,
  • Ahmet Koc,
  • Pratyush Mishra,
  • Dirk Strumberg,
  • Roma S. Fourmanov,
  • Lukas Heine,
  • Jens Kleesiek,
  • Daniel Munárriz,
  • Gianluca Asti,
  • Mridula Mokoonlall,
  • Marisa Kometas,
  • Eduardo Rego,
  • Rabea Mecklenbrauck,
  • Marta Sobas,
  • Depei Wu,
  • Felix Nensa,
  • Merlin Engelke

摘要

Despite advances for patients with acute leukemia health disparities limit access to diagnosis and treatment. Artificial Intelligence (AI) approaches may address some disparities. We retrospectively assemble a diverse, international cohort of 6206 leukemia patients from 20 centers to test an AI tool designed to support leukemia diagnosis using standard laboratory results. Executing the pretrained algorithm results in varying accuracy metrics. With confidence cutoff predictions, 2000-fold bootstrapped area under the curve (AUROC) metrics are 0.94 for acute myeloid leukemia (AML), 0.98 for the promyelocytic subtype and 0.84 for acute lymphoblastic leukemia. However, this cutoff excludes 70.8–92.5% of patients from predictions. We improve accuracy and robustness, while maintaining generalizability via an ensemble of Isolation Forest and Local Outlier Factor increasing AUROC for AML from 0.72 to 0.84 (hold-out test set, patients below confidence threshold), while excluding only 12.1% of patients. Furthermore, we retrain the algorithm for pediatric patients.