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<p>Peripheral blood smear examination is labor-intensive and operator-dependent. We conducted a multicenter, prospective, randomized paired study of an AI-assisted digital morphology system in routine practice. Among 1570 analyzed smears from three centers, AI-assisted analysis outperformed manual microscopy across 15 nucleated cell categories, including improved screening for acute promyelocytic leukemia through detection of abnormal promyelocytes (F1 score 0.794 [95% CI, 0.667–0.892] vs 0.658 [0.508–0.767]). AI assistance also improved diagnostic performance across most erythrocyte morphology categories, including better detection of teardrop cells, a morphologic clue relevant to myelofibrosis (F1 score 0.306 [0.233–0.374] vs 0.261 [0.190–0.323]). Platelet estimation was more accurate with AI assistance, with lower absolute mean deviation from flow cytometry (2.05 vs 14.08 × 10<sup>9</sup>/L). AI assistance increased technician efficiency by approximately 60%, supporting its potential role in improving the consistency and efficiency of morphology reporting for the evaluated cell categories in routine smear-review workflows.</p>

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AI-assisted digital peripheral blood morphology multicenter randomized paired method clinical validation study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Song-song LU,
  • Yong-hui GUO,
  • Dong-mei LIU,
  • Xiao-ning CHEN,
  • Meng-jie ZHU,
  • Zhong-hui ZHANG,
  • Wei ZHAI,
  • Man HAN,
  • Jing-zhong ZHAO,
  • Yi YE,
  • Jianping XIAO,
  • Yu-jing CHEN,
  • Ya-ying LIN,
  • Jie-yi LU,
  • Hui-xian LUO,
  • Jun CAO,
  • Yong WANG,
  • Dong ZHENG,
  • Xue-dong CHEN,
  • Jun QIU,
  • Hui Wang

摘要

Peripheral blood smear examination is labor-intensive and operator-dependent. We conducted a multicenter, prospective, randomized paired study of an AI-assisted digital morphology system in routine practice. Among 1570 analyzed smears from three centers, AI-assisted analysis outperformed manual microscopy across 15 nucleated cell categories, including improved screening for acute promyelocytic leukemia through detection of abnormal promyelocytes (F1 score 0.794 [95% CI, 0.667–0.892] vs 0.658 [0.508–0.767]). AI assistance also improved diagnostic performance across most erythrocyte morphology categories, including better detection of teardrop cells, a morphologic clue relevant to myelofibrosis (F1 score 0.306 [0.233–0.374] vs 0.261 [0.190–0.323]). Platelet estimation was more accurate with AI assistance, with lower absolute mean deviation from flow cytometry (2.05 vs 14.08 × 109/L). AI assistance increased technician efficiency by approximately 60%, supporting its potential role in improving the consistency and efficiency of morphology reporting for the evaluated cell categories in routine smear-review workflows.