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<p>Soil-transmitted helminths primarily comprise <i>Ascaris lumbricoides</i>, <i>Trichuris trichiura</i>, and hookworms, infecting more than 600&#xa0;million people globally, particularly in underserved communities. Manual microscopy of Kato-Katz thick smears is a widely used diagnostic method in monitoring and control programs, but is time-consuming, requires on-site experts and has low sensitivity, especially for light intensity infections. In this study, portable whole-slide scanners and deep learning-based artificial intelligence (AI) were deployed in a primary healthcare setting in Kenya. Stool samples (<i>n</i> = 965) were collected from school children and Kato-Katz thick smears were digitized for AI-based detection. Light-intensity infections accounted for 96.7% of cases. Three diagnostic methods - manual microscopy, autonomous AI and human expert-verified AI - were compared to a composite reference standard, which combined expert-verified helminth eggs in physical and digital smears. Sensitivity for <i>A. lumbricoides</i>, <i>T. trichiura</i> and hookworms was 50.0%, 31.2%, and 77.8% for manual microscopy; 50.0%, 84.4%, and 87.4% for the autonomous AI; and 100%, 93.8%, and 92.2% for expert-verified AI in smears suitable for analysis (<i>n</i> = 704). Specificity exceeded 97% across all methods. The expert-verified AI had higher sensitivity than the other methods while maintaining high specificity for the detection of soil-transmitted helminths in Kato-Katz thick smears, especially in light-intensity infections.</p>

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AI-supported versus manual microscopy of Kato-Katz smears for diagnosis of soil-transmitted helminth infections in a primary healthcare setting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月27日

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  • Joar von Bahr,
  • Antti Suutala,
  • Hakan Kucukel,
  • Harrison Kaingu,
  • Felix Kinyua,
  • Martin Muinde,
  • Kevan Osundwa,
  • Wigina Ronald,
  • Jackson Muinde,
  • Billy Ngasala,
  • Mikael Lundin,
  • Andreas Mårtensson,
  • Nina Linder,
  • Johan Lundin

摘要

Soil-transmitted helminths primarily comprise Ascaris lumbricoides, Trichuris trichiura, and hookworms, infecting more than 600 million people globally, particularly in underserved communities. Manual microscopy of Kato-Katz thick smears is a widely used diagnostic method in monitoring and control programs, but is time-consuming, requires on-site experts and has low sensitivity, especially for light intensity infections. In this study, portable whole-slide scanners and deep learning-based artificial intelligence (AI) were deployed in a primary healthcare setting in Kenya. Stool samples (n = 965) were collected from school children and Kato-Katz thick smears were digitized for AI-based detection. Light-intensity infections accounted for 96.7% of cases. Three diagnostic methods - manual microscopy, autonomous AI and human expert-verified AI - were compared to a composite reference standard, which combined expert-verified helminth eggs in physical and digital smears. Sensitivity for A. lumbricoides, T. trichiura and hookworms was 50.0%, 31.2%, and 77.8% for manual microscopy; 50.0%, 84.4%, and 87.4% for the autonomous AI; and 100%, 93.8%, and 92.2% for expert-verified AI in smears suitable for analysis (n = 704). Specificity exceeded 97% across all methods. The expert-verified AI had higher sensitivity than the other methods while maintaining high specificity for the detection of soil-transmitted helminths in Kato-Katz thick smears, especially in light-intensity infections.