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Purpose <p>An artificial intelligence (AI) system for detecting interstitial lung abnormalities (ILA) was previously developed but requires external validation. This study aimed to examine the robustness across different populations and investigate associations between the system outputs and traction bronchiectasis/bronchiolectasis severity patterns.</p> Materials and methods <p>CT scans from population-based samples of the Rotterdam Study (2018–2019) and the Age Gene/Environment Susceptibility Reykjavik (AGES-Reykjavik) Study (baseline CT: 2002–2006, follow-up CT: 2007–2011) were used in this secondary analysis of the two cohorts. The AI system calculated ILA probability score (AI score) in the range from 0 to 1. Three experienced readers evaluated independently all CT scans for ILA, and two chest radiologists assessed traction bronchiectasis/bronchiolectasis using the 4-scale traction bronchiectasis/bronchiolectasis index (TBI) for severity by consensus. Receiver operating characteristic (ROC) analysis and Kruskal–Wallis test were used for statistical analysis.</p> Results <p>The system analyzed 932 CT scans of the Rotterdam Study (mean participant age, 79.6&#xa0;years ± 4.3 (SD), 482 women) and 5242 CT scans of the AGES-Reykjavik Study (mean participant age, 76.4&#xa0;years ± 5.6, 3032 women), and achieved area under the ROC curve of 0.841 (95% CI 0.804, 0.879) and 0.823 (95% CI 0.798, 0.847), respectively. AI scores correlated with readers’ certainty, decreasing from unanimous ILA cases to No-ILA cases. Higher baseline AI scores correlated with greater severity of traction bronchiectasis/bronchiolectasis (TBI-3: 0.931 [IQR, 0.911–0.932], TBI-2: 0.738 [IQR, 0.406–0.880], TBI-1: 0.537 [IQR, 0.317–0.761], TBI-0: 0.250 [IQR, 0.136–0.455]).</p> Conclusion <p>The system demonstrated robust ILA detection performance across different populations, with AI scores showing associations with traction bronchiectasis/bronchiolectasis severity.</p>

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Automated interstitial lung abnormalities detection at CT: external validation and potential recognition of traction bronchiectasis/bronchiolectasis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月11日

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  • Yusei Nakamura,
  • Taiki Fukuda,
  • Kota Aoyagi,
  • Masami Kawagishi,
  • Yuki Ko,
  • Noriaki Wada,
  • Takuya Hino,
  • Tomoyuki Hida,
  • Meike W. Vernooij,
  • Daniel Bos,
  • Daan W. Loth,
  • Masahiro Ozaki,
  • Akihiro Koga,
  • Heida Bjarnadottir,
  • Valborg Gudmundsdottir,
  • Gunnar Gudmundsson,
  • Vilmundur Gudnason,
  • Mizuki Nishino,
  • David C. Christiani,
  • Gary M. Hunninghake,
  • Kousei Ishigami,
  • Hiroto Hatabu

摘要

Purpose

An artificial intelligence (AI) system for detecting interstitial lung abnormalities (ILA) was previously developed but requires external validation. This study aimed to examine the robustness across different populations and investigate associations between the system outputs and traction bronchiectasis/bronchiolectasis severity patterns.

Materials and methods

CT scans from population-based samples of the Rotterdam Study (2018–2019) and the Age Gene/Environment Susceptibility Reykjavik (AGES-Reykjavik) Study (baseline CT: 2002–2006, follow-up CT: 2007–2011) were used in this secondary analysis of the two cohorts. The AI system calculated ILA probability score (AI score) in the range from 0 to 1. Three experienced readers evaluated independently all CT scans for ILA, and two chest radiologists assessed traction bronchiectasis/bronchiolectasis using the 4-scale traction bronchiectasis/bronchiolectasis index (TBI) for severity by consensus. Receiver operating characteristic (ROC) analysis and Kruskal–Wallis test were used for statistical analysis.

Results

The system analyzed 932 CT scans of the Rotterdam Study (mean participant age, 79.6 years ± 4.3 (SD), 482 women) and 5242 CT scans of the AGES-Reykjavik Study (mean participant age, 76.4 years ± 5.6, 3032 women), and achieved area under the ROC curve of 0.841 (95% CI 0.804, 0.879) and 0.823 (95% CI 0.798, 0.847), respectively. AI scores correlated with readers’ certainty, decreasing from unanimous ILA cases to No-ILA cases. Higher baseline AI scores correlated with greater severity of traction bronchiectasis/bronchiolectasis (TBI-3: 0.931 [IQR, 0.911–0.932], TBI-2: 0.738 [IQR, 0.406–0.880], TBI-1: 0.537 [IQR, 0.317–0.761], TBI-0: 0.250 [IQR, 0.136–0.455]).

Conclusion

The system demonstrated robust ILA detection performance across different populations, with AI scores showing associations with traction bronchiectasis/bronchiolectasis severity.