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Background <p>The diagnosis of idiopathic pulmonary fibrosis (IPF) is complex, which requires lung biopsy, if necessary, and multidisciplinary discussions with specialists. Clinical diagnosis of the two ailments is particularly challenging due to the impact of interobserver variability. Several studies have endeavored to utilize image-based machine learning to diagnose IPF and its subtype of usual interstitial pneumonia (UIP). However, the diagnostic accuracy of this approach lacks evidence-based support.</p> Objective <p>We conducted a systematic review and meta-analysis to explore the diagnostic efficiency of image-based machine learning (ML) for IPF.</p> Data sources and methods <p>We comprehensively searched PubMed, Cochrane, Embase, and Web of Science databases up to August 24, 2024. During the meta-analysis, we carried out subgroup analyses by imaging source (computed radiography/computed tomography) and modeling type (deep learning/other) to evaluate its diagnostic performance for IPF.</p> Results <p>The meta-analysis findings indicated that in the diagnosis of IPF, the C-index, sensitivity, and specificity of ML were 0.93 (95% CI 0.89–0.97), 0.79 (95% CI 0.73–0.83), and 0.84 (95% CI 0.79–0.88), respectively. The sensitivity of radiologists/clinicians in diagnosing IPF was 0.69 (95% CI 0.56–0.79), with a specificity of 0.93 (95% CI 0.74–0.98). For UIP diagnosis, the C-index of ML was 0.91 (95% CI 0.87–0.94), with a sensitivity of 0.92 (95% CI 0.80–0.97) and a specificity of 0.92 (95%CI 0.82–0.97). In contrast, the sensitivity of radiologists/clinicians in diagnosing UIP was 0.69 (95% CI 0.50–0.84), with a specificity of 0.90 (95% CI 0.82–0.94).</p> Conclusions <p>Image-based machine learning techniques demonstrate robust data processing and recognition capabilities, providing strong support for accurate diagnosis of idiopathic pulmonary fibrosis and usual interstitial pneumonia. Future multicenter large-scale studies are warranted to develop more intelligent evaluation tools to further enhance clinical diagnostic efficiency.</p> <p><i>Trial registration</i> This study protocol was registered with PROSPERO (CRD42022383162).</p>

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Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月15日

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  • Li Cong,
  • Ying Chen,
  • Xiaolei He,
  • MaiLiKai KuErBan,
  • Chao Wu,
  • Liping Chen

摘要

Background

The diagnosis of idiopathic pulmonary fibrosis (IPF) is complex, which requires lung biopsy, if necessary, and multidisciplinary discussions with specialists. Clinical diagnosis of the two ailments is particularly challenging due to the impact of interobserver variability. Several studies have endeavored to utilize image-based machine learning to diagnose IPF and its subtype of usual interstitial pneumonia (UIP). However, the diagnostic accuracy of this approach lacks evidence-based support.

Objective

We conducted a systematic review and meta-analysis to explore the diagnostic efficiency of image-based machine learning (ML) for IPF.

Data sources and methods

We comprehensively searched PubMed, Cochrane, Embase, and Web of Science databases up to August 24, 2024. During the meta-analysis, we carried out subgroup analyses by imaging source (computed radiography/computed tomography) and modeling type (deep learning/other) to evaluate its diagnostic performance for IPF.

Results

The meta-analysis findings indicated that in the diagnosis of IPF, the C-index, sensitivity, and specificity of ML were 0.93 (95% CI 0.89–0.97), 0.79 (95% CI 0.73–0.83), and 0.84 (95% CI 0.79–0.88), respectively. The sensitivity of radiologists/clinicians in diagnosing IPF was 0.69 (95% CI 0.56–0.79), with a specificity of 0.93 (95% CI 0.74–0.98). For UIP diagnosis, the C-index of ML was 0.91 (95% CI 0.87–0.94), with a sensitivity of 0.92 (95% CI 0.80–0.97) and a specificity of 0.92 (95%CI 0.82–0.97). In contrast, the sensitivity of radiologists/clinicians in diagnosing UIP was 0.69 (95% CI 0.50–0.84), with a specificity of 0.90 (95% CI 0.82–0.94).

Conclusions

Image-based machine learning techniques demonstrate robust data processing and recognition capabilities, providing strong support for accurate diagnosis of idiopathic pulmonary fibrosis and usual interstitial pneumonia. Future multicenter large-scale studies are warranted to develop more intelligent evaluation tools to further enhance clinical diagnostic efficiency.

Trial registration This study protocol was registered with PROSPERO (CRD42022383162).