Heart & lung : the journal of critical care · 2025
Systematic review and meta-analysis of artificial intelligence models for diagnosing and subphenotyping ARDS in adults.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод пока не подготовлен
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Muñoz Javier, Ruíz-Cacho Rocío, Fernández-Araujo Nerio José, Candela Alberto, Visedo Lourdes Carmen, Muñoz-Visedo Javier
Аннотация
BACKGROUND: Artificial intelligence (AI) has emerged as a promising tool to improve the diagnosis and characterization of ARDS, including the identification of subphenotypes.
OBJECTIVES: To evaluate the diagnostic performance and methodological quality of AI models for identifying ARDS and its subphenotypes in adults.
METHODS: We conducted a systematic review and meta-analysis of 63 studies (n = 135,762) published between 2013 and 2024 in PubMed, Embase, and the Cochrane Library. Extracted outcomes included sensitivity, specificity, AUROC, and validation methods. Risk of bias was assessed with PROBAST, and AI-specific metrics (overfitting, generalization, interpretability, discrimination, calibration) were reported.
RESULTS: Pooled sensitivity was 0.89 (95 % CI 0.84-0.93), specificity 0.88 (95 % CI 0.83-0.92), and AUROC 0.90 (95 % CI 0.86-0.94), with high heterogeneity (I² > 85 %). Twenty-two studies (31 %) were rated high quality, with sensitivity 0.86 (95 % CI 0.82-0.89) and specificity 0.82 (95 % CI 0.78-0.85). Deep learning models (n = 14) achieved sensitivity 0.91, while machine learning models (n = 19) showed 0.87. Imaging-based models (n = 15) outperformed non-imaging approaches. COVID-19 studies (n = 9) reported sensitivity 0.90 with comparable AUROC and specificity. Only seven studies (18 %) investigated subphenotyping, identifying hyperinflammatory and hypoinflammatory profiles with potential therapeutic relevance. Calibration reporting was missing in 47 % and external validation in most (29/63).
CONCLUSION: AI models for ARDS demonstrate promising diagnostic accuracy but are limited by poor calibration and scarce external validation. Subphenotyping remains exploratory but suggests opportunities for real-time patient stratification. Prospective validation and standardized reporting are essential for clinical adoption.
OBJECTIVES: To evaluate the diagnostic performance and methodological quality of AI models for identifying ARDS and its subphenotypes in adults.
METHODS: We conducted a systematic review and meta-analysis of 63 studies (n = 135,762) published between 2013 and 2024 in PubMed, Embase, and the Cochrane Library. Extracted outcomes included sensitivity, specificity, AUROC, and validation methods. Risk of bias was assessed with PROBAST, and AI-specific metrics (overfitting, generalization, interpretability, discrimination, calibration) were reported.
RESULTS: Pooled sensitivity was 0.89 (95 % CI 0.84-0.93), specificity 0.88 (95 % CI 0.83-0.92), and AUROC 0.90 (95 % CI 0.86-0.94), with high heterogeneity (I² > 85 %). Twenty-two studies (31 %) were rated high quality, with sensitivity 0.86 (95 % CI 0.82-0.89) and specificity 0.82 (95 % CI 0.78-0.85). Deep learning models (n = 14) achieved sensitivity 0.91, while machine learning models (n = 19) showed 0.87. Imaging-based models (n = 15) outperformed non-imaging approaches. COVID-19 studies (n = 9) reported sensitivity 0.90 with comparable AUROC and specificity. Only seven studies (18 %) investigated subphenotyping, identifying hyperinflammatory and hypoinflammatory profiles with potential therapeutic relevance. Calibration reporting was missing in 47 % and external validation in most (29/63).
CONCLUSION: AI models for ARDS demonstrate promising diagnostic accuracy but are limited by poor calibration and scarce external validation. Subphenotyping remains exploratory but suggests opportunities for real-time patient stratification. Prospective validation and standardized reporting are essential for clinical adoption.