Quantitative Analysis of Lung Opacities on Routine Chest X-Ray Radiograph

Background: Chest X-ray (CXR) remains the most widely used imaging modality for evaluating pulmonary diseases. Interpretation of lung opacities on CXRs is traditionally qualitative and subject to inter-observer variability. Artificial intelligence (AI) offers an opportunity for objective and reproducible quantification of lung opacities. Objectives: To quantitatively assess lung opacity extent on routine chest X-rays using AI-based analysis, compare AI scores with radiologist grading, and evaluate the relationship between lung opacity severity and clinical outcomes. Methods: A prospective cross-sectional observational study was conducted on 80 patients with radiographically evident lung opacities at a tertiary care hospital. AI-based image processing software quantified lung opacity extent (%) and generated opacity scores. These were compared with radiologist-assigned opacity grades. Statistical analysis included descriptive statistics, ANOVA, chi-square test, Pearson correlation, and ROC curve analysis. Results: The mean lung opacity extent was 46.59 ± 25.74%. No statistically significant difference in opacity extent was observed across different pulmonary diagnoses (ANOVA, p = 0.489). AI opacity scores showed no significant association with radiologist grading (χ² = 160.0, p = 0.441). Lung opacity extent did not correlate with hospital stay duration (r = 0.001, p = 0.991). ROC analysis demonstrated poor predictive performance of AI opacity score for severity classification (AUC = 0.584). Conclusion: AI-based quantitative lung opacity analysis provides objective measurements but showed limited agreement with radiologist interpretation and poor predictive accuracy for disease severity. Further refinement of AI models and integration with clinical parameters are required to enhance clinical utility.