AI-Assisted Chest X-Ray Reading Improves Sensitivity Without Reducing Specificity: A Crossover Study
| Autoři | |
|---|---|
| Rok publikování | 2026 |
| Druh | Stať ve sborníku |
| Konference | ICT for Intelligent Systems (ICTIS 2025) |
| Citace | |
| Doi | https://doi.org/10.1007/978-981-95-1353-6_38 |
| Popis | The growing demand for chest radiography, combined with radiologist shortages and increasing workloads, underscores the need for innovative diagnostic support tools. This crossover study evaluates the impact of a commercially available deep learning-based automatic detection software (DLAD) on radiologists' performance in interpreting chest X-rays (CXRs). Five radiologists independently assessed a dataset of 540 anonymized CXRs in two phases—without and with DLAD assistance—separated by a 30-day washout period. DLAD support significantly improved diagnostic performance: overall sensitivity increased from 0.762 (95% CI: 0.705–0.811) to 0.911 (0.870–0.941, p < 0.001), while specificity remained unchanged at 0.850 (0.805–0.887, p = 0.331). The positive predictive value (PPV) rose slightly from 0.810 (0.755–0.856) to 0.836 (0.788–0.876, p = 0.331), and the negative predictive value (NPV) improved from 0.810 (0.763–0.850) to 0.941 (0.882–0.947, p < 0.001). These improvements were consistent across all readers, with a marked reduction in false negatives. The findings demonstrate DLAD’s potential to enhance diagnostic accuracy, increase sensitivity, and support radiologists in chest X-ray interpretation without compromising specificity. |