Using Deep Learning and Adaptive Window Adjustment to Facilitate the Detection of Pulmonary Edema Detection in Chest X-Rays

Cardiogenic pulmonary edema encompasses a diversity of subtypes, each of which requires specific treatment strategies. Physicians must have the ability to rapidly identify edema subtypes in medical images to achieve timely intervention and mitigate lung impairment. This study compared six supervised classification models pre-trained and tested using the MIMIC-CXR dataset in the identification of cardiogenic pulmonary edema subtypes. Note that the same analytic methods were consistently applied across the six parameter sets. Fine-tuning the windowing to L: 2500 and W: 3000 resulted in five AUC values that surpassed 0.8, despite the fact that this did not result in peak accuracy across all test data categories. The predictive accuracy for vascular congestion reached 90%, while that of interstitial and alveolar edema reached 96%. This research holds significant potential for the early diagnosis and targeted treatment of cardiogenic pulmonary edema to enhance the standard of patient care.