Optimizing the Synergistic Potential of Pseudo-Labels from Radiology Notes and Annotated Ground Truth in Identifying Pulmonary Opacities on Chest Radiographs for Early Detection of Acute Respiratory Distress Syndrome.

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening lung injury, hallmarks of which are bilateral radiographic opacities. Studies have shown that early recognition of ARDS could reduce severity and lethal clinical sequela. A Convolutional Neural Network (CNN) model that can identify bilateral pulmonary opacities on chest x-ray (CXR) images can aid early ARDS recognition.

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.

Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis.

Hair-Dye-Related Accidental Poisoning and Death.

Para phenylenediamine (PPD) is a common component of hair dye as well as temporary tattoos and is a well-known cause of type 4 hypersensitivity reactions from topical exposure. While there have been several cases reported in the literature describing toxicities following ingestion, there are a paucity of reports of severe systemic disease following topical exposure. Cases of PPD ingestion have been reported to present with angioedema-like reactions, often progressing to rhabdomyolysis and renal failure.