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. Obtaining large datasets with ground truth labels to train CNNs is challenging, as medical image annotation requires clinical expertise and meticulous consideration. In this work, we implement a natural language processing pipeline that extracts pseudo-labels CXR images by parsing radiology notes for abnormal findings. We obtain ground-truth annotations from clinicians for the presence of pulmonary opacities for a subset of these images. A knowledge distillation-based teacher-student training framework is implemented to leverage the larger dataset with noisy pseudo-labels. Our results show an AUC of 0.93 (95%CI 0.92-0.94) for the prediction of bilateral opacities on chest radiographs.

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. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or 'equivocal' images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the 'equivocal' class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems.

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. To our knowledge, there have only been two reported cases of severe reactions following topical exposure to PPD. We present a case of a 59-year-old man with topical exposure to hair dye who presented with an angioedema-like reaction shortly after topical exposure to PPD containing hair dye that rapidly progressed to rhabdomyolysis, renal failure, and eventually death.