Systemic eosinophilic disease presenting as dacryoadenitis.

Eosinophilic disease with orbital involvement is rare. We present two patients with dacryoadenitis associated with local and systemic eosinophilia. A 32-year-old man presented with episodic dacryoadenitis, lower respiratory inflammation and peripheral eosinophilia. Lung and lacrimal gland biopsies demonstrated eosinophilic infiltrate without granuloma, necrosis, or vasculitis. He improved with oral corticosteroids and Mepolizumab, an IL-5 inhibitor. The second case involved a 33-year-old man who similarly presented with episodic dacryoadenitis, pulmonary inflammation and pain/swelling in the hands and feet. Lacrimal gland biopsy demonstrated a predominantly eosinophilic infiltrate without granuloma or vasculitis. Symptoms improved with oral corticosteroids. Although neither patient was provided a definitive diagnosis, both were determined to have an eosinophilic condition on the spectrum of eosinophilic asthma or eosinophilic granulomatosis with polyangiitis.

Ensemble neural network model for detecting thyroid eye disease using external photographs.

PURPOSE: To describe an artificial intelligence platform that detects thyroid eye disease (TED). DESIGN: Development of a deep learning model. METHODS: 1944 photographs from a clinical database were used to train a deep learning model. 344 additional images ('test set') were used to calculate performance metrics. Receiver operating characteristic, precision-recall curves and heatmaps were generated. From the test set, 50 images were randomly selected ('survey set') and used to compare model performance with ophthalmologist performance. 222 images obtained from a separate clinical database were used to assess model recall and to quantitate model performance with respect to disease stage and grade. RESULTS: The model achieved test set accuracy of 89.2%, specificity 86.9%, recall 93.4%, precision 79.7% and an F1 score of 86.0%. Heatmaps demonstrated that the model identified pixels corresponding to clinical features of TED. On the survey set, the ensemble model achieved accuracy, specificity, recall, precision and F1 score of 86%, 84%, 89%, 77% and 82%, respectively. 27 ophthalmologists achieved mean performance of 75%, 82%, 63%, 72% and 66%, respectively. On the second test set, the model achieved recall of 91.9%, with higher recall for moderate to severe (98.2%, n=55) and active disease (98.3%, n=60), as compared with mild (86.8%, n=68) or stable disease (85.7%, n=63). CONCLUSIONS: The deep learning classifier is a novel approach to identify TED and is a first step in the development of tools to improve diagnostic accuracy and lower barriers to specialist evaluation.