Resolution of Laryngeal Ulceration with Prednisolone Therapy in a Patient with Crohn's Disease.

A 23-year-old man diagnosed with Crohn's disease (CD) was treated with infliximab. He developed new-onset sore throat and dysphagia during admission, and nasopharyngoscopy revealed epiglottic ulceration. Laryngeal ulceration was considered as an extraintestinal manifestation of CD owing to treatment failure with antibiotics and hydrocortisone. This strongly suggested that laryngeal ulceration was a complication of CD because of the rapid improvement in the symptoms and lesions after prednisolone administration. Furthermore, this treatment process demonstrated the superior anti-inflammatory effect of prednisolone over that of hydrocortisone and supported the assumption of inflammation related to CD.

Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas.

OBJECTIVE: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annotated data available for training. Convolutional neural networks (CNNs) have the potential to improve image analysis and have demonstrated good performance in many settings. This study applied machine-learning technologies to laryngoscopy to determine the accuracy of computer recognition of known laryngeal lesions found in patients post-extubation. METHODS: This is a proof of concept study that used a convenience sample of transnasal, flexible, distal-chip laryngoscopy images from patients post-extubation in the intensive care unit. After manually annotating images at the pixel-level, we applied a CNN-based method for analysis of granulomas and ulcerations to test potential machine-learning approaches for laryngoscopy analysis. RESULTS: A total of 127 images from 25 patients were manually annotated for presence and shape of these lesions-100 for training, 27 for evaluating the system. There were 193 ulcerations (148 in the training set; 45 in the evaluation set) and 272 granulomas (208 in the training set; 64 in the evaluation set) identified. Time to annotate each image was approximately 3 minutes. Machine-based analysis demonstrated per-pixel sensitivity of 82.0% and 62.8% for granulomas and ulcerations respectively; specificity was 99.0% and 99.6%. CONCLUSION: This work demonstrates the feasibility of machine learning via CNN-based methods to add objectivity to laryngoscopy analysis, suggesting that CNN may aid in laryngoscopy analysis for other conditions in the future.