Digital Image Analysis of Heterogeneous Tuberculosis Pulmonary Pathology in Non-Clinical Animal Models using Deep Convolutional Neural Networks.

Efforts to develop effective and safe drugs for treatment of tuberculosis require preclinical evaluation in animal models. Alongside efficacy testing of novel therapies, effects on pulmonary pathology and disease progression are monitored by using histopathology images from these infected animals. To compare the severity of disease across treatment cohorts, pathologists have historically assigned a semi-quantitative histopathology score that may be subjective in terms of their training, experience, and personal bias. Manual histopathology therefore has limitations regarding reproducibility between studies and pathologists, potentially masking successful treatments. This report describes a pathologist-assistive software tool that reduces these user limitations, while providing a rapid, quantitative scoring system for digital histopathology image analysis. The software, called 'Lesion Image Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven different pathology features, including three different lesion types from pulmonary tissues of the C3HeB/FeJ tuberculosis mouse model. LIRA was developed to improve the efficiency of histopathology analysis for mouse tuberculosis infection models, this approach has also broader applications to other disease models and tissues. The full source code and documentation is available from https://Github.com/TB-imaging/LIRA.

Utilizing technology to improve intraoperative family communication.

PURPOSE: To create and assess satisfaction with an electronic-medical-record (EMR) integrated communication system designed to optimize perioperative communication with families. METHODS: We built a tool in the EMR's intraoperative nursing navigation screen for sending customized or standardized text pages to families in English or Spanish. Preoperatively, families were given text pagers with instructions and a hospital map to facilitate leaving the waiting area. After 6 months, Press-Ganey™ data and internal surveys from randomly selected families, and all nurses and surgeons were analyzed for satisfaction and effectiveness. RESULTS: Press-Ganey™ data demonstrated 30% improvement in patient satisfaction (p < 0.05). Among families, > 90% indicated pagers were easy to use and provided the desired information during surgery. Of nurses, >90% found the system easy to use and believed it improved families' experience. All surgeons reported improved intraoperative communication and ease of finding families postoperatively. CONCLUSION: Perioperative family communication via EMR-integrated text improves efficiency and family, nurse, and surgeon satisfaction.