Integrating Battlefield Documentation into Virtual Reality Medical Simulation Training: Virtual Battlefield Assisted Trauma Distributed Observation Kit (BATDOK).

Introduction: Clinical documentation is an essential component of the provision of medical care, enabling continuity of information across provider and site handoffs. This is particularly important in the combat casualty care setting when a single casualty may be treated by four or more or five completely disparate teams across the roles of care. The Battlefield Assisted Trauma Distributed Observation Kit (BATDOK) is a digital battlefield clinical documentation system developed by the Air Force Research Laboratory to address this need.

Federated Learning with Research Prototypes: Application to Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology.

Rationale And Objectives: Early prostate cancer detection and staging from MRI is extremely challenging for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their performance within and across institutions. To enable this for prototype-stage algorithms, where the majority of existing research remains, we introduce a flexible federated learning framework for cross-site training, validation, and evaluation of custom deep learning prostate cancer detection algorithms.

Materials And Methods: We introduce an abstraction of prostate cancer groundtruth that represents diverse annotation and histopathology data.

Harnessing clinical annotations to improve deep learning performance in prostate segmentation.

Purpose: Developing large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. We evaluated the direct use of a large dataset with only clinically generated annotations in development of high-performance segmentation models for small research-quality challenge datasets.

Materials And Methods: We used a large retrospective dataset from our institution comprised of 1,620 clinically generated segmentations, and two challenge datasets (PROMISE12: 50 patients, ProstateX-2: 99 patients).

Optimizing Spatial Biopsy Sampling for the Detection of Prostate Cancer.

Purpose: The appropriate number of systematic biopsy cores to retrieve during magnetic resonance imaging (MRI)-targeted prostate biopsy is not well defined. We aimed to demonstrate a biopsy sampling approach that reduces required core count while maintaining diagnostic performance.

Materials And Methods: We collected data from a cohort of 971 men who underwent MRI-ultrasound fusion targeted biopsy for suspected prostate cancer.

Federated learning improves site performance in multicenter deep learning without data sharing.

Objective: To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).

Materials And Methods: Deep learning models were trained at each participating institution using local clinical data, and an additional model was trained using FL across all of the institutions.

Results: We found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset.

Semi-automated PIRADS scoring via mpMRI analysis.

Prostate cancer (PCa) is the most common solid organ cancer and second leading cause of death in men. Multiparametric magnetic resonance imaging (mpMRI) enables detection of the most aggressive, clinically significant PCa (csPCa) tumors that require further treatment. A suspicious region of interest (ROI) detected on mpMRI is now assigned a Prostate Imaging-Reporting and Data System (PIRADS) score to standardize interpretation of mpMRI for PCa detection.

A domain-level DNA strand displacement reaction enumerator allowing arbitrary non-pseudoknotted secondary structures.

Information technologies enable programmers and engineers to design and synthesize systems of startling complexity that nonetheless behave as intended. This mastery of complexity is made possible by a hierarchy of formal abstractions that span from high-level programming languages down to low-level implementation specifications, with rigorous connections between the levels. DNA nanotechnology presents us with a new molecular information technology whose potential has not yet been fully unlocked in this way.

Estimating the Health-Related Quality of Life of Twitter Users Using Semantic Processing.

Social media presents a rich opportunity to gather health information with limited intervention through the analysis of completely unstructured and unlabeled microposts. We sought to estimate the health-related quality of life (HRQOL) of Twitter users using automated semantic processing methods. We collected tweets from 878 Twitter users recruited through online solicitation and in-person contact with patients.

Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images.

Predicting infarct volume from magnetic resonance perfusion-weighted imaging can provide helpful information to clinicians in deciding how aggressively to treat acute stroke patients. Models have been developed to predict tissue fate, yet these models are mostly built using hand-crafted features (e.g.

Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images.

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head.

An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies.

Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gleason predictions across an entire slide. Deep learning-based segmentation models can automatically learn visual semantics from data, which alleviates the need for feature engineering.

A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies.

Gleason grading of histological images is important in risk assessment and treatment planning for prostate cancer patients. Much research has been done in classifying small homogeneous cancer regions within histological images. However, semi-supervised methods published to date depend on pre-selected regions and cannot be easily extended to an image of heterogeneous tissue composition.

Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies.

Objective: It is crucial for clinicians to stay up to date on current literature in order to apply recent evidence to clinical decision making. Automatic summarization systems can help clinicians quickly view an aggregated summary of literature on a topic. Casama, a representation and summarization system based on "contextualized semantic maps," captures the findings of biomedical studies as well as the contexts associated with patient population and study design.

Patient Portal Preferences: Perspectives on Imaging Information.

Patient portals have the potential to provide content that is specifically tailored to a patient's information needs based on diagnoses and other factors. In this work, we conducted a survey of 41 lung cancer patients at an outpatient lung cancer clinic at the medical center of the University of California Los Angeles, to gain insight into these perceived information needs and opinions on the design of a portal to fulfill them. We found that patients requested access to information related to diagnosis and imaging, with more than half of the patients reporting that they did not anticipate an increase in anxiety due to access to medical record information via a portal.

Imaging-histologic discordance at percutaneous biopsy of the lung.

Rationale And Objectives: The purpose of this study was to quantify the degree of imaging-histologic discordance in a cohort of patients undergoing computed tomography (CT)-guided lung biopsy for focal lung disease.

Materials And Methods: A retrospective review was performed of 186 patients who underwent percutaneous lung biopsy of a parenchymal lesion at our institution between January and December 2009. Diagnostic radiology reports of CT or positron emission tomography-CTs performed before biopsy were used to classify the lesion as malignant or benign by five readers.

Data model for personalized patient health guidelines: an exploratory study.

Practitioner guidelines simultaneously provide broad overviews and in-depth details of disease. Written for experts, they are difficult for patients to understand, yet patients often use these guidelines as a source of information to help them to learn about their health. Using practitioner guidelines along with patient information needs and preferences, we created a method to design an information model for providing patients access to their personal health information, linked to individualized, relevant supporting information from guidelines within a patient portal.

Clinical uncertainties, health service challenges, and ethical complexities of HIV "test-and-treat": a systematic review.

Despite the HIV "test-and-treat" strategy's promise, questions about its clinical rationale, operational feasibility, and ethical appropriateness have led to vigorous debate in the global HIV community. We performed a systematic review of the literature published between January 2009 and May 2012 using PubMed, SCOPUS, Global Health, Web of Science, BIOSIS, Cochrane CENTRAL, EBSCO Africa-Wide Information, and EBSCO CINAHL Plus databases to summarize clinical uncertainties, health service challenges, and ethical complexities that may affect the test-and-treat strategy's success. A thoughtful approach to research and implementation to address clinical and health service questions and meaningful community engagement regarding ethical complexities may bring us closer to safe, feasible, and effective test-and-treat implementation.