Hand osteoarthritis: clinical phenotypes, molecular mechanisms and disease management

Osteoarthritis (OA) is a highly prevalent condition and the hand is the most commonly affected site. Patients with hand OA frequently report symptoms of pain, functional limitations, and frustration in undertaking everyday activities. The condition presents clinically with changes to the bone, ligaments, cartilage and synovial tissue, which can be observed using radiography, ultrasonography or MRI. Hand OA is a heterogeneous disorder and is considered to be multifactorial in aetiology. This review provides an overview of the epidemiology, presentation and burden of hand OA, including an update on hand OA imaging (including the development of novel techniques), disease mechanisms and management. In particular, areas for which new evidence has substantially changed the way we understand, consider and treat hand OA are highlighted. For example, genetic studies, clinical trials and careful prospective imaging studies from the past 5 years are beginning to provide insights into the pathogenesis of hand OA that might uncover new therapeutic targets in disease

An Integrated Building Management System for the WPI Campus

The goal of this project was to create an integrated computer information system for the Plant Services Department at WPI to assist in making decisions related to the safety and efficient operation of the buildings on the WPI campus. This was accomplished by determining the requirements of WPI building managers, analyzing the suitability of the existing data, and designing and validating a prototype of a building management system.

Towards Vocabulary Development by Convention

A major bottleneck for a wider deployment and use of ontologies and knowledge engineering techniques is the lack of established conventions along with cumbersome and inefficient support for vocabulary and ontology authoring. We argue, that the pragmatic development by convention paradigm well-accepted within software engineering, can be successfully applied for ontology engineering, too. However, the definition of a valid set of conventions requires broadly-accepted best-practices. In this regard, we empirically analyzed a number of popular vocabularies and ontology development efforts with respect to their use of guidelines and common practices. Based on this analysis, we identified the following main aspects of common practices: documentation, internationalization, naming, structure, reuse, validation and authoring. In this paper, these aspects are presented and discussed in detail. We propose a set of practices for each aspect and evaluate their relevance in a study with vocabulary developers. The overall goal is to pave the way for a new paradigm of vocabulary development similar to Software Development by Convention, which we name Vocabulary Development by Convention

Gender and Age Distribution of Thyroid Nodules using Imaging Diagnostic and Management Modalities in Albania; Our data

Purpose: The purpose of this study was to evaluate gender and age distribution and the importance of imaging modalities in the evaluation and management of thyroid nodular disease in Albania nowadays. Material and method: The study analyzed the results of 310 patients (N=257 women; N=53 men), assessed at U. O. Clinic, American Hospital and Hygiea Hospital, Tirana, Albania during one year period; May 2015 – May 2016. The average age in the sample was 45.6 years. Thyroid nodules were found in 221 cases (185 females and 36 males). The patients had a standard ultrasound (US) of the thyroid; scintigraphy (SC) and ultrasound-guide fine needle aspiration biopsy (US-FNAB) when indicated. Results: The study shows that nodular disease of the thyroid was found in 84 % of women patients, and in 16 % of men patients. The largest presence of nodules was at the age group of 41-49 years. Moreover, this survey showed that ultrasound can help to distinguish nodules that are definitely benign and those with suspicious features that may require further investigation under FNAB. Some statistically significant connections were found between ultrasound characteristics and FNAB. Conclusion: Females were vast majority of patients as in regards of gender with 84 % of total sample population in the study of 310 subjects. And group age of 41-49 years old were more affected. Ultrasound and other imaging modalities are reliable methods for diagnosis and management of thyroid nodules, including patients' selection to undergo through FNAB. SC was indicated to evaluate the functional features of a nodule, especially in the presence of a low TSH value. In Albania these modalities are now used by many trained medical staff thus offering quality medical service to our patients over all. Keywords: Thyroid nodule, malignant, ultrasound, FNAB DOI: 10.7176/ALST/92-04 Publication date: February 28th 202

Version Control and Change Validation for RDF Datasets

The dynamic and distributed nature of the Semantic Web demands for methodologies and systems fostering collective participation to the evolution of datasets. In collaborative and iterative processes for dataset development, it is important to keep track of individual changes for provenance. Different scenarios may require mechanisms to foster consensus, resolve conflicts between competing changes, reversing or ignoring changes etc. In this paper, we perform a landscape analysis of version control for RDF datasets, emphasizing the importance of change reversion to support validation. Firstly, we discuss different representations of changes in RDF datasets and introduce higher-level perspectives on change. Secondly, we analyze diverse approaches to version control. We conclude by focusing on validation, characterizing it as a separate need from the mere preservation of different versions of a dataset

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework

The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning-the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework

peer reviewedThe advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects

Automated Classification of Radiographic Knee Osteoarthritis Severity Using Deep Neural Networks

Purpose: To develop an automated model for staging knee osteoarthritis severity from radiographs and to compare its performance to that of musculoskeletal radiologists. Materials and Methods: Radiographs from the Osteoarthritis Initiative staged by a radiologist committee using the Kellgren-Lawrence (KL) system were used. Before using the images as input to a convolutional neural network model, they were standardized and augmented automatically. The model was trained with 32 116 images, tuned with 4074 images, evaluated with a 4090-image test set, and compared to two individual radiologists using a 50-image test subset. Saliency maps were generated to reveal features used by the model to determine KL grades. Results: With committee scores used as ground truth, the model had an average F1 score of 0.70 and an accuracy of 0.71 for the full test set. For the 50-image subset, the best individual radiologist had an average F1 score of 0.60 and an accuracy of 0.60; the model had an average F1 score of 0.64 and an accuracy of 0.66. Cohen weighted κ between the committee and model was 0.86, comparable to intraexpert repeatability. Saliency maps identified sites of osteophyte formation as influential to predictions. Conclusion: An end-to-end interpretable model that takes full radiographs as input and predicts KL scores with state-of-the-art accuracy, performs as well as musculoskeletal radiologists, and does not require manual image preprocessing was developed. Saliency maps suggest the model's predictions were based on clinically relevant information. Supplemental material is available for this article. © RSNA, 2020

Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate Cancer Incorporating an In Silico Clinical Trial and a Cost-Utility Study

SIMPLE SUMMARY: Low–intermediate prostate cancer has a number of viable treatment options, such as radical prostatectomy and radiotherapy, with similar survival outcomes but different treatment-related side effects. The aim of this study is to facilitate patient-specific treatment selection by developing a decision support system (DSS) that incorporates predictive models for cancer-free survival and treatment-related side effects. We challenged this DSS by validating it against randomized clinical trials and assessing the benefit through a cost–utility analysis. We aim to expand upon the applications of this DSS by using it as the basis for an in silico clinical trial for an underrepresented patient group. This modeling study shows that DSS-based treatment decisions will result in a clinically relevant increase in the patients’ quality of life and can be used for in silico trials. ABSTRACT: The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213–433)) and more quality-adjusted life years (QALYs; 0.11 years, 95% CI: 0.00–0.22) than randomized treatment selection