A Recommender System Based on Cohorts' Similarity.

Aiming to better understand the genetic and environmental associations of Alzheimer's disease, many clinical trials and scientific studies have been conducted. However, these studies are often based on a small number of participants. To address this limitation, there is an increasing demand of multi-cohorts studies, which can provide higher statistical power and clinical evidence. However, this data integration implies dealing with the diversity of cohorts structures and the wide variability of concepts. Moreover, discovering similar cohorts to extend a running study is typically a demanding task. In this paper, we present a recommendation system to allow finding similar cohorts based on profile interests. The method uses collaborative filtering mixed with context-based retrieval techniques to find relevant cohorts on scientific literature about Alzheimer's diseases. The method was validated in a set of 62 cohorts.

Controlled searching in reversibly de-identified medical imaging archives.

Nowadays, digital medical imaging in healthcare has become a fundamental tool for medical diagnosis. This growth has been accompanied by the development of technologies and standards, such as the DICOM standard and PACS. This environment led to the creation of collaborative projects where there is a need to share medical data between different institutions for research and educational purposes. In this context, it is necessary to maintain patient data privacy and provide an easy and secure mechanism for authorized personnel access. This paper presents a solution that fully de-identifies standard medical imaging objects, including metadata and pixel data, providing at the same time a reversible de-identifier mechanism that retains search capabilities from the original data. The last feature is important in some scenarios, for instance, in collaborative platforms where data is anonymized when shared with the community but searchable for data custodians or authorized entities. The solution was integrated into an open source PACS archive and validated in a multidisciplinary collaborative scenario.

A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format.

Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community.

A Cloud Architecture for Teleradiology-as-a-Service.

BACKGROUND: Telemedicine has been promoted by healthcare professionals as an efficient way to obtain remote assistance from specialised centres, to get a second opinion about complex diagnosis or even to share knowledge among practitioners. The current economic restrictions in many countries are increasing the demand for these solutions even more, in order to optimize processes and reduce costs. However, despite some technological solutions already in place, their adoption has been hindered by the lack of usability, especially in the set-up process. OBJECTIVES: In this article we propose a telemedicine platform that relies on a cloud computing infrastructure and social media principles to simplify the creation of dynamic user-based groups, opening up opportunities for the establishment of teleradiology trust domains. METHODS: The collaborative platform is provided as a Software-as-a-Service solution, supporting real time and asynchronous collaboration between users. To evaluate the solution, we have deployed the platform in a private cloud infrastructure. The system is made up of three main components - the collaborative framework, the Medical Management Information System (MMIS) and the HTML5 (Hyper Text Markup Language) Web client application - connected by a message-oriented middleware. RESULTS: The solution allows physicians to create easily dynamic network groups for synchronous or asynchronous cooperation. The network created improves dataflow between colleagues and also knowledge sharing and cooperation through social media tools. The platform was implemented and it has already been used in two distinct scenarios: teaching of radiology and tele-reporting. CONCLUSIONS: Collaborative systems can simplify the establishment of telemedicine expert groups with tools that enable physicians to improve their clinical practice. Streamlining the usage of this kind of systems through the adoption of Web technologies that are common in social media will increase the quality of current solutions, facilitating the sharing of clinical information, medical imaging studies and patient diagnostics among collaborators.

A recommender system for medical imaging diagnostic.

The large volume of data captured daily in healthcare institutions is opening new and great perspectives about the best ways to use it towards improving clinical practice. In this paper we present a context-based recommender system to support medical imaging diagnostic. The system relies on data mining and context-based retrieval techniques to automatically lookup for relevant information that may help physicians in the diagnostic decision.

A machine learning methodology for medical imaging anonymization.

Privacy protection is a major requirement for the complete success of EHR systems, becoming even more critical in collaborative scenarios, where data is shared among institutions and practitioners. While textual data can be easily de-identified, patient data in medical images implies a more elaborate approach. In this work we present a solution for sensitive word identification in medical images based on a combination of two machine-learning models, achieving a F1-score of 0.94. Three experts evaluated the system performance. They analyzed the output of the present methodology and categorized the studies in three groups: studies that had their sensitive words removed (true positive), studies with complete patient identity (false negative) and studies with mistakenly removed data (false positive). The experts were unanimous regarding the relevance of the present tool in collaborative medical environments, as it may improve the exchange of anonymized patient data between institutions.

Dicoogle Mobile: a medical imaging platform for Android.

Mobile computing technologies are increasingly becoming a valuable asset in healthcare information systems. The adoption of these technologies helps to assist in improving quality of care, increasing productivity and facilitating clinical decision support. They provide practitioners with ubiquitous access to patient records, being actually an important component in telemedicine and tele-work environments. We have developed Dicoogle Mobile, an Android application that provides remote access to distributed medical imaging data through a cloud relay service. Besides, this application has the capability to store and index local imaging data, so that they can also be searched and visualized. In this paper, we will describe Dicoogle Mobile concept as well the architecture of the whole system that makes it running.