Diagnosis of Interproximal Caries Lesions in Bitewing Radiographs Using a Deep Convolutional Neural Network-Based Software.

The aim of this study was to evaluate the diagnostic reliability of a web-based artificial intelligence program for the detection of interproximal caries in bitewing radiographs. Three hundred bitewing radiographs of patients were subjected to the evaluation of a convolutional neural network. First, the images were visually evaluated by a previously trained and calibrated operator with radiodiagnosis experience.

Using Machine Learning to Collect and Facilitate Remote Access to Biomedical Databases: Development of the Biomedical Database Inventory.

Background: Currently, existing biomedical literature repositories do not commonly provide users with specific means to locate and remotely access biomedical databases.

Objective: To address this issue, we developed the Biomedical Database Inventory (BiDI), a repository linking to biomedical databases automatically extracted from the scientific literature. BiDI provides an index of data resources and a path to access them seamlessly.

genoDraw: A Web Tool for Developing Pedigree Diagrams Using the Standardized Human Pedigree Nomenclature Integrated with Biomedical Vocabularies.

The integration of genetic information in current clinical routine has raised a need for tools to exploit family genetic knowledge. On the clinical side, an application for managing and visualizing pedigree diagrams could provide genetics specialists with an integrated environment with potential positive impact on their current practice. This article presents a web tool (genoDraw) that provides clinical practitioners with the ability to create, maintain and visualize patients' and their families' information in the form of pedigree diagrams.

Carbon Nanotubes' Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra.

This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux () under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria.

Querying clinical data in HL7 RIM based relational model with morph-RDB.

Background: Semantic interoperability is essential when carrying out post-genomic clinical trials where several institutions collaborate, since researchers and developers need to have an integrated view and access to heterogeneous data sources. One possible approach to accommodate this need is to use RDB2RDF systems that provide RDF datasets as the unified view. These RDF datasets may be materialized and stored in a triple store, or transformed into RDF in real time, as virtual RDF data sources.

A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer.

Introduction: The introduction of omics data and advances in technologies involved in clinical treatment has led to a broad range of approaches to represent clinical information. Within this context, patient stratification across health institutions due to omic profiling presents a complex scenario to carry out multi-center clinical trials.

Methods: This paper presents a standards-based approach to ensure semantic integration required to facilitate the analysis of clinico-genomic clinical trials.

Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL.

This paper describes a new Cohort Selection application implemented to support streamlining the definition phase of multi-centric clinical research in oncology. Our approach aims at both ease of use and precision in defining the selection filters expressing the characteristics of the desired population. The application leverages our standards-based Semantic Interoperability Solution and a Groovy DSL to provide high expressiveness in the definition of filters and flexibility in their composition into complex selection graphs including splits and merges.

Enabling semantic interoperability in multi-centric clinical trials on breast cancer.

Background And Objectives: Post-genomic clinical trials require the participation of multiple institutions, and collecting data from several hospitals, laboratories and research facilities. This paper presents a standard-based solution to provide a uniform access endpoint to patient data involved in current clinical research.

Methods: The proposed approach exploits well-established standards such as HL7 v3 or SPARQL and medical vocabularies such as SNOMED CT, LOINC and HGNC.

Semantic Normalization and Query Abstraction Based on SNOMED-CT and HL7: Supporting Multicentric Clinical Trials.

Advances in the use of omic data and other biomarkers are increasing the number of variables in clinical research. Additional data have stratified the population of patients and require that current studies be performed among multiple institutions. Semantic interoperability and standardized data representation are a crucial task in the management of modern clinical trials.

A data model based on semantically enhanced HL7 RIM for sharing patient data of breast cancer clinical trials.

Breast cancer clinical trial researchers have to handle heterogeneous data coming from different data sources, overloading biomedical researchers when they need to query data for retrospective analysis. This paper presents the Common Data Model (CDM) proposed within the INTEGRATE EU project to homogenize data coming from different clinical partners. This CDM is based on the Reference Information Model (RIM) from the Health Level 7 (HL7) version 3.