Medical 3D printing: methods to standardize terminology and report trends.

BackgroundMedical 3D printing is expanding exponentially, with tremendous potential yet to be realized in nearly all facets of medicine. Unfortunately, multiple informal subdomain-specific isolated terminological 'silos' where disparate terminology is used for similar concepts are also arising as rapidly. It is imperative to formalize the foundational terminology at this early stage to facilitate future knowledge integration, collaborative research, and appropriate reimbursement. The purpose of this work is to develop objective, literature-based consensus-building methodology for the medical 3D printing domain to support expert consensus.ResultsWe first quantitatively survey the temporal, conceptual, and geographic diversity of all existing published applications within medical 3D printing literature and establish the existence of self-isolating research clusters. We then demonstrate an automated objective methodology to aid in establishing a terminological consensus for the field based on objective analysis of the existing literature. The resultant analysis provides a rich overview of the 3D printing literature, including publication statistics and trends globally, chronologically, technologically, and within each major medical discipline. The proposed methodology is used to objectively establish the dominance of the term "3D printing" to represent a collection of technologies that produce physical models in the medical setting. We demonstrate that specific domains do not use this term in line with objective consensus and call for its universal adoption.ConclusionOur methodology can be applied to the entirety of medical 3D printing literature to obtain a complete, validated, and objective set of recommended and synonymous definitions to aid expert bodies in building ontological consensus

Prototype semantic infrastructure for automated small molecule classification and annotation in lipidomics

<p>Abstract</p> <p>Background</p> <p>The development of high-throughput experimentation has led to astronomical growth in biologically relevant lipids and lipid derivatives identified, screened, and deposited in numerous online databases. Unfortunately, efforts to annotate, classify, and analyze these chemical entities have largely remained in the hands of human curators using manual or semi-automated protocols, leaving many novel entities unclassified. Since chemical function is often closely linked to structure, accurate structure-based classification and annotation of chemical entities is imperative to understanding their functionality.</p> <p>Results</p> <p>As part of an exploratory study, we have investigated the utility of semantic web technologies in automated chemical classification and annotation of lipids. Our prototype framework consists of two components: an ontology and a set of federated web services that operate upon it. The formal lipid ontology we use here extends a part of the LiPrO ontology and draws on the lipid hierarchy in the LIPID MAPS database, as well as literature-derived knowledge. The federated semantic web services that operate upon this ontology are deployed within the Semantic Annotation, Discovery, and Integration (SADI) framework. Structure-based lipid classification is enacted by two core services. Firstly, a structural annotation service detects and enumerates relevant functional groups for a specified chemical structure. A second service reasons over lipid ontology class descriptions using the attributes obtained from the annotation service and identifies the appropriate lipid classification. We extend the utility of these core services by combining them with additional SADI services that retrieve associations between lipids and proteins and identify publications related to specified lipid types. We analyze the performance of SADI-enabled eicosanoid classification relative to the LIPID MAPS classification and reflect on the contribution of our integrative methodology in the context of high-throughput lipidomics.</p> <p>Conclusions</p> <p>Our prototype framework is capable of accurate automated classification of lipids and facile integration of lipid class information with additional data obtained with SADI web services. The potential of programming-free integration of external web services through the SADI framework offers an opportunity for development of powerful novel applications in lipidomics. We conclude that semantic web technologies can provide an accurate and versatile means of classification and annotation of lipids.</p

3D printed ventricular septal defect patch: a primer for the 2015 Radiological Society of North America (RSNA) hands-on course in 3D printing.

Hand-held three dimensional models of the human anatomy and pathology, tailored-made protheses, and custom-designed implants can be derived from imaging modalities, most commonly Computed Tomography (CT). However, standard DICOM format images cannot be 3D printed; instead, additional image post-processing is required to transform the anatomy of interest into Standard Tessellation Language (STL) format is needed. This conversion, and the subsequent 3D printing of the STL file, requires a series of steps. Initial post-processing involves the segmentation-demarcation of the desired for 3D printing parts and creating of an initial STL file. Then, Computer Aided Design (CAD) software is used, particularly for wrapping, smoothing and trimming. Devices and implants that can also be 3D printed, can be designed using this software environment. The purpose of this article is to provide a tutorial on 3D Printing with the test case of complex congenital heart disease (CHD). While the infant was born with double outlet right ventricle (DORV), this hands-on guide to be featured at the 2015 annual meeting of the Radiological Society of North America Hands-on Course in 3D Printing focused on the additional finding of a ventricular septal defect (VSD). The process of segmenting the heart chambers and the great vessels will be followed by optimization of the model using CAD software. A virtual patch that accurately matches the patient's VSD will be designed and both models will be prepared for 3D printing

Self-organizing ontology of biochemically relevant small molecules

<p>Abstract</p> <p>Background</p> <p>The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest.</p> <p>Results</p> <p>To address this, we present a formal framework based on Semantic Web technologies for the automatic design of chemical ontology which can be used for automated classification of novel entities. We demonstrate the automatic self-assembly of a structure-based chemical ontology based on 60 MeSH and 40 ChEBI chemical classes. This ontology is then used to classify 200 compounds with an accuracy of 92.7%. We extend these structure-based classes with molecular feature information and demonstrate the utility of our framework for classification of functionally relevant chemicals. Finally, we discuss an iterative approach that we envision for future biochemical ontology development.</p> <p>Conclusions</p> <p>We conclude that the proposed methodology can ease the burden of chemical data annotators and dramatically increase their productivity. We anticipate that the use of formal logic in our proposed framework will make chemical classification criteria more transparent to humans and machines alike and will thus facilitate predictive and integrative bioactivity model development.</p

ClassyFire: automated chemical classification with a comprehensive, computable taxonomy

Additional file 5. Use cases. Text-based search on the ClassyFire web server. (A) Building the query. (B) Sparteine, one of the returned compounds

Clinical situations for which 3D Printing is considered an appropriate representation or extension of data contained in a medical imaging examination: Vascular conditions

BACKGROUND: Medical three-dimensional (3D) printing has demonstrated utility and value in anatomic models for vascular conditions. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (3DPSIG) provides appropriateness recommendations for vascular 3D printing indications. METHODS: A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with vascular indications. Each study was vetted by the authors and strength of evidence was assessed according to published appropriateness ratings. RESULTS: Evidence-based recommendations for when 3D printing is appropriate are provided for the following areas: aneurysm, dissection, extremity vascular disease, other arterial diseases, acute venous thromboembolic disease, venous disorders, lymphedema, congenital vascular malformations, vascular trauma, vascular tumors, visceral vasculature for surgical planning, dialysis access, vascular research/development and modeling, and other vasculopathy. Recommendations are provided in accordance with strength of evidence of publications corresponding to each vascular condition combined with expert opinion from members of the 3DPSIG. CONCLUSION: This consensus appropriateness ratings document, created by the members of the 3DPSIG, provides an updated reference for clinical standards of 3D printing for the care of patients with vascular conditions

Radiological Society of North America (RSNA) 3D Printing Special Interest Group (SIG) clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: breast conditions.

The use of medical 3D printing has expanded dramatically for breast diseases. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides updated appropriateness criteria for breast 3D printing in various clinical scenarios. Evidence-based appropriateness criteria are provided for the following clinical scenarios: benign breast lesions and high-risk breast lesions, breast cancer, breast reconstruction, and breast radiation (treatment planning and radiation delivery)

Semantic Web integration of Cheminformatics resources with the SADI framework

<p>Abstract</p> <p>Background</p> <p>The diversity and the largely independent nature of chemical research efforts over the past half century are, most likely, the major contributors to the current poor state of chemical computational resource and database interoperability. While open software for chemical format interconversion and database entry cross-linking have partially addressed database interoperability, computational resource integration is hindered by the great diversity of software interfaces, languages, access methods, and platforms, among others. This has, in turn, translated into limited reproducibility of computational experiments and the need for application-specific computational workflow construction and semi-automated enactment by human experts, especially where emerging interdisciplinary fields, such as systems chemistry, are pursued. Fortunately, the advent of the Semantic Web, and the very recent introduction of RESTful Semantic Web Services (SWS) may present an opportunity to integrate all of the existing computational and database resources in chemistry into a machine-understandable, unified system that draws on the entirety of the Semantic Web.</p> <p>Results</p> <p>We have created a prototype framework of Semantic Automated Discovery and Integration (SADI) framework SWS that exposes the QSAR descriptor functionality of the Chemistry Development Kit. Since each of these services has formal ontology-defined input and output classes, and each service consumes and produces RDF graphs, clients can automatically reason about the services and available reference information necessary to complete a given overall computational task specified through a simple SPARQL query. We demonstrate this capability by carrying out QSAR analysis backed by a simple formal ontology to determine whether a given molecule is drug-like. Further, we discuss parameter-based control over the execution of SADI SWS. Finally, we demonstrate the value of computational resource envelopment as SADI services through service reuse and ease of integration of computational functionality into formal ontologies.</p> <p>Conclusions</p> <p>The work we present here may trigger a major paradigm shift in the distribution of computational resources in chemistry. We conclude that envelopment of chemical computational resources as SADI SWS facilitates interdisciplinary research by enabling the definition of computational problems in terms of ontologies and formal logical statements instead of cumbersome and application-specific tasks and workflows.</p

Radiological Society of North America (RSNA) 3D printing Special Interest Group (SIG) clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: Breast conditions

The use of medical 3D printing has expanded dramatically for breast diseases. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides updated appropriateness criteria for breast 3D printing in various clinical scenarios. Evidence-based appropriateness criteria are provided for the following clinical scenarios: benign breast lesions and high-risk breast lesions, breast cancer, breast reconstruction, and breast radiation (treatment planning and radiation delivery)