Computer technology applications in surgical implant dentistry: a systematic review

PURPOSE: To assess the literature on accuracy and clinical performance of computer technology applications in surgical implant dentistry. MATERIALS AND METHODS: Electronic and manual literature searches were conducted to collect information about (1) the accuracy and (2) clinical performance of computer-assisted implant systems. Meta-regression analysis was performed for summarizing the accuracy studies. Failure/complication rates were analyzed using random-effects Poisson regression models to obtain summary estimates of 12-month proportions. RESULTS: Twenty-nine different image guidance systems were included. From 2,827 articles, 13 clinical and 19 accuracy studies were included in this systematic review. The meta-analysis of the accuracy (19 clinical and preclinical studies) revealed a total mean error of 0.74 mm (maximum of 4.5 mm) at the entry point in the bone and 0.85 mm at the apex (maximum of 7.1 mm). For the 5 included clinical studies (total of 506 implants) using computer-assisted implant dentistry, the mean failure rate was 3.36% (0% to 8.45%) after an observation period of at least 12 months. In 4.6% of the treated cases, intraoperative complications were reported; these included limited interocclusal distances to perform guided implant placement, limited primary implant stability, or need for additional grafting procedures. CONCLUSION: Differing levels and quantity of evidence were available for computer-assisted implant placement, revealing high implant survival rates after only 12 months of observation in different indications and a reasonable level of accuracy. However, future long-term clinical data are necessary to identify clinical indications and to justify additional radiation doses, effort, and costs associated with computer-assisted implant surgery.

CompAS: A new approach to commonality and variability analysis with applications in computer assisted orthopaedic surgery

In rapidly evolving domains such as Computer Assisted Orthopaedic Surgery (CAOS) emphasis is often put first on innovation and new functionality, rather than in developing the common infrastructure needed to support integration and reuse of these innovations. In fact, developing such an infrastructure is often considered to be a high-risk venture given the volatility of such a domain. We present CompAS, a method that exploits the very evolution of innovations in the domain to carry out the necessary quantitative and qualitative commonality and variability analysis, especially in the case of scarce system documentation. We show how our technique applies to the CAOS domain by using conference proceedings as a key source of information about the evolution of features in CAOS systems over a period of several years. We detect and classify evolution patterns to determine functional commonality and variability. We also identify non-functional requirements to help capture domain variability. We have validated our approach by evaluating the degree to which representative test systems can be covered by the common and variable features produced by our analysis.

State-of-the-art aortic imaging: Part II - applications in transcatheter aortic valve replacement and endovascular aortic aneurysm repair.

Transcatheter aortic valve replacement (TAVR) as well as thoracic and abdominal endovascular aortic repair (TEVAR and EVAR) rely on accurate pre- and postprocedural imaging. This review article discusses the application of imaging, including preprocedural assessment and measurements as well as postprocedural imaging of complications. Furthermore, the exciting perspective of computational fluid dynamics (CFD) based on cross-sectional imaging is presented. TAVR is a minimally invasive alternative for treatment of aortic valve stenosis in patients with high age and multiple comorbidities who cannot undergo traditional open surgical repair. Given the lack of direct visualization during the procedure, pre- and peri-procedural imaging forms an essential part of the intervention. Computed tomography angiography (CTA) is the imaging modality of choice for preprocedural planning. Routine postprocedural follow-up is performed by echocardiography to confirm treatment success and detect complications. EVAR and TEVAR are minimally invasive alternatives to open surgical repair of aortic pathologies. CTA constitutes the preferred imaging modality for both preoperative planning and postoperative follow-up including detection of endoleaks. Magnetic resonance imaging is an excellent alternative to CT for postoperative follow-up, and is especially beneficial for younger patients given the lack of radiation. Ultrasound is applied in screening and postoperative follow-up of abdominal aortic aneurysms, but cross-sectional imaging is required once abnormalities are detected. Contrast-enhanced ultrasound may be as sensitive as CTA in detecting endoleaks.

MR Spectroscopy and Spectroscopic Imaging for Evaluation of Skeletal Muscle Metabolism: Basics and Applications in Metabolic Diseases

Magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) provide metabolic information on the musculoskeletal system, thus helping to understand the biochemical and pathophysiological nature of numerous diseases. In particular, MRS has been used to study the energy metabolism of muscular tissue since the very beginning of magnetic resonance examinations in humans when small-bore magnets for studies of the limbs became available. Even more than in other organs, the observation of non-proton-nuclei was important in muscle tissue. Spatial localization was less demanding in these studies, however, high temporal resolution was necessary to follow metabolism during exercise and recovery. The observation of high-energy phosphates during and after the application of workload gives insight into oxidative phosphorylation, a process that takes place in the mitochondria and characterizes impaired mitochondrial function. New applications in insulin-resistant patients followed the development of volume-selective 1H-MRS in whole-body magnets. Nowadays, multinuclear MRS and MRSI of the musculoskeletal system provide several windows to vital biochemical pathways noninvasively. It is shown how MRS and MRSI have been used in numerous diseases to characterize an involvement of the muscular metabolism.

Service Level Agreements-Driven Management of Distributed Applications in Cloud Computing Environments

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.

On degeneracy and invariances of random fields paths with applications in Gaussian process modelling

We study pathwise invariances and degeneracies of random fields with motivating applications in Gaussian process modelling. The key idea is that a number of structural properties one may wish to impose a priori on functions boil down to degeneracy properties under well-chosen linear operators. We first show in a second order set-up that almost sure degeneracy of random field paths under some class of linear operators defined in terms of signed measures can be controlled through the two first moments. A special focus is then put on the Gaussian case, where these results are revisited and extended to further linear operators thanks to state-of-the-art representations. Several degeneracy properties are tackled, including random fields with symmetric paths, centred paths, harmonic paths, or sparse paths. The proposed approach delivers a number of promising results and perspectives in Gaussian process modelling. In a first numerical experiment, it is shown that dedicated kernels can be used to infer an axis of symmetry. Our second numerical experiment deals with conditional simulations of a solution to the heat equation, and it is found that adapted kernels notably enable improved predictions of non-linear functionals of the field such as its maximum.

Recovery and Analysis of Transaction Scope from Scattered Information in Java Enterprise Applications

Java Enterprise Applications (JEAs) are large systems that integrate multiple technologies and programming languages. Transactions in JEAs simplify the development of code that deals with failure recovery and multi-user coordination by guaranteeing atomicity of sets of operations. The heterogeneous nature of JEAs, however, can obfuscate conceptual errors in the application code, and in particular can hide incorrect declarations of transaction scope. In this paper we present a technique to expose and analyze the application transaction scope in JEAs by merging and analyzing information from multiple sources. We also present several novel visualizations that aid in the analysis of transaction scope by highlighting anomalies in the specification of transactions and violations of architectural constraints. We have validated our approach on two versions of a large commercial case study.

Glucagon-like peptide-1 receptor overexpression in cancer and its impact on clinical applications

Peptide hormones of the glucagon-like peptide (GLP) family play an increasing clinical role, such as GLP-1 in diabetes therapy. Moreover, GLP receptors are overexpressed in various human tumor types and therefore represent molecular targets for important clinical applications. In particular, virtually all benign insulinomas highly overexpress GLP-1 receptors (GLP-1R). Targeting GLP-1R with the stable GLP-1 analogs (111)In-DOTA/DPTA-exendin-4 offers a new approach to successfully localize these small tumors. This non-invasive technique has the potential to replace the invasive localization of insulinomas by selective arterial stimulation and venous sampling. Malignant insulinomas, in contrast to their benign counterparts, express GLP-1R in only one-third of the cases, while they more often express the somatostatin type 2 receptors. Importantly, one of the two receptors appears to be always expressed in malignant insulinomas. The GLP-1R overexpression in selected cancers is worth to be kept in mind with regard to the increasing use of GLP-1 analogs for diabetes therapy. While the functional role of GLP-1R in neoplasia is not known yet, it may be safe to monitor patients undergoing GLP-1 therapy carefully.

Genitourinary applications of diffusion-weighted MR imaging in the pelvis

Diffusion-weighted (DW) magnetic resonance (MR) imaging has a large number of potential clinical applications in the female and male pelvis and can easily be added to any routine MR protocol. In the female pelvis, DW imaging allows improvement of staging in endometrial and cervical cancer, especially in locally advanced disease and in patients in whom contrast medium administration should be avoided. It can also be helpful in characterizing complex adnexal masses and in depicting recurrent tumor after treatment of various gynecologic malignancies. DW imaging shows promising results in monitoring treatment response in patients undergoing radiation therapy of cervical cancer. An increase in apparent diffusion coefficient (ADC) values of responders precedes changes in size and may therefore allow early assessment of treatment success. In the male pelvis, the detection of prostate cancer in the peripheral zone is relatively easier than in the central gland based on the underlying ADC values, whereas overlapping values reported in the central gland still need further research. DW imaging might also be applied in the noninvasive evaluation of bladder cancer to differentiate between superficial and muscle-invasive tumors. Initial promising results have been reported in differentiating benign from malignant pelvic lymph nodes based on the ADC values; however, larger-scale studies will be needed to allow the detection of lymph node metastases in an individual patient. Prerequisites for successfully performing DW imaging of the female and male pelvis are standardization of the DW imaging technique, including the choice of b values, administration of an antiperistaltic drug, and comparison of DW findings with those of morphologic MR imaging.