Informatics to support patient choice between diverse medical systems

Culturally, philosophically and religiously diverse medical systems including Western medicine, Traditional Chinese Medicine, Ayurvedic Medicine and Homeopathic Medicine, once situated in places and times relatively unconnected from each other, currently co-exist to a point where patients must choose which system to consult. These decisions require comparative analyses, yet the divergence in key underpinning assumptions is so great that comparisons cannot easily be made. However, diverse medical systems can be meaningfully juxtaposed for the purpose of making practical decisions if relevant information is presented appropriately. Information regarding privacy provisions inherent in the typical practice of each medical system is an important element in this juxtaposition. In this paper the information needs of patients making decisions regarding the selection of a medical system are examined.

Monologue or Intercultural Dialogue Between Medical Systems? An Educational Challenge for Medical Sciences

Each medical cultural system constructs knowledge about health through specialization or interculturalism. The knowledge constructed through interculturalism has sought, mainly, to adapt the delivery of health care services to the users’ cultural referents. This emphasis has overlooked the opportunities embedded in the establishment of intercultural relationships between medical systems based on dialogue, especially in regard to the adjustment of the disciplinary boundaries of medical cultural systems that would allow the construction of new knowledge on health. This absence of dialogue has been determined by epistemological barriers inherent to every system as well as by social domination. This article presents some concepts related to cognition processes which encourage the reflection on the possibilities to overcome such barriers so that the health sciences may contribute to the effective implementation of the World Health Organization and the State’s recommendations on the matter.

The Effect of emergency medical systems on prehospital cardiovascular care : an evaluation of studies and an inventory of data bases.

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Multimodal Man-machine Interface and Virtual Reality for Assistive Medical Systems

The results of research the intelligence multimodal man-machine interface and virtual reality means for assistive medical systems including computers and mechatronic systems (robots) are discussed. The gesture translation for disability peoples, the learning-by-showing technology and virtual operating room with 3D visualization are presented in this report and were announced at International exhibition "Intelligent and Adaptive Robots–2005".

Using narrow beam profiles to quantify focal spot size, for accurate Monte Carlo simulations of SRS/SRT systems

This study investigates the variation of photon field penumbra shape with initial electron beam diameter, for very narrow beams. A Varian Millenium MLC (Varian Medical Systems, Palo Alto, USA) and a Brainlab m3 microMLC (Brainlab AB. Feldkirchen, Germany) were used, with one Varian iX linear accelerator, to produce fields that were (nominally) 0.20 cm across. Dose profiles for these fields were measured using radiochromic film and compared with the results of simulations completed using BEAMnrc and DOSXYZnrc, where the initial electron beam was set to FWHM = 0.02, 0.10, 0.12, 0.15, 0.20 and 0.50 cm. Increasing the electron-beam FWHM produced increasing occlusion of the photon source by the closely spaced collimator leaves and resulted in blurring of the simulated profile widths from 0.26 to 0.64 cm, for the MLC, from 0.12 to 0.43 cm, for the microMLC. Comparison with measurement data suggested that the electron spot size in the clinical linear accelerator was between FWHM = 0.10 and 0.15 cm, encompassing the result of our previous output-factor based work, which identified a FWHM of 0.12. Investigation of narrow-beam penumbra variation has been found to be a useful procedure, with results varying noticeably with linear accelerator spot size and allowing FWHM estimates obtained using other methods to be verified.

Mechanical characterization of the varian Exact-arm and R-arm support systems for eight aS500 electronic portal imaging devices

The aim of this study is to compare the positioning accuracy at different gantry angles of two electronic portal imaging devices (EPIDs) support arm systems by using EPID difference images as a measure for displacement. This work presents a comparison of the mechanical performance of eight Varian aS500 (Varian Medical Systems, Palo Alto, CA) EPIDs, mounted using either the Varian Exact-arm or R-arm.

Wearable inkjet-printed antenna performance for medical applications at 868/915 MHz

The use of biosensors attached to the body for health monitoring is now readily accepted, and the merits of such systems and their potential impact on healthcare receive much attention. Wearable medical systems used in clinical applications to monitor vital signs must be comfortable to wear, yet have robust performance to ensure reliable communications links. Additionally, and vital to the success of these innovations, is that these solutions are disposable to avoid risk of patient infection and this means that they must be ultra-low cost. Antennas optimized for printing using conductive inks offer new exciting advances in making a truly disposable solution.

Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

The development of a Monte Carlo system to verify radiotherapy treatment dose calculations

Recent advances in the planning and delivery of radiotherapy treatments have resulted in improvements in the accuracy and precision with which therapeutic radiation can be administered. As the complexity of the treatments increases it becomes more difficult to predict the dose distribution in the patient accurately. Monte Carlo methods have the potential to improve the accuracy of the dose calculations and are increasingly being recognised as the “gold standard” for predicting dose deposition in the patient. In this study, software has been developed that enables the transfer of treatment plan information from the treatment planning system to a Monte Carlo dose calculation engine. A database of commissioned linear accelerator models (Elekta Precise and Varian 2100CD at various energies) has been developed using the EGSnrc/BEAMnrc Monte Carlo suite. Planned beam descriptions and CT images can be exported from the treatment planning system using the DICOM framework. The information in these files is combined with an appropriate linear accelerator model to allow the accurate calculation of the radiation field incident on a modelled patient geometry. The Monte Carlo dose calculation results are combined according to the monitor units specified in the exported plan. The result is a 3D dose distribution that could be used to verify treatment planning system calculations. The software, MCDTK (Monte Carlo Dicom ToolKit), has been developed in the Java programming language and produces BEAMnrc and DOSXYZnrc input files, ready for submission on a high-performance computing cluster. The code has been tested with the Eclipse (Varian Medical Systems), Oncentra MasterPlan (Nucletron B.V.) and Pinnacle3 (Philips Medical Systems) planning systems. In this study the software was validated against measurements in homogenous and heterogeneous phantoms. Monte Carlo models are commissioned through comparison with quality assurance measurements made using a large square field incident on a homogenous volume of water. This study aims to provide a valuable confirmation that Monte Carlo calculations match experimental measurements for complex fields and heterogeneous media.

The development of Monte Carlo techniques for the verification of radiotherapy treatments

Introduction: Recent advances in the planning and delivery of radiotherapy treatments have resulted in improvements in the accuracy and precision with which therapeutic radiation can be administered. As the complexity of the treatments increases it becomes more difficult to predict the dose distribution in the patient accurately. Monte Carlo (MC) methods have the potential to improve the accuracy of the dose calculations and are increasingly being recognised as the ‘gold standard’ for predicting dose deposition in the patient [1]. This project has three main aims: 1. To develop tools that enable the transfer of treatment plan information from the treatment planning system (TPS) to a MC dose calculation engine. 2. To develop tools for comparing the 3D dose distributions calculated by the TPS and the MC dose engine. 3. To investigate the radiobiological significance of any errors between the TPS patient dose distribution and the MC dose distribution in terms of Tumour Control Probability (TCP) and Normal Tissue Complication Probabilities (NTCP). The work presented here addresses the first two aims. Methods: (1a) Plan Importing: A database of commissioned accelerator models (Elekta Precise and Varian 2100CD) has been developed for treatment simulations in the MC system (EGSnrc/BEAMnrc). Beam descriptions can be exported from the TPS using the widespread DICOM framework, and the resultant files are parsed with the assistance of a software library (PixelMed Java DICOM Toolkit). The information in these files (such as the monitor units, the jaw positions and gantry orientation) is used to construct a plan-specific accelerator model which allows an accurate simulation of the patient treatment field. (1b) Dose Simulation: The calculation of a dose distribution requires patient CT images which are prepared for the MC simulation using a tool (CTCREATE) packaged with the system. Beam simulation results are converted to absolute dose per- MU using calibration factors recorded during the commissioning process and treatment simulation. These distributions are combined according to the MU meter settings stored in the exported plan to produce an accurate description of the prescribed dose to the patient. (2) Dose Comparison: TPS dose calculations can be obtained using either a DICOM export or by direct retrieval of binary dose files from the file system. Dose difference, gamma evaluation and normalised dose difference algorithms [2] were employed for the comparison of the TPS dose distribution and the MC dose distribution. These implementations are spatial resolution independent and able to interpolate for comparisons. Results and Discussion: The tools successfully produced Monte Carlo input files for a variety of plans exported from the Eclipse (Varian Medical Systems) and Pinnacle (Philips Medical Systems) planning systems: ranging in complexity from a single uniform square field to a five-field step and shoot IMRT treatment. The simulation of collimated beams has been verified geometrically, and validation of dose distributions in a simple body phantom (QUASAR) will follow. The developed dose comparison algorithms have also been tested with controlled dose distribution changes. Conclusion: The capability of the developed code to independently process treatment plans has been demonstrated. A number of limitations exist: only static fields are currently supported (dynamic wedges and dynamic IMRT will require further development), and the process has not been tested for planning systems other than Eclipse and Pinnacle. The tools will be used to independently assess the accuracy of the current treatment planning system dose calculation algorithms for complex treatment deliveries such as IMRT in treatment sites where patient inhomogeneities are expected to be significant. Acknowledgements: Computational resources and services used in this work were provided by the HPC and Research Support Group, Queensland University of Technology, Brisbane, Australia. Pinnacle dose parsing made possible with the help of Paul Reich, North Coast Cancer Institute, North Coast, New South Wales.

A role for IGF-1R-targeted therapies in small-cell lung cancer?

Background: Small-cell lung cancer (SCLC) is an aggressive disease with a poor prognosis. The insulin-like growth factor-1 receptor (IGF-1R) is an autocrine growth factor and an attractive therapeutic target in many solid tumors, but particularly in lung cancer. Patients and Methods: This study examined tumor samples from 23 patients diagnosed with SCLC, 11 resected specimens and 12 nodal biopsies obtained by mediastinoscopy, for expression of IGF-1R using the monoclonal rabbit anti-IGF-1R (clone G11, Ventana Medical Systems, Tucson, AZ) and standard immunohistochemistry (IHC). Results: All 23 tumor samples expressed IGF-1R with a range of stain intensity from weak (1+) to strong (3+). Ten tumors had a score of 3+, 7 tumors 2+, and 6 tumors 1+. Patient survival data were available for all 23 patients. Two patients died < 30 days post biopsy, therefore, the intensity of anti-IGF-1R immunostaining for 21 patients was correlated to survival. Patients with 3+ immunostaining had a poorer prognosis (P = .003). The overall survival of patients who underwent surgical resection was significantly better (median survival not reached) than patients who were not resected (median survival, 7.4 months) (P = .006). Conclusion: IGF-1R targeted therapies may have a role in the treatment of SCLC in combination with chemotherapy or as maintenance therapy. Further studies on the clinical benefit of targeting IGF-1R in SCLC are needed.

Field size consistency of nominally matched linacs

Given that there is increasing recognition of the effect that submillimetre changes in collimator position can have on radiotherapy beam dosimetry, this study aimed to evaluate the potential variability in small field collimation that may exist between otherwise matched linacs. Field sizes and field output factors were measured using radiochromic film and an electron diode, for jaw- and MLC-collimated fields produced by eight dosimetrically matched Varian iX linacs (Varian Medical Systems, Palo Alto, USA). This study used nominal sizes from 0.6×0.6 to 10×10 cm215 , for jaw-collimated fields,and from 1×1 to 10×10 cm216 , for MLC-collimated fields, delivered from a zero (head up, beam directed vertically downward) gantry angle. Differences between the field sizes measured for the eight linacs exceeded the uncertainty of the film measurements and the repositioning uncertainty of the jaws and MLCs on one linac. The dimensions of fields defined by MLC leaves were more consistent between linacs, while also differing more from their nominal values than fields defined by orthogonal jaws. The field output factors measured for the different linacs generally increased with increasing measured field size for the nominal 0.6×0.6 and 1×1 cm2 fields, and became consistent between linacs for nominal field sizes of 2×2 cm2 25 and larger. The inclusion in radiotherapy treatment planning system beam data of small field output factors acquired in fields collimated by jaws (rather than the more-reproducible MLCs), associated with either the nominal or the measured field sizes, should be viewed with caution. The size and reproducibility of the fields (especially the small fields) used to acquire treatment planning data should be investigated thoroughly as part of the linac or planning system commissioning process. Further investigation of these issues, using different linac models, collimation systems and beam orientations, is recommended.