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There is a need for a real-time, non-invasive method to monitor blood pH in a patient line during cardiopulmonary bypass, as today’s methods are both invasive and time consuming. Blood pH is an indicator of physiological and biochemical activity in the body and needs to be kept within a relatively narrow range, typically between 7.35-7.45. A pH value outside this range can be critical for the patient and therefore needs to be carefully monitored throughout the course of cardiopulmonary bypass. In this study the feasibility of using spectroscopic methods for indirect measurement of pH was investigated, and both transmission and reflectance spectroscopy were tested. The results showed that NIR reflectance spectroscopy is a feasible technique for blood pH monitoring during cardiopulmonary bypass. A strong correlation was found between measured pH values and spectral output in the wavelength range 800-930 nm. It was suggested that by means of the statistical partial least square regression method, a model could be created with three regression factors with a cross-validated R2 of 0.906 and a prediction error RMSEP of 0.089 pH units. The results presented here form a foundation for further analysis and experiments with larger sample set and more controlled experimental environment.

The heart disease Atrial Fibrillation (AFib) has increased worldwide in recent years. Untreated AFib can lead to cardiovascular complications such as stroke and heart failure. AFib is detected by physicians using Electrocardiogram (ECG). Since this disease can occur without symptoms for some patients, it can lead to late detection. Therefore, smart solutions for continuous monitoring of ECG to detect AFib is needed. This paper presents an approach to model an low-cost intelligent health monitoring system (IHMS) to classify and detect AFib in ECG using 1D Convolutional Neural Network (CNN). The core objective of this paper were to investigate the suitability of the computing architecture, edge and cloud, for an IHMS, and how complex 1D-CNN could be deployed to an edge device. Three 1D-CNN models with increased complexity was designed, trained and tested on AFib and NSR episodes collected from 25 records of the LTAF database. Each record were noise filtered and segmented into 10 sec. The best 1D-CNN model presented an accuracy of 83.93 %, 89.83 % in AUC, 84.32 % in sensitivity (AFIb), 83.46 % in specificity (NSR), 84.81 % in F1-score and 68.23 % in MCC. Two experiments into end-to-end delay and prediction time were performed todetermine the computing architectures suitability. The end-to-end delay were measured by sending ECG segments of different sizes to both computing architectures, while the prediction time were measured by deploying the designed 1D-CNN models on both computing architectures. Both measurements were added together to form the response time of the computing architectures. The edge computing architecture produced a delay around 0.019-0.377 sec and prediction time around 0.00X sec compared to cloud’s delay around 1.32-4.43 sec and 0.000X in prediction time. Resulting, that the edge computing architecture produced a lower response time and therefore considered the more suitable architecture for an IHMS. The designed 1D-CNN models had no issues in executing on the edge device, resulting in the conclusion that the most complex model to execute had 6 convolutional layers. The presented result in this paper contributes to the development of a health monitoring system in terms of choosing computing architecture platform and model complexity for a resource constraint device.

Introduktion Lång väntetid för Stockholms sjukhusbundna akutmottagningar är ett stort problem, samt stor spridning av väntetider mellan de olika mottagningarna. Prediktion av väntetid för Stockholms akutmottagningar är av värde för personer vid val av mottagning och kan i längden resulterai en spridning av patientbelastningen på Stockholms sjukhus. Metod Tidskoder och prioritetsklassificering för besökare på en av Stockholms akutmottagningar användes för att utreda linjära förhållanden mellan kö och väntetid, utifrån vilka prediktionsmodeller togs fram. Resultat Bästa linjära prediktiva metod ger ett medelfel på 86 minuter. Akutmottangningsköer uppvisar ett ickelinjärt förhållande till väntetider och följer inte dess formellt definierade prioritetsordning. Slutsats Djupare förståelse för hur akutmottagningsköer är organiserade i praktiken behövs för prediktion av väntetid som är tillräckligt precis för att vara av värde vid val av mottagning.

I dagsläget pågår en omfattande planering av utbyggnationen samt upprustningen av Södersjukhuset. Byggnationen beräknas preliminärt vara i bruk under mars 2018. I och med förändringen kommer sjukhuset få en helt ny sterilcentral.  Till en sterilcentral anländer kontaminerat medicinskt gods från tillhörande eller externa sjukhus. Efter att ha genomgått diskdesinficering och autoklavering på sterilcentralen kommer godset så småningom ut som sterilt för ytterligare användning.  Det finns idag en i stort sett färdig ritning för den nya sterilcentralen, men en osäkerhet i om antal utritade diskdesinfektorer och autoklaver kommer kunna omhänderta det framtida flödet av kontaminerat medicinskt gods. Målet med projektet var att undersöka Södersjukhusets preliminära ritning och sedan avgöra om ritningens diskdesinfektor- samt autoklavlinje är rätt dimensionerad och kan följas vid utbyggnationen. För att uppfylla detta mål har flödesundersökningar samt kapacitetsberäkningar utförts på dagens sterilcentral. Resultaten från dessa undersökningar har sedan jämförts med ritningen för att kunna dra en slutsats i huruvida ritningens specifikationer gällande diskdesinfektor- samt autoklavlinje är tillräckliga.  Antalet maskiner i den preliminära ritningen kommer kunna hantera dagens flöde av medicinskt gods om de har en kapacitet minst lika stor som dagens utrustning. Därför drogs slutsatsen att denna ritning kunde anses bli den slutgiltiga. / At the moment a comprehensive planning of the reconstruction of Södersjukhuset is in progress. The reconstruction is estimated to be done during March 2018, and with this the hospital will receive a new sterilization center. A sterilization center takes care of medical material that is contaminated at arrival. After the sterilization process, including desinfection and autoclaving, the material leaves the sterilization center as sterile. Today, a drawing of the new sterilization center exists, but there is an uncertainty whether the number of drawn washing desinfectors and autoclaves will be able to handle the incoming material. The goal with the project was to investigate the current drawing and then conclude whether the number of washing desinfectors and autoclaves is correctly dimensioned, and can be followed at the reconstruction. To reach the goal, investigations of the flow and calculations of the capacity, has been done at the current sterilization center. These have then been compared to the drawing, which made it possible to conclude whether the drawing’s number of machines is correctly dimensioned. The number of machines in the present drawing will be able to take care of the daily flow of medical material, if they have a capacity of at least the same as the current machines. Thus the conclusion came to be that the drawing can be considered as the definitive.

The backprojection-then-filtering algorithm was applied to proton CT data to reconstruct a map of proton stopping power relative to water (RSP) in air, water and bone. Backprojections were performed along three commonly used path estimates for the proton: straight line path, cubic spline path, and most likely path. The proton CT data was obtained through simulations using the GEANT4 simulation toolkit. Two elliptical phantoms were inspected, and an accuracy of 0.2% and 0.8% was obtained for the RSP in water and bone respectively in the region of interest, while the RSP of air was significantly underestimated.

Segmenting brain MR scans could be highly benecial for diagnosing, treating and evaluating the progress of specic diseases. Up to this point, manual segmentation,performed by experts, is the conventional method in hospitals and clinical environments. Although manual segmentation is accurate, it is time consuming, expensive and might not be reliable. Many non-automatic and semi automatic methods have been proposed in the literature in order to segment MR brain images, but the levelof accuracy is not comparable with manual segmentation. The aim of this project is to implement and make a preliminary evaluation of a method based on machine learning technique for segmenting gray matter (GM),white matter (WM) and cerebrospinal uid (CSF) of brain MR scans using images available within the open MICCAI grand challenge (MRBrainS13).The proposed method employs supervised articial neural network based autocontext algorithm, exploiting intensity-based, spatial-based and shape model-basedlevel set segmentation results as features of the network. The obtained average results based on Dice similarity index were 97.73%, 95.37%, 82.76%, 88.47% and 84.78% for intracranial volume, brain (WM + GM), CSF, WM and GM respectively. This method achieved competitive results with considerably shorter required training time in MRBrainsS13 challenge.

Helgenomsekvensering genererar enorma mängder komplex data som kan vara svår att analysera. Visualisering av denna data är ett viktigt steg för att underlätta analys. Av speciellt intresse är visualisering av strukturella varianter, variationer i DNA större än 1000 baspar, som tros ligga till grund för flera genetiska sjukdomar. För detta ändamål utvecklades fyra verktyg: ett cirkeldiagram, ett täckningsdiagram, ett karyotypdiagram och en interaktionsvärmekarta. Mjukvaran skrevs i språket Python och utnyttjar ramverket Qt och tillhörande Python-bindningar för dess grafiska användargränssnitt, tillsammans med biblioteket Matplotlib för att plotta vissa grafer. Verktygen innehåller en mängd funktioner och knyter ihop dessa i ett för användaren enkelt gränssnitt, men plats för vidareutveckling finns. En rad förslag till sådan vidareutveckling diskuteras, så som att implementera fler funktioner, integrera verkygen bättre med befintlig mjukvara, och förbättra portabilitet genom nätverksfunktioner. / Whole genome sequencing generates enormous amounts of complex data that can be difficult to analyze. Visualization of this data is an important step to facilitate analysis. Of particular interest is visualization of structural variants, variations in DNA greater than 1000 base pairs, some of which are thought to be the cause of genetic disorders. For this purpose four tools were developed: a circle diagram, a coverage diagram, a karyotype diagram and an interaction heatmap. The software was written in Python and utilizes the framework Qt and associated Python-bindings for its graphical user interface, together with the library Matplotlib for some plotting functions. Although the tools feature a variety of functions and tie these together in an easy to use interface, there is still room for development. A number of suggestions for such development is discussed, such as implementing more functions, integrating the tools better with existing software, and improving portability through network functions.

No description available.

The microcirculation system is crucial for the function of delivering biological markers such as oxygen and removing carbon dioxide from all the cells forming the complex ma- trix of tissue in the body. To keep up with the demands of each and every cell, there is a response from the microvasculature - resulting from for instance changes in blood flow to the tissue area. Infections cause disturbances in this important system, which increases the risk of development into one of the world’s most common syndrome - sepsis. This con- dition can be explained as a biological response affecting each and every vital organ, and can as a result of the dysfunction be life threatening. Studies have shown when monitoring pulse and respiratory rate the response is not visually quick enough to be able to determine the gravity in the state of the patient. The primarily chosen biological markers were oxy- genated hemoglobin and deoxygenated hemoglobin present in blood, respectively melanin in the skin. This was performed using the optical instrument Spatial Frequency Domain Imaging in combination with a Tissue Viability Imager respectively an Enhanced Perfusion and Oxygen Saturation-equipment. The formulated aim for this thesis was separated into an optical part and a mathematical modeling part. Regarding the optical section the aim was to understand if there were any optical methods more preferable to detect changes in the microcirculation, whilst the modeling section aimed to understand how to construct the best adjusted model for the changes in the biological markers and how these could be related to sepsis. Spatial Frequency Domain Imaging is an optical technique able to generate two- dimensional maps of the absorption coefficient and the reduced scattering coefficient of a biological tissue surface. The skin of healthy subjects were illuminated with RGB-LEDs to detect the chromophores of interest. The data obtained from the experimental sessions was then collected to work as a base for building a mathematical model. The experimental session was performed with a total of six healthy subjects and the data was collected dur- ing a control-measurement and a simulated sepsis-measurement using a pressure chamber and applying negative pressure to the lower part of the body. The mathematical model was based on theory regarding the biological events of sepsis in the microcirculation and was described by ordinary differential equations. The results were presented in graphs and the resulting model likewise, with an addi- tional figure to describe the source of associated equations written to describe the events. An observation of a distinct difference in the deoxygenated, respectively oxygenated hemoglobin could be observed and did show in general more changes during the measure- ments using a lower body negative pressure chamber. The chosen optical approach was the Spatial Frequency Domain Imaging equipment along with the mathematical model named as the Macro-Micro model due to its more realistic design. Future improvements were dis- cussed and summarized as a repetition of the experimental sessions and including more parameters and relationships between the biological markers and the model. This would contribute to more robust results.