The aim of the project was to compare the workflow for an Elekta Linac with and without the surfacescanning system Catalyst and describe pros and cons with both workflows. The findings in the reportcan be used as decision support in development of Elekta products and workflow improvements. The method for the project was to do interviews, observations and time measurements at Södersjukhuset(not using Catalyst) and Sundsvalls sjukhus (using Catalyst). The workflows were graded in an as-sessment protocol covering time efficiency, comfort, noise, resources, reliability, cost, dosage and sideeffects. Different workflow scenarios were simulated in AnyLogic. The result of the project was that, according to our protocol, the workflow with Catalyst was ratedhigher than without it. The simulations in Anylogic showed that minimizing gaps in the treatment sched-ule generated the same number of patients treated per day, if the positioning could not be done faster.The simulations also showed that removing position verification with cone beam computer tomography(CBCT), an imaging system which is used in addition to the Catalyst system, would increase the numberof treated patients with approximately 33%. The conclusion was that there were no great differences in time efficiency between the workflows. How-ever, considering the higher reliability and comfort for the patient, optical surface scanning can improvethe positioning for Elekta Linac and is therefore worth implementing. Minimizing treatment gaps wouldnot improve the workflow. Removing the use of CBCT would increase the number of treated patientsper day.

I detta projekt har Googles röstgränssnitt (eng: Google Cloud Speech API) utvärderats utifrån syftet att skapa ett program som ska identifiera en person baserat på dess röst. Detta projekt gjordes tillsammans med ett företager Call Knut vars mål är att utforma en tjänst som bygger på AI teknik som ska ringa upp till äldre. Eftersom tjänsten riktar sig mot äldre vill företaget Call Knut ha ett program som kan identifiera de äldre baserat på rösten.  Ett program skapades med hjälp av Googles röstgränssnitt för att transkribera och urskilja två röster i en ljudfil. Därefter samlades det in ljudfiler från olika personer i ett brett åldersspann och ljudfilerna kombinerades. De kombinerade ljudfilerna analyserades sedan för att kunna verifiera om Googles röstgränssnitt är optimalt för ändamålet. I 29,2 % av de kombinerade ljudfilerna lyckades Googles röstgränssnitt med att både urskilja och transkribera. Totalt misslyckades Googles röstgränssnitt med 70,8 % av inmatningarna.  Vår slutsats blev att Googles röstgränssnitt inte är lämpligt att använda för att utveckla Call Knuts planerade tjänst där rösturskiljningen måste fungera med hög precision. Vidare utvecklingsarbete rekommenderas att fokusera på att testa andra program eller röstgränssnitt. / In this project, the Google Speech API has been evaluated based on the purpose of creating a program that will identify a person based on their voice. This project is done together with a company called Call Knut whose goal is to design a service based on AI technology that will call the elderly. Since the service is aimed at the elderly, Call Knut wants a program that can identify the elderly based on their voice.  An application was created using the Google Speech API to transcribe and distinguish two voices in an audio file. Then audio files were collected from different people in a wide age range and audio files were combined. The combined audio files were then analyzed to verify whether the Google Cloud interface is optimal for the purpose. In 29.2 % of the combined audio files Google Speech API managed to both distinguish two voices and transcribe what they said. In total, Google Speech API failed with 70.8 % of the entries.  Our conclusion was that Google's voice interface is not suitable to use to develop Call Knut’s planned service where voice recognition must work with high precision. Further development work is recommended to focus on testing other programs or voice interfaces.

Coronapandemin har bidragit till en ökad försäljning av desinficeringsrobotar/UVC-robotar till sjukvården. UVC-roboten SteriPro, som användes i detta arbete, har inte tidigare tillämpats på ett svenskt sjukhus. Målet med arbetet var att fastställa mervärdet av desinficeringsroboten som komplement till befintliga städrutiner på sjukhus, i detta arbete avgränsat till Västmanlands sjukhus i Västerås. Ett mervärde som berör UVC-robotens strålningseffektivitet och användarvänlighet. Provtagningar och analysering av utvalda provpunkter före- och efter de manuella städrutinerna samt efter UVC-robotens desinficeringsprocess utfördes. Även faktorer som berör UVC-robotens användarvänlighet sammanställdes.    Det konstaterades att UVC-roboten reducerade antalet kolonibildande enheter mellan provtagningarna, mellan den manuellt utförda städningen och UVC-robotens desinficeringsprocesser, samt att användarvänligheten påvisade både för- och nackdelar. Slutsatsen angående UVC-robotens övergripande mervärde är att den har god potential att tillämpas inom vården i och med dess goda desinficeringsförmåga och enkla användning. Däremot skapar UVC-robotens avvikande elektriska standard problem för en naturlig integration av UVC-roboten i de befintliga städrutinerna vilket gör att den i nuläget ej är redo för den svenska vården. / The coronavirus pandemic has contributed to increased sales of disinfection robots/UVC-robotsto healthcare. The UVC-robot SteriPro, which was used in this study, has not previously been applied in a Swedish hospital. The aim of the study was to establish the added value ofdisinfection robots as complements to existing cleaning routines at hospitals, in this study at Västmanlands sjukhus in Västerås. An added value that affects the UVC-robot’s radiation efficiency and user-friendliness. Sampling and analysis of selected test points before and after the manual cleaning routines and after the UVC-robot's disinfection process were performed. Factors influencing the usability ofthe UVC-robot were also compiled. It was found that the UVC-robot reduced the number of colony-forming units between samplings, between the manual cleaning and the disinfection processes of the UVC-robot, and that the user-friendliness demonstrated both advantages and disadvantages. The conclusion regarding the UVC-robot’s overall added value is that is has good potential to be applied inhealthcare due to its good disinfection ability and ease of use. However, the UVC-robot’s deviating electrical standard creates problems for a natural integration of the UVC-robot into existing cleaning routines, which means that it is not currently ready for Swedish healthcare.

Detta examensarbete har genomförts på uppdrag av företaget Sangair i syfte att bidra med kunskap i deras utveckling av en produkt ämnad att behandla sepsis med hjälp av ozon. Sepsis är en av de ledande dödsorsakerna i världen och behandlas idag primärt med antibiotika. På grund av den ökade antibiotikaresistensen efterfrågas nya behandlingsmetoder. Ozon har bakteriedödande egenskaper och det finns goda indikationer på att ozonterapier kan komma att bli ett komplement till antibiotikabehandlingar. För att ozon ska lösas in väl i blod krävs låga temperaturer. Blod kan kylas utanför kroppen med hjälp av värmeväxlare som har till uppgift att överföra värme mellan två fluider. Målet för examensarbetet var att undersöka två värmeväxlare, CSC14 och BCD Vanguard från tillverkaren Sorin Group för att utvärdera deras kapacitet att kyla blod till temperaturer lägre än 12 °C. Som en del i målet undersöks värmeväxlarnas effektivitet i flödesintervallet 80–150 ml/min, samt förhållandet mellan den önskade temperaturen och den uppnådda temperaturen i processen.  Arbetet utfördes primärt genom flertalet tester där ett system med värmeväxlare kopplades upp. Vid genomförandet av testerna användes mjölk för att simulera blodets egenskaper. Inställningen för kylaren var den parameter som hade störst påverkan på temperaturen som mjölken kunde kylas till. Skillnaden mellan den önskade och den uppmätta temperaturen var större ju närmre 0 °C kylartemperaturen kom. Testerna visade att CSC14 inte fungerar optimalt vid låga flödeshastigheter och därför ansågs denna inte lämplig för vidare tester. BCD Vanguard har en god effektivitet inom flödesintervallet där effektiviteten ökar med flödeshastigheten. / This thesis work was carried out on behalf of the company Sangair with the purpose of contributing to their product development where they are designing a medical device for treating sepsis using ozone. Sepsis is one of the leading causes of death worldwide and is primarily treated using antibiotics. Due to the increase in antibiotic resistant bacteria new treatment methods must be developed. Ozone is bactericidal and may serve as a complement to antibiotic treatments. The solubility of ozone in blood increases with decreasing temperatures. Heat exchangers, which transfer heat between two fluids, can be used to cool the blood outside the human body.  The goal for this thesis work was to investigate two heat exchangers, CSC14 and BCD Vanguard, both manufactured by Sorin Group. The objective was to evaluate their respective capacity to cool blood to temperatures below 12 °C. The efficiency of the heat exchangers for flow rates ranging from 80—150 ml/min and the relationship between the desired temperature and the actual temperature were evaluated.  Multiple tests were performed with heat exchangers connected in a simple system. Milk was used to simulate the properties of blood. The settings of the cooling unit had the greatest effect on the achieved cooling. Differences between the desired and the measured temperature were greater the closer to 0 °C the cooler was set. The test unveiled that CSC14 does not perform well at low flow rates and thus no further testing was done using that heat exchanger. The BCD Vanguard was shown to have a high efficiency in the desired interval with performance increasing with increasing flow rates.

Framgången av mönsterigenkänning inom AI (artificiell intelligens) har skapat höga förväntningar om att AI ska kunna appliceras inom vården, framför allt inom radiologi. Det danska företaget Radiobotics har utvecklat en maskininlärningsbaserad programvara som diagnostiserar knäartros, för att assistera vårdpersonalen i deras arbete. Denna AI-programvara vid namn RBknee analyserar en röntgenbild utifrån tre diagnostiska parametrar som förekommer vid knäartros, för att sedan sammanställa de radiologiska fynden i en skriftlig rapport tillsammans med en slutgiltig diagnos. För att få förståelse för hur RBknees analysförmåga påverkas av en bildkvalitetsförsämring undersöktes för vilken kontrast och brusnivå som RBknee genererar ett felaktigt utlåtande gällande de diagnostiska parametrarna och slutdiagnosen. Vidare undersöktes om graden av knäartros påverkade RBknee analysförmåga vid en bildkvalitetsförsämring. Ett bildunderlag med kliniskt tagna slätröntgenbilder av knän degraderades med avseende på kontrast och brus för att sedan analyseras av RBknee. Förändringar av RBknees utlåtande för de degraderade bilderna jämfört med originalbildens utlåtande sammanställdes och studerades. Resultatet visade att det inte gick att identifiera en specifik försämringsgrad av bildkvaliteten där RBknee genererade ett felaktigt utlåtande. RBknees förmåga att generera ett korrekt utlåtande var bättre vid en kontrastdegradering än vid en brusdegradering. Det konstaterades att en ökad brusnivå ökade risken för ett felaktigt utlåtande av RBknee, samt att brusets position på röntgenbilden hade en påverkan. Det gick även att fastställa att röntgenbilder av knän med en lägre grad av knäartros i högre grad riskerade att få felaktiga utlåtanden av RBknee. / The success of pattern recognition in AI (artificial intelligence) has brought high expectations for AI to be applied in healthcare, especially in radiology. A machine learning software for knee osteoarthritis diagnosis has been developed by the Danish company Radiobotics. The AI software, named RBknee, analyses digital radiographs and annotates osteoarthritis related findings. The findings, together with a conclusion, are compiled in a written report. RBknee is intended to assist healthcare professionals in radiographic analysis. How RBknees analytical ability is affected by a reduced image quality was studied by examining the contrast and noise level which cause RBknee to generate incorrect findings and conclusions. If the image quality reduction caused RBknees analytically ability to differ with different degrees of knee osteoarthritis, was also studied. The image quality of clinical digital radiographs of knees was reduced and analysed by RBknee. RBknees findings and conclusion were compared with the report of the original image, where the changes were compiled into tables. No specific reduction of image quality that restricted RBknee analytically ability was established in the study. An increased noise level seemed to increase the risk of receiving an incorrect report by RBknee. RBknees ability to generate correct report was better for contrast degraded images than for images with increased noise level. The position of the noise in the radiograph also seemed to have an impact on RBknees analytical ability. It was also possible to establish that knees with a lower degree of knee osteoarthritis were more likely to receive an incorrect report from RBknee.

ECG is used clinically to detect a multitude of medical conditions, such as heart-problems like arrhythmias and heart failure, and to give a good general image of the function of the heart with a quick and harmless exam. In many clinical cases, normal ECG measurements cannot be taken, such as with fetuses where ECG signals from the mother’s own body hinder the measurement. This paper examines using machine learning algorithms to be able to simulate ECG graphs from ultrasound data alone. These algorithms are trained on ultrasound and ECG data acquired from the same patient simultaneously. The data used in the training of the algorithms is taken from samples acquired from 100 adult patients. The results found using this method to simulate an ECG indicate good possibilities for future usefulness, where machine learning to acquire simulated ECG can help facilitate clinicians in evaluating fetal heart function, as well as in other cases where ECG cannot be measured normally. / EKG används kliniskt för att upptäcka en mängd olika åkommor, så som hjärtsvikt och arytmier, men också för att ge en generell bild av hjärtfunktionen med en snabb och harmlös undersökning. I många kliniska fall kan dock inte normal EKG mätning ske, så som för foster då EKG signaler från moderns egna kropp hindrar EKG-mätningen. I detta papper undersöks användandet av maskininlärningsalgoritmer för att kunna simulera EKG grafer från enbart ultraljuds data. Dessa algoritmer är tränade på ultraljud och EKG data som simultant fåtts från samma undersökning av en patient. I detta papper har ultraljudsdatan som använts kommit från 100 mätningar från olika vuxna patienter. Resultaten funna från undersökningen av EKG simulerings metoden indikerar goda möjligheter för framtida användbarhet, då maskininlärningsalgoritmer för att simulera EKG kan underlätta när kliniker ska utvärdera hjärtfunktionen hos foster, eller i andra fall då EKG inte kan mätas normalt.

På Kalmar Länssjukhus har det varit problematiskt att lokalisera rullstolar på sjukhusets område. För att underlätta den dagliga verksamheten kring dessa rullstolar har sensorer uppkopplade till LoRaWAN varit en möjlig lösning. Detta projekt har därför undersökt om LoRaWAN tillsammans med ett positioneringssystem uppfyller kriterierna för att vara en lämplig lösning.  De parametrar som undersökts var noggrannheten för geografisk positionsbestämning av en sensor, signalstyrkan och väntetiden för signalöverföringen. För att mäta de önskade parametrarna användes sensorer kopplade till spårningssystemet Traxmate. Sensorns geografiska position beräknades i Traxmate genom trilateration där MAC – adresser hos närliggande Wi – Fi accesspunkter utnyttjades. Spårningssystemet kunde därmed hämta rådata från sensorerna som därefter sammanställdes i MATLAB. Resultatet visade att LoRaWAN i sig kan vara lämpligt i en sjukhusmiljö. Däremot finns det brister i positioneringssystemet som bör vidareutvecklas för att kunna rekommenderas till ett sjukhus. / Kalmar Länssjukhus has been experiencing difficulties with localization of wheelchairs in the hospital area. In order to facilitate the work of the staff who are searching for the wheelchairs, sensors connected to the LoRaWAN wireless network can be used. Thus, this project has evaluated LoRaWAN together with a positioning system to determine whether the solution is suitable for a hospital environment.  The evaluated parameters where the accuracy of geographical position determination of a sensor, the received signal strength indicator, and the delayed time for the transmission of a signal. Sensors connected to the Traxmate tracking system were used to measure the desired parameters. The sensor’s geographical position was calculated in Traxmate by trilateration where MAC – addresses of nearby Wi – Fi access points were used. The tracking system was thus able to retrieve raw data from the sensors which were then compiled in MATLAB. The results showed that the wireless network structure LoRaWAN satisfied the criteria for usage in a hospital environment. However, the positioning system showed some flaws and thus it should be further investigated in order to be recommended for usage in a hospital environment.

Recognition of physical and mental responses to stress is important for stress assessment and management as its negative effects in health can be prevented or reduced. Wearable technology, mainly using electroencephalogram (EEG), provides information such as tracking fitness activity, disease detection, and monitoring neurologicalstates of individuals. However, the recording of EEG signals from a wearable device is inconvenient, expensive, and uncomfortable during normal daily activities. This study introduces the application of tensor decomposition of non-EEG data for visualizing and classifying neurological statuses with application to human stress recognition. The multimodal dataset of non-EEG physiological signals publicly available from the PhysioNet database was used for testing the proposed method. To visualize the biosignals in a low dimensional feature space, the multi-way factorization technique known as the PARAFAC was applied for feature extraction. Results show visualizations that well separate the four groups of neurological statuses obtained from twenty healthy subjects. The extracted features were then used for pattern classification. Two statistical classifiers, which are the multinomial logit regression(MLR) and linear discriminant analysis (LDA), were implemented. The results show that the MLR and LDA can identify the four neurological statuses with accuracies of 95% and 98.8%, respectively. This study suggests the potential application of tensor decomposition for the analysis of physiological measurements collected from multiple sensors. Moreover, the proposed study contributes to the advancement of wearable technology for human stress monitoring. With tensor decomposition of complex multi-sensor or multi-channel data, simple classification techniques can be employed to achieve similar results obtained using sophisticated machine-learning techniques.

Cancer is the second largest mortal disease in Sweden, and high efforts are made to develop the treatment of cancer. One of the main treatment methods is radiotherapy, which uses ionizing radiation to damage the cancerous cells. This has the chance of stopping the cell reproduction, and the goal is to reduce the tumor and stop the tumor growth. The most common forms of radiotherapy uses external beams to irradiate the tumor. In intensity modulated radiotherapy, IMRT, the beam fluences are optimized to give a highly conformal dose, i.e. a dose distribution which is restricted to the tumor and has low dose values outside of the tumor. A conformal dose is necessary to spare healthy tissue and sensitive organs, and thus keep the side-effects of the treatment at an acceptable level. The optimized beam shapes are created using a multileaf collimator, MLC. Finding the leaf positions and dose levels is formulated as a problem in the framework of mathematical optimization. Currently, one of the limitations in delivering conformal dose is due to patient movement during the treatment. In IMRT, the beams are delivered by consecutive segments, and the exact pairing of the segments with the patient position will have an impact on the delivered dose. This is called the interplay effect, and can cause both underdosage of the tumor and overdosage of the surrounding tissue. There are methods of mitigating the interplay effect. For example, the beam could be restricted to a single phase of the motion by repeatedly turning it on and off. This is known as gating. However, gating and many other interplay mitigation techniques lead to prolonged treatment times, which decreases the clinical throughput, causes higher patient discomfort and gives higher uncertainties in the delivered dose. This makes it desirable to find methods which avoid prolonged treatment times, while still giving highly conformal doses. Ideally, the best method would be to have a beam which follows any target movement. This idea is known as target tracking. In this thesis, an optimization method is suggested which includes the interplay effect in the treatment optimization. Two main treatment strategies are proposed. The method which is simplest to implement clinically is to create plans which are robust against uncertainties in the times for the patient motion. The resulting doses are found to give acceptable target covering where similar, conventional plans give a significant target underdose. To further increase the conformality of the doses, a non-robust method paired with gating technology is suggested. This method can effectively be seen as a target tracking method, and has the possibility to give highly conformal doses under acceptable treatment times.

Vulnerable road users (VRUs) are overrepresented in the statistics on severe and deadly injuries in traffic accidents, most commonly involving the head. The finite element (FE) method presents the possibility to model complex interactions between the human body and vehicles in order to better understand the injury mechanisms. While the rapid development of computer capacity has allowed for increasingly detailed FE-models, there is always a benefit of reducing the studied problem. Due to its material properties, the brain is more sensitive to rotational motion than to purely linear, resulting in complex injury causation. When studying brain injuries caused by a direct impact to the head, simulations using an isolated head model significantly increases efficiency compared to using a complete human body model. Also evaluation of head protective systems uses isolated mechanical head representations. It is not, however, established the extent to which the boundary conditions of the head determine the outcome of brain injuries. FE models of both the entire human body and the isolated head were used in this thesis to study the effect of the body, as well as active neck muscle tension, on brain injury outcome in VRU accidents. A pediatric neck model was also developed to enable the study of age-specific effects. A vehicle windscreen model was developed to evaluate the necessity of capturing the failure deformation during pedestrian head impacts. It was shown that the influence of the neck and body on brain injury prediction is greater in longer duration impacts, such as pedestrian head-to-windscreen impacts with an average difference of 21%. In accidents with shorter duration impacts, such as head-to-ground bicycle accidents, the average influence was between 3-12%. The influence did not consistently increase or limit the severity, and was dependent on the degree of rotation induced by the impact, as well as the mode of deformation induced in the neck. It was also shown that the predicted brain injury severity is dependent on capturing the large deformations of fractured windscreen, with the greatest effect near the windscreen frame. The pediatric neck model showed a large effect of age-dependent anatomical changes on inertial head loading, making it a promising tool to study the age-dependent effects in VRU accidents. / <p>QC 20171013</p>