2,931 research outputs found

    Understanding the experience of ‘brain fog’ in coeliac disease: an interpretative phenomenological analysis

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    This thesis is submitted by Emily May Ahmed in partial fulfilment of the degree of Doctor of Clinical Psychology at the University of Birmingham. The thesis is comprised of three chapters. The first chapter is a meta-analysis which aims to provide a current prevalence estimate of depression in adults with coeliac disease, including evaluation of risk of bias factors. Additionally, it includes a brief secondary analysis, within the appendix, describing prevalence and relative risk estimates for other mental health disorders associated with coeliac disease. The second chapter is a qualitative empirical study which uses interpretative phenomenological analysis (IPA) methodology to explore the complex lived experiences of one of the lesser-known symptoms associated with coeliac disease – ‘brain fog’, in seven participants. Both the meta-analysis and empirical studies have clear clinical implications for the cognitive and psychological support that individuals with coeliac disease should be offered during and after diagnosis. Finally, the third chapter is comprised of two press release documents, which provides an accessible summary of the main findings of both the meta-analysis and the empirical research study

    Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history

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    Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach

    Study of soft materials, flexible electronics, and machine learning for fully portable and wireless brain-machine interfaces

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    Over 300,000 individuals in the United States are afflicted with some form of limited motor function from brainstem or spinal-cord related injury resulting in quadriplegia or some form of locked-in syndrome. Conventional brain-machine interfaces used to allow for communication or movement require heavy, rigid components, uncomfortable headgear, excessive numbers of electrodes, and bulky electronics with long wires that result in greater data artifacts and generally inadequate performance. Wireless, wearable electroencephalograms, along with dry non-invasive electrodes can be utilized to allow recording of brain activity on a mobile subject to allow for unrestricted movement. Additionally, multilayer microfabricated flexible circuits, when combined with a soft materials platform allows for imperceptible wearable data acquisition electronics for long term recording. This dissertation aims to introduce new electronics and training paradigms for brain-machine interfaces to provide remedies in the form of communication and movement for these individuals. Here, training is optimized by generating a virtual environment from which a subject can achieve immersion using a VR headset in order to train and familiarize with the system. Advances in hardware and implementation of convolutional neural networks allow for rapid classification and low-latency target control. Integration of materials, mechanics, circuit and electrode design results in an optimized brain-machine interface allowing for rehabilitation and overall improved quality of life.Ph.D

    An investigation into mild traumatic brain injury identification, management, and mitigation

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    Concussion is classified as a mild traumatic brain injury which can be induced by biomechanical forces such as a physical impact to the head or body, which results in a transient neurological disturbance without obvious structural brain damage. Immediate access to tools that can identify, diagnosis and manage concussion are wide ranging and can lack consistency in application. It is well documented that there are frequent incidences of concussion across amateur and professional sport such as popular contact sports like rugby union. A primary aim of this thesis was to establish the current modalities of ‘pitch side’ concussion management, identification, and diagnosis across amateur and professional sporting populations. Furthermore, the research sought to understand existing concussion management and concussion experiences by means of recording the player’s experiences and perceptions (retired professional rugby union players). These qualitative studies sought to gain insights into concussion experiences, the language used to discuss concussion and the duty of care which medical staff, coaching personnel, and club owners have towards professional rugby players in their employment. In addition, possible interventions to reduce the incidence of concussion in amateur and professional sports were investigated. These included a ‘proof of concept’ using inertial measurement units and a smartphone application, a tackle technique coaching app for amateur sports. Other research data investigating the use of neurological function data and neuromuscular fatigue in current professional rugby players as a novel means of monitoring injury risk were included in this research theme. The findings of these studies suggest that there is an established head injury assessment process for professional sports. However, in amateur sport settings, this is not the existing practice and may expose amateur players to an increased risk of post-concussion syndrome or early retirement. Many past professional rugby union players stated that they did not know the effects of cumulative repetitive head impacts. They discussed how they minimised and ignored repeated concussions due to peer pressure or pressure from coaches or their own internal pressures of maintaining a livelihood. These data suggest that players believed that strong willed medical staff, immutable to pressures from coaching staff or even athletes themselves, were essential for player welfare and that club owners have a long-term duty of care to retired professional rugby union players. However, there are anecdotal methods suggested to reduce concussion incidence. For example, neck strengthening techniques to mitigate against collision impacts. There is, no longitudinal evidence to suggest that neck strength can reduce the impacts of concussion in adult populations . Additionally, other factors such as lowering the tackle height in the professional and amateur game is currently being investigated as a mitigating factor to reduce head injury risk. The final theme of the thesis investigated possible methods to reduce injury incidence in amateur and professional athletes. The novel tackle technique platform could assist inexperienced amateur coaches on how to coach effective tackle technique to youth players. The findings from the neurological function data suggests that this may be an alternative way for coaches to assess and gather fatigue data on professional rugby union players alongside additional subjective measures and neuromuscular function data. Recently, the awareness of concussion as an injury and the recognition of concussion in many sports settings has improved. These incremental improvements have led to increased discussion regarding possible measures to mitigate the effects of concussion. There are many additional procedures to be implemented before a comprehensive concussion management is universally available, particularly in amateur and community sports. These necessary processes could be technological advances (e.g., using smart phone technology) for parents and amateur coaches to assist in the early identification of concussion or evidence-based concussion reduction strategies

    Ergonomics in laparoscopic surgery: a work system analysis to reduce work-related musculoskeletal disorders across surgeons in Peruvian hospitals

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    Laparoscopic surgery, also called minimally invasive surgery, is a type of surgery in which the surgeon operates by viewing the surgery on a screen that projects images from a camera inserted into the patient's abdomen. Laparoscopic tools are long (usually up to 35 cm) and require fine motor skills and visual perception for manipulation, restricting the degrees of freedom to move within the patient. This restriction causes surgeons to operate with limited vision and restricted movement and force them to work with assistants who assist in conducting the cameras, acting as "the surgeons' eyes". Because of its minimally invasive nature, laparoscopic surgery is well accepted by patients but is challenging and complex for the surgeon. This is due to the restriction of movement and perception that forces surgeons to adopt awkward postures with high exposition, which increases the likelihood of work-related musculoskeletal disorders (WRMSD). WRMSDs are detrimental to surgeons' health and potentially may impact patient safety. Studies often highlight the problems of surgeons in high-income countries, whose solutions and clinical guides often cannot be applied to countries like Peru, which have severe deficiencies in its healthcare system. For this reason, the thesis proposes a contextualised investigation of the Peruvian surgical work system to investigate the main factors contributing to the development of WRMSD in laparoscopic surgeons, which may affect patient safety. The analysis aimed to propose possible recommendations to support redesigning the laparoscopic surgery work system in Peruvian hospitals. Five studies were developed to achieve the aims based on the Systems Engineering Initiative for patient safety model, an ergonomics model for healthcare systems analysis. The first three studies were developed parallel with a mixed convergent design approach concluding in an integrating study. The last two studies (study four and five) had a quantitative approach. The first study used a qualitative approach by collecting information through interviews with laparoscopic surgeons and observing their work in real surgeries. The second study adopted a quantitative approach through a questionnaire-based survey applied to 140 surgeons in Peru. The third study analysed the extent to which the postures adopted by surgeons in real surgeries increase the risk of WRMSD and their association with factors in the work system using the RULA method. The results of the three studies were integrated into an integrative study, concluding that the raised height of the operating table and other system factors related to tasks, person and technology raises the risk of WRMSD. Based on these results, the fourth study analysed the relationship between surgeons and operating tables to understand how many surgeons could reach suitable working heights. The study concluded that no operating table available in Peruvian hospitals nor in the market would be suitable for 90% of Peruvian surgeons. The tables were too high to accommodate surgeons with optimal working surface height to perform laparoscopic surgery. Then, a fifth study was conducted to determine an acceptable working height based on surgeon preferences and system factors and concluded that surgeons would accept a working height between 49 cm to 70 cm in height, which is lower than current operating tables. The lowest height was reached when surgeons had to operate on obese patients and perform intracorporeal suturing tasks. Finally, the thesis concludes with recommendations for redesigning working heights for 90% of the Peruvian medical population, considering work system elements of the Peruvian context

    A Cognitive Intervention for Everyday Executive Function in Female Survivors of Intimate Partner Violence Related Traumatic Brain Injury, A Single-Case Experimental Design (SCED)

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    An estimated 31,500,000 females have experienced at least one intimate partner violence (IPV) related traumatic brain injury (TBI), or IPV-TBI in their lifetime in the United States of America (USA) alone. Survivors often experience executive function (EF) impairments, resulting in numerous functional and psychological challenges. Despite this, there are currently no studies into EF interventions for IPV-TBI survivors available. Compensatory cognitive rehabilitation and EF coaching have shown positive outcomes for EF in TBI. The current study aimed to investigate the effects of an intervention, combining cognitive rehabilitation and EF coaching for female survivors of IPV-TBI with EF impairments. A multiple baseline single case experimental design (MB-SCED) was used. Two female participants (age M=51.5, range=44-59) completed the study. The independent variable was a four-week cognitive intervention, the dependent variables were everyday executive function, goal attainment, and health-related quality of life (HRQoL). Analysis revealed that the intervention may have benefits for EF goal attainment, self-reported EF and HRQoL. However, these should be interpreted with caution due to the study limitations. The study highlights the need for further clinical interventions and research for IPV-TBI survivors

    Ovarian hormones shape brain structure, function, and chemistry: A neuropsychiatric framework for female brain health

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    There are robust sex differences in brain anatomy, function, as well as neuropsychiatric and neurodegenerative disease risk (1-6), with women approximately twice as likely to suffer from a depressive illness as well as Alzheimer’s Disease. Disruptions in ovarian hormones likely play a role in such disproportionate disease prevalence, given that ovarian hormones serve as key regulators of brain functional and structural plasticity and undergo major fluctuations across the female lifespan (7-9). From a clinical perspective, there is a wellreported increase in depression susceptibility and initial evidence for cognitive impairment or decline during hormonal transition states, such as the postpartum period and perimenopause (9-14). What remains unknown, however, is the underlying mechanism of how fluctuations in ovarian hormones interact with other biological factors to influence brain structure, function, and chemistry. While this line of research has translational relevance for over half the population, neuroscience is notably guilty of female participant exclusion in research studies, with the male brain implicitly treated as the default model and only a minority of basic and clinical neuroscience studies including a female sample (15-18). Female underrepresentation in neuroscience directly limits opportunities for basic scientific discovery; and without basic knowledge of the biological underpinnings of sex differences, we cannot address critical sexdriven differences in pathology. Thus, my doctoral thesis aims to deliberately investigate the influence of sex and ovarian hormones on brain states in health as well as in vulnerability to depression and cognitive impairment:Table of Contents List of Abbreviations ..................................................................................................................... i List of Figures .............................................................................................................................. ii Acknowledgements .....................................................................................................................iii 1 INTRODUCTION .....................................................................................................................1 1.1 Lifespan approach: Sex, hormones, and metabolic risk factors for cognitive health .......3 1.2 Reproductive years: Healthy models of ovarian hormones, serotonin, and the brain ......4 1.2.1 Ovarian hormones and brain structure across the menstrual cycle ........................4 1.2.2 Serotonergic modulation and brain function in oral contraceptive users .................6 1.3 Neuropsychiatric risk models: Reproductive subtypes of depression ...............................8 1.3.1 Hormonal transition states and brain chemistry measured by PET imaging ...........8 1.3.2 Serotonin transporter binding across the menstrual cycle in PMDD patients .......10 2 PUBLICATIONS ....................................................................................................................12 2.1 Publication 1: Association of estradiol and visceral fat with structural brain networks and memory performance in adults .................................................................................13 2.2 Publication 2: Longitudinal 7T MRI reveals volumetric changes in subregions of human medial temporal lobe to sex hormone fluctuations ..............................................28 2.3 Publication 3: One-week escitalopram intake alters the excitation-inhibition balance in the healthy female brain ...............................................................................................51 2.4 Publication 4: Using positron emission tomography to investigate hormone-mediated neurochemical changes across the female lifespan: implications for depression ..........65 2.5 Publication 5: Increase in serotonin transporter binding across the menstrual cycle in patients with premenstrual dysphoric disorder: a case-control longitudinal neuro- receptor ligand PET imaging study ..................................................................................82 3 SUMMARY ...........................................................................................................................100 References ..............................................................................................................................107 Supplementary Publications ...................................................................................................114 Author Contributions to Publication 1 .....................................................................................184 Author Contributions to Publication 2 .....................................................................................186 Author Contributions to Publication 3 .....................................................................................188 Author Contributions to Publication 4 .....................................................................................190 Author Contributions to Publication 5 .....................................................................................191 Declaration of Authenticity ......................................................................................................193 Curriculum Vitae ......................................................................................................................194 List of Publications ................................................................................................................195 List of Talks and Posters ......................................................................................................19

    Deep Multimodality Image-Guided System for Assisting Neurosurgery

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    Intrakranielle Hirntumoren gehören zu den zehn hĂ€ufigsten bösartigen Krebsarten und sind fĂŒr eine erhebliche MorbiditĂ€t und MortalitĂ€t verantwortlich. Die grĂ¶ĂŸte histologische Kategorie der primĂ€ren Hirntumoren sind die Gliome, die ein Ă€ußerst heterogenes Erschei-nungsbild aufweisen und radiologisch schwer von anderen HirnlĂ€sionen zu unterscheiden sind. Die Neurochirurgie ist meist die Standardbehandlung fĂŒr neu diagnostizierte Gliom-Patienten und kann von einer Strahlentherapie und einer adjuvanten Temozolomid-Chemotherapie gefolgt werden. Die Hirntumorchirurgie steht jedoch vor großen Herausforderungen, wenn es darum geht, eine maximale Tumorentfernung zu erreichen und gleichzeitig postoperative neurologische Defizite zu vermeiden. Zwei dieser neurochirurgischen Herausforderungen werden im Folgenden vorgestellt. Erstens ist die manuelle Abgrenzung des Glioms einschließlich seiner Unterregionen aufgrund seines infiltrativen Charakters und des Vorhandenseins einer heterogenen KontrastverstĂ€rkung schwierig. Zweitens verformt das Gehirn seine Form ̶ die so genannte "Hirnverschiebung" ̶ als Reaktion auf chirurgische Manipulationen, Schwellungen durch osmotische Medikamente und AnĂ€sthesie, was den Nutzen prĂ€opera-tiver Bilddaten fĂŒr die Steuerung des Eingriffs einschrĂ€nkt. Bildgesteuerte Systeme bieten Ärzten einen unschĂ€tzbaren Einblick in anatomische oder pathologische Ziele auf der Grundlage moderner BildgebungsmodalitĂ€ten wie Magnetreso-nanztomographie (MRT) und Ultraschall (US). Bei den bildgesteuerten Instrumenten handelt es sich hauptsĂ€chlich um computergestĂŒtzte Systeme, die mit Hilfe von Computer-Vision-Methoden die DurchfĂŒhrung perioperativer chirurgischer Eingriffe erleichtern. Die Chirurgen mĂŒssen jedoch immer noch den Operationsplan aus prĂ€operativen Bildern gedanklich mit Echtzeitinformationen zusammenfĂŒhren, wĂ€hrend sie die chirurgischen Instrumente im Körper manipulieren und die Zielerreichung ĂŒberwachen. Daher war die Notwendigkeit einer BildfĂŒhrung wĂ€hrend neurochirurgischer Eingriffe schon immer ein wichtiges Anliegen der Ärzte. Ziel dieser Forschungsarbeit ist die Entwicklung eines neuartigen Systems fĂŒr die peri-operative bildgefĂŒhrte Neurochirurgie (IGN), nĂ€mlich DeepIGN, mit dem die erwarteten Ergebnisse der Hirntumorchirurgie erzielt werden können, wodurch die GesamtĂŒberle-bensrate maximiert und die postoperative neurologische MorbiditĂ€t minimiert wird. Im Rahmen dieser Arbeit werden zunĂ€chst neuartige Methoden fĂŒr die Kernbestandteile des DeepIGN-Systems der Hirntumor-Segmentierung im MRT und der multimodalen prĂ€ope-rativen MRT zur intraoperativen US-Bildregistrierung (iUS) unter Verwendung der jĂŒngs-ten Entwicklungen im Deep Learning vorgeschlagen. Anschließend wird die Ergebnisvor-hersage der verwendeten Deep-Learning-Netze weiter interpretiert und untersucht, indem fĂŒr den Menschen verstĂ€ndliche, erklĂ€rbare Karten erstellt werden. Schließlich wurden Open-Source-Pakete entwickelt und in weithin anerkannte Software integriert, die fĂŒr die Integration von Informationen aus Tracking-Systemen, die Bildvisualisierung und -fusion sowie die Anzeige von Echtzeit-Updates der Instrumente in Bezug auf den Patientenbe-reich zustĂ€ndig ist. Die Komponenten von DeepIGN wurden im Labor validiert und in einem simulierten Operationssaal evaluiert. FĂŒr das Segmentierungsmodul erreichte DeepSeg, ein generisches entkoppeltes Deep-Learning-Framework fĂŒr die automatische Abgrenzung von Gliomen in der MRT des Gehirns, eine Genauigkeit von 0,84 in Bezug auf den WĂŒrfelkoeffizienten fĂŒr das Bruttotumorvolumen. Leistungsverbesserungen wurden bei der Anwendung fort-schrittlicher Deep-Learning-AnsĂ€tze wie 3D-Faltungen ĂŒber alle Schichten, regionenbasier-tes Training, fliegende Datenerweiterungstechniken und Ensemble-Methoden beobachtet. Um Hirnverschiebungen zu kompensieren, wird ein automatisierter, schneller und genauer deformierbarer Ansatz, iRegNet, fĂŒr die Registrierung prĂ€operativer MRT zu iUS-Volumen als Teil des multimodalen Registrierungsmoduls vorgeschlagen. Es wurden umfangreiche Experimente mit zwei Multi-Location-Datenbanken durchgefĂŒhrt: BITE und RESECT. Zwei erfahrene Neurochirurgen fĂŒhrten eine zusĂ€tzliche qualitative Validierung dieser Studie durch, indem sie MRT-iUS-Paare vor und nach der deformierbaren Registrierung ĂŒberlagerten. Die experimentellen Ergebnisse zeigen, dass das vorgeschlagene iRegNet schnell ist und die besten Genauigkeiten erreicht. DarĂŒber hinaus kann das vorgeschlagene iRegNet selbst bei nicht trainierten Bildern konkurrenzfĂ€hige Ergebnisse liefern, was seine AllgemeingĂŒltigkeit unter Beweis stellt und daher fĂŒr die intraoperative neurochirurgische FĂŒhrung von Nutzen sein kann. FĂŒr das Modul "ErklĂ€rbarkeit" wird das NeuroXAI-Framework vorgeschlagen, um das Vertrauen medizinischer Experten in die Anwendung von KI-Techniken und tiefen neuro-nalen Netzen zu erhöhen. Die NeuroXAI umfasst sieben ErklĂ€rungsmethoden, die Visuali-sierungskarten bereitstellen, um tiefe Lernmodelle transparent zu machen. Die experimen-tellen Ergebnisse zeigen, dass der vorgeschlagene XAI-Rahmen eine gute Leistung bei der Extraktion lokaler und globaler Kontexte sowie bei der Erstellung erklĂ€rbarer Salienzkar-ten erzielt, um die Vorhersage des tiefen Netzwerks zu verstehen. DarĂŒber hinaus werden Visualisierungskarten erstellt, um den Informationsfluss in den internen Schichten des Encoder-Decoder-Netzwerks zu erkennen und den Beitrag der MRI-ModalitĂ€ten zur end-gĂŒltigen Vorhersage zu verstehen. Der ErklĂ€rungsprozess könnte medizinischen Fachleu-ten zusĂ€tzliche Informationen ĂŒber die Ergebnisse der Tumorsegmentierung liefern und somit helfen zu verstehen, wie das Deep-Learning-Modell MRT-Daten erfolgreich verar-beiten kann. Außerdem wurde ein interaktives neurochirurgisches Display fĂŒr die EingriffsfĂŒhrung entwickelt, das die verfĂŒgbare kommerzielle Hardware wie iUS-NavigationsgerĂ€te und Instrumentenverfolgungssysteme unterstĂŒtzt. Das klinische Umfeld und die technischen Anforderungen des integrierten multimodalen DeepIGN-Systems wurden mit der FĂ€higkeit zur Integration von (1) prĂ€operativen MRT-Daten und zugehörigen 3D-Volumenrekonstruktionen, (2) Echtzeit-iUS-Daten und (3) positioneller Instrumentenver-folgung geschaffen. Die Genauigkeit dieses Systems wurde anhand eines benutzerdefi-nierten Agar-Phantom-Modells getestet, und sein Einsatz in einem vorklinischen Operati-onssaal wurde simuliert. Die Ergebnisse der klinischen Simulation bestĂ€tigten, dass die Montage des Systems einfach ist, in einer klinisch akzeptablen Zeit von 15 Minuten durchgefĂŒhrt werden kann und mit einer klinisch akzeptablen Genauigkeit erfolgt. In dieser Arbeit wurde ein multimodales IGN-System entwickelt, das die jĂŒngsten Fort-schritte im Bereich des Deep Learning nutzt, um Neurochirurgen prĂ€zise zu fĂŒhren und prĂ€- und intraoperative Patientenbilddaten sowie interventionelle GerĂ€te in das chirurgi-sche Verfahren einzubeziehen. DeepIGN wurde als Open-Source-Forschungssoftware entwickelt, um die Forschung auf diesem Gebiet zu beschleunigen, die gemeinsame Nut-zung durch mehrere Forschungsgruppen zu erleichtern und eine kontinuierliche Weiter-entwicklung durch die Gemeinschaft zu ermöglichen. Die experimentellen Ergebnisse sind sehr vielversprechend fĂŒr die Anwendung von Deep-Learning-Modellen zur UnterstĂŒtzung interventioneller Verfahren - ein entscheidender Schritt zur Verbesserung der chirurgi-schen Behandlung von Hirntumoren und der entsprechenden langfristigen postoperativen Ergebnisse
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