Long axial field of view PET scanners:a road map to implementation and new possibilities

In this contribution, several opportunities and challenges for long axial field of view (LAFOV) PET are described. It is an anthology in which the main issues have been highlighted. A consolidated overview of the camera system implementation, business and financial plan, opportunities and challenges is provided. What the nuclear medicine and molecular imaging community can expect from these new PET/CT scanners is the delivery of more comprehensive information to the clinicians for advancing diagnosis, therapy evaluation and clinical research

INfluence of Successful Periodontal Intervention in Renal Disease (INSPIRED):study protocol for a randomised controlled pilot clinical trial

Abstract Background Patients with chronic kidney disease (CKD) exhibit increased morbidity and mortality which is associated with an increased systemic inflammatory burden. Identifying and managing comorbid diseases that contribute to this load may inform novel care pathways that could have a beneficial impact on the morbidity/mortality associated with CKD. Periodontitis, a highly prevalent, chronic inflammatory disease affecting the supporting structures of teeth, is associated with an increased systemic inflammatory and oxidative stress burden and the successful treatment of periodontitis has been shown to reduce both. This pilot study aims to gather data to inform a definitive study into the impact of successful periodontal treatment on the cardio-renal health of patients with CKD. Methods/design This pilot study will employ a randomised, controlled, parallel-group design. Sixty adult patients, with CKD with a high risk of progression and with periodontitis, from the Queen Elizabeth Hospital, Birmingham, will be randomised to receive either immediate, intensive periodontal treatment (n = 30) or treatment at a delay of 12 months (n = 30). Patients will be excluded if they have reached end-stage renal disease or have received specialist periodontal treatment in the previous year. Periodontal treatment will be delivered under local anaesthetic, on an outpatient basis, over several visits by a qualified dental hygienist at the Birmingham Dental Hospital, UK. Patients in the delayed-treatment arm will continue to receive the standard community level of periodontal care for a period of 12 months followed by the intensive periodontal treatment. Randomization will occur using a centralised telephone randomisation service, following baseline assessments. The assessor of periodontal health will be blinded to the patients’ treatment allocation. Patients in either arm will be followed up at 3-monthly intervals for 18 months. Aside from the pilot outcomes to inform the practicalities of a larger trial later, data on cardio-renal function, periodontal health and patient-reported outcomes will be collected at each time point. Discussion This pilot randomised controlled trial will investigate the viability of undertaking a larger-scale study investigating the effect of treating periodontitis and maintaining periodontal health on cardio-renal outcomes in patients with CKD. Trial registration National Institute of Health Research (NIHR) Clinical Research Network (UKCRN ID: 18458), ID: ISRCTN10227738 . Registered retrospectively to both registers on 23 April 2015

A study to scope structures, processes and related outcomes of clinical pharmacy practice as part of the multidisciplinary care of patients with chronic kidney disease.

Chronic kidney disease (CKD) is a complex health-related comorbidity with an enormous economic burden on any healthcare system globally. Clinical pharmacy services have potential to contribute significantly to the multidisciplinary team providing safe, effective and economic care for patients. However, published literature shows there is a lack of robust evidence for the role of clinical pharmacists in providing care to patients with CKD. The overall aim of this doctoral research was to investigate the structures, processes and related outcomes of clinical pharmacy practice in the care of patients with CKD. This doctoral research was undertaken under two stages. Stage 1 was a systematic review to appraise, synthesize and present the available evidence on the structures, processes and related outcomes of clinical pharmacy practice in the care of patients with CKD. While there is some evidence of positive impact on clinical, humanistic and economic outcomes, this evidence is generally of low quality and insufficient volume. While the existing evidence is in favour of pharmacists' involvement in the multidisciplinary team providing care to patients with CKD, more high-quality research is warranted. A sequential explanatory design underpinned by the Consolidated Framework of Implementation Research (CFIR) was employed in Stage 2 of this doctoral research. It was executed in two phases of data generation. The findings from the first phase informed the subsequent phase. In Phase 1, an online theoretically based cross-sectional survey was conducted on the behaviours and experiences of clinical pharmacists caring for patients with CKD. Seventy-one respondents completed the survey with a response rate of 50%. The majority of respondents provided general pharmaceutical care to dialysis and transplant patients, were confident in their abilities and tried new ways of working including independent prescribing. There was high level of agreement among the respondents in relation to CFIR items for clinical practice. Most respondents strongly agreed / agreed with CFIR items for prescribing practice, yet 39.6% disagreed that they had sufficient cover for their prescribing duties when they are away. Many expressed that lack of resources was the main barrier to providing more advanced care. Further work is needed to explore these matters in more depth. Phase 2 of Stage 2 involved a semi-structured qualitative interview with clinical pharmacist prescribers' members of the UK Renal Pharmacy Group involved in the care of patients with CKD. Data saturation was confirmed after completing and analysing 14 interviews. The key findings of the interviews demonstrated positive views of prescribing practice for patients with CKD among the pharmacists. Underpinning the research with CFIR helped identify the key facilitators and barriers to the implementation of prescribing practice and facilitated identifying key areas for further developing the service. Overall, this doctoral research produced original contribution to knowledge in the area of clinical pharmacy services in the care for patients with CKD in the UK and with emphasis of prescribing practice. The rigorous and robust findings from Stage 2 of the research can help further develop pharmacy practice and prescribing practice in the care for patients with CKD. More research is needed to explore the potential to implement such practices in a wider context

Computational Approaches to Assisting Patients\u27 Medical Comprehension from Electronic Health Records

Patient-centered care has been established as a fundamental approach to improve the quality of health care in a seminal report by the Institute of Medicine published at the start of the century. Improved access to health information and demand for greater transparency contributed to its move into the mainstream. Research has also demonstrated that actively involving patients in the management of their own health can lead to better outcomes, and potentially lower costs. However, despite the efforts in many areas of medicine to embrace patient-centered care, engaging patients is still considered a challenge. One of the barriers is the lack of effective tools to help patients understand their health conditions, options and their consequences. Patient portals are now widely adopted by hospitals and other healthcare practices to provide patients with the capabilities to view their own Electronic Health Records. They are a rich resource of information for patients. However, the language in the records are generally difficult for patients without training in medicine to understand. Furthermore, the amount of information can often be overwhelming as well. In this work, we propose computational approaches to foster patient engagement from three aspects by exploiting the rich information in the medical records. First, we design a framework to automatically generate health literacy instruments to measure a patient\u27s literacy levels. This framework exploits readily available large scale corpora to generate instruments in a commonly used test format. Second, we investigate methods that can determine the readability of complex documents such as health records. We propose to rank document readability, instead of assigning a grade level or a pre-defined difficulty category. Lastly, we examine the problem of finding targeted educational materials to facilitate patient comprehension of medical notes. We study methods to formulate effective queries from specialized and long clinical narratives. In addition, we propose a neural network based method to identify medical concepts that are important to patients. The three aspects of this work address the issues of the overabundance and technical complexity of medical language in health records. We demonstrate that our approaches are effective with various experiments and evaluation metric

Data mart based research in heart surgery

Arnrich B. Data mart based research in heart surgery. Bielefeld (Germany): Bielefeld University; 2006.The proposed data mart based information system has proven to be useful and effective in the particular application domain of clinical research in heart surgery. In contrast to common data warehouse systems who are focused primarily on administrative, managerial, and executive decision making, the primary objective of the designed and implemented data mart was to provide an ongoing, consolidated and stable research basis. Beside detail-oriented patient data also aggregated data are incorporated in order to fulfill multiple purposes. Due to the chosen concept, this technique integrates the current and historical data from all relevant data sources without imposing any considerable operational or liability contract risk for the existing hospital information systems (HIS). By this means the possible resistance of involved persons in charge can be minimized and the project specific goals effectively met. The challenges of isolated data sources, securing a high data quality, data with partial redundancy and consistency, valuable legacy data in special file formats, and privacy protection regulations are met with the proposed data mart architecture. The applicability was demonstrated in several fields, including (i) to permit easy comprehensive medical research, (ii) to assess preoperative risks of adverse surgical outcomes, (iii) to get insights into historical performance changes, (iv) to monitor surgical results, (v) to improve risk estimation, and (vi) to generate new knowledge from observational studies. The data mart approach allows to turn redundant data from the electronically available hospital data sources into valuable information. On the one hand, redundancies are used to detect inconsistencies within and across HIS. On the other hand, redundancies are used to derive attributes from several data sources which originally did not contain the desired semantic meaning. Appropriate verification tools help to inspect the extraction and transformation processes in order to ensure a high data quality. Based on the verification data stored during data mart assembly, various aspects on the basis of an individual case, a group, or a specific rule can be inspected. Invalid values or inconsistencies must be corrected in the primary source data bases by the health professionals. Due to all modifications are automatically transferred to the data mart system in a subsequent cycle, a consolidated and stable research data base is achieved throughout the system in a persistent manner. In the past, performing comprehensive observational studies at the Heart Institute Lahr had been extremely time consuming and therefore limited. Several attempts had already been conducted to extract and combine data from the electronically available data sources. Dependent on the desired scientific task, the processes to extract and connect the data were often rebuilt and modified. Consequently the semantics and the definitions of the research data changed from one study to the other. Additionally, it was very difficult to maintain an overview of all data variants and derived research data sets. With the implementation of the presented data mart system the most time and effort consuming process with conducting successful observational studies could be replaced and the research basis remains stable and leads to reliable results

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

Challenges and Opportunities of End-to-End Learning in Medical Image Classification

Das Paradigma des End-to-End Lernens hat in den letzten Jahren die Bilderkennung revolutioniert, aber die klinische Anwendung hinkt hinterher. Bildbasierte computergestützte Diagnosesysteme basieren immer noch weitgehend auf hochtechnischen und domänen-spezifischen Pipelines, die aus unabhängigen regelbasierten Modellen bestehen, welche die Teilaufgaben der Bildklassifikation wiederspiegeln: Lokalisation von auffälligen Regionen, Merkmalsextraktion und Entscheidungsfindung. Das Versprechen einer überlegenen Entscheidungsfindung beim End-to-End Lernen ergibt sich daraus, dass domänenspezifische Zwangsbedingungen von begrenzter Komplexität entfernt werden und stattdessen alle Systemkomponenten gleichzeitig, direkt anhand der Rohdaten, und im Hinblick auf die letztendliche Aufgabe optimiert werden. Die Gründe dafür, dass diese Vorteile noch nicht den Weg in die Klinik gefunden haben, d.h. die Herausforderungen, die sich bei der Entwicklung Deep Learning-basierter Diagnosesysteme stellen, sind vielfältig: Die Tatsache, dass die Generalisierungsfähigkeit von Lernalgorithmen davon abhängt, wie gut die verfügbaren Trainingsdaten die tatsächliche zugrundeliegende Datenverteilung abbilden, erweist sich in medizinische Anwendungen als tiefgreifendes Problem. Annotierte Datensätze in diesem Bereich sind notorisch klein, da für die Annotation eine kostspielige Beurteilung durch Experten erforderlich ist und die Zusammenlegung kleinerer Datensätze oft durch Datenschutzauflagen und Patientenrechte erschwert wird. Darüber hinaus weisen medizinische Datensätze drastisch unterschiedliche Eigenschaften im Bezug auf Bildmodalitäten, Bildgebungsprotokolle oder Anisotropien auf, und die oft mehrdeutige Evidenz in medizinischen Bildern kann sich auf inkonsistente oder fehlerhafte Trainingsannotationen übertragen. Während die Verschiebung von Datenverteilungen zwischen Forschungsumgebung und Realität zu einer verminderten Modellrobustheit führt und deshalb gegenwärtig als das Haupthindernis für die klinische Anwendung von Lernalgorithmen angesehen wird, wird dieser Graben oft noch durch Störfaktoren wie Hardwarelimitationen oder Granularität von gegebenen Annotation erweitert, die zu Diskrepanzen zwischen der modellierten Aufgabe und der zugrunde liegenden klinischen Fragestellung führen. Diese Arbeit untersucht das Potenzial des End-to-End-Lernens in klinischen Diagnosesystemen und präsentiert Beiträge zu einigen der wichtigsten Herausforderungen, die derzeit eine breite klinische Anwendung verhindern. Zunächst wird der letzten Teil der Klassifikations-Pipeline untersucht, die Kategorisierung in klinische Pathologien. Wir demonstrieren, wie das Ersetzen des gegenwärtigen klinischen Standards regelbasierter Entscheidungen durch eine groß angelegte Merkmalsextraktion gefolgt von lernbasierten Klassifikatoren die Brustkrebsklassifikation im MRT signifikant verbessert und eine Leistung auf menschlichem Level erzielt. Dieser Ansatz wird weiter anhand von kardiologischer Diagnose gezeigt. Zweitens ersetzen wir, dem Paradigma des End-to-End Lernens folgend, das biophysikalische Modell, das für die Bildnormalisierung in der MRT angewandt wird, sowie die Extraktion handgefertigter Merkmale, durch eine designierte CNN-Architektur und liefern eine eingehende Analyse, die das verborgene Potenzial der gelernten Bildnormalisierung und einen Komplementärwert der gelernten Merkmale gegenüber den handgefertigten Merkmalen aufdeckt. Während dieser Ansatz auf markierten Regionen arbeitet und daher auf manuelle Annotation angewiesen ist, beziehen wir im dritten Teil die Aufgabe der Lokalisierung dieser Regionen in den Lernprozess ein, um eine echte End-to-End-Diagnose baserend auf den Rohbildern zu ermöglichen. Dabei identifizieren wir eine weitgehend vernachlässigte Zwangslage zwischen dem Streben nach der Auswertung von Modellen auf klinisch relevanten Skalen auf der einen Seite, und der Optimierung für effizientes Training unter Datenknappheit auf der anderen Seite. Wir präsentieren ein Deep Learning Modell, das zur Auflösung dieses Kompromisses beiträgt, liefern umfangreiche Experimente auf drei medizinischen Datensätzen sowie eine Serie von Toy-Experimenten, die das Verhalten bei begrenzten Trainingsdaten im Detail untersuchen, und publiziren ein umfassendes Framework, das unter anderem die ersten 3D-Implementierungen gängiger Objekterkennungsmodelle umfasst. Wir identifizieren weitere Hebelpunkte in bestehenden End-to-End-Lernsystemen, bei denen Domänenwissen als Zwangsbedingung dienen kann, um die Robustheit von Modellen in der medizinischen Bildanalyse zu erhöhen, die letztendlich dazu beitragen sollen, den Weg für die Anwendung in der klinischen Praxis zu ebnen. Zu diesem Zweck gehen wir die Herausforderung fehlerhafter Trainingsannotationen an, indem wir die Klassifizierungskompnente in der End-to-End-Objekterkennung durch Regression ersetzen, was es ermöglicht, Modelle direkt auf der kontinuierlichen Skala der zugrunde liegenden pathologischen Prozesse zu trainieren und so die Robustheit der Modelle gegenüber fehlerhaften Trainingsannotationen zu erhöhen. Weiter adressieren wir die Herausforderung der Input-Heterogenitäten, mit denen trainierte Modelle konfrontiert sind, wenn sie an verschiedenen klinischen Orten eingesetzt werden, indem wir eine modellbasierte Domänenanpassung vorschlagen, die es ermöglicht, die ursprüngliche Trainingsdomäne aus veränderten Inputs wiederherzustellen und damit eine robuste Generalisierung zu gewährleisten. Schließlich befassen wir uns mit dem höchst unsystematischen, aufwendigen und subjektiven Trial-and-Error-Prozess zum Finden von robusten Hyperparametern für einen gegebene Aufgabe, indem wir Domänenwissen in ein Set systematischer Regeln überführen, die eine automatisierte und robuste Konfiguration von Deep Learning Modellen auf einer Vielzahl von medizinischen Datensetzen ermöglichen. Zusammenfassend zeigt die hier vorgestellte Arbeit das enorme Potenzial von End-to-End Lernalgorithmen im Vergleich zum klinischen Standard mehrteiliger und hochtechnisierter Diagnose-Pipelines auf, und präsentiert Lösungsansätze zu einigen der wichtigsten Herausforderungen für eine breite Anwendung unter realen Bedienungen wie Datenknappheit, Diskrepanz zwischen der vom Modell behandelten Aufgabe und der zugrunde liegenden klinischen Fragestellung, Mehrdeutigkeiten in Trainingsannotationen, oder Verschiebung von Datendomänen zwischen klinischen Standorten. Diese Beiträge können als Teil des übergreifende Zieles der Automatisierung von medizinischer Bildklassifikation gesehen werden - ein integraler Bestandteil des Wandels, der erforderlich ist, um die Zukunft des Gesundheitswesens zu gestalten