Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

Recent Advances in Minimally Invasive Surgery

Minimally invasive surgery has become a common term in visceral as well as gynecologic surgery. It has almost evolved into its own surgical speciality over the past 20 years. Today, being firmly established in every subspeciality of visceral surgery, it is now no longer a distinct skillset, but a fixed part of the armamentarium of surgical options available. In every indication, the advantages of a minimally invasive approach include reduced intraoperative blood loss, less postoperative pain, and shorter rehabilitation times, as well as a marked reduction of overall and surgical postoperative morbidity. In the advent of modern oncologic treatment algorithms, these effects not only lower the immediate impact that an operation has on the patient, but also become important key steps in reducing the side-effects of surgery. Thus, they enable surgery to become a module in modern multi-disciplinary cancer treatment, which blends into multimodular treatment options at different times and prolongs and widens the possibilities available to cancer patients. In this quickly changing environment, the requirement to learn and refine not only open surgical but also different minimally invasive techniques on high levels deeply impact modern surgical training pathways. The use of modern elearning tools and new and praxis-based surgical training possibilities have been readily integrated into modern surgical education,which persists throughout the whole surgical career of modern gynecologic and visceral surgery specialists

Video Kinematic Evaluation: new insights on the cardiac mechanical function

The cardiac mechanical function plays a critical role in governing and regulating its performance under both normal and pathological conditions. The left ventricle has historically received more attention in both congenital and acquired heart diseases and was considered as the mainstay of normal hemodynamics. However, over the past few decades, there has been increasing recognition of the pivotal role of the right ventricle in determining functional performance status and prognosis in multiple conditions. Nonetheless, the ventricles should not be considered separately as they share the septum, are encircled with common myocardial fibers and are surrounded by the pericardium. Thus, changes in the filling of one ventricle may alter the mechanical function of its counterpart. This ventricular interdependence remains even after the removal of the pericardium because of constrictive pericarditis or during open chest surgery. Interestingly, during open chest surgery, only the right ventricle mechanical activity is visually checked by the surgeon and cardiologist due to the absence of an intraoperative imaging technique able to evaluate its complex function. Noteworthy, most of the imaging techniques available to clinicians are established for the assessment of the left ventricle, with the ejection fraction being the most used parameter. However, this value is a measure of global systolic function which comes short in identifying regional myocardial impairment and the mechanical contraction. Therefore, new approaches are needed to deeply investigate the mechanics of both ventricles and correctly assess the cardiac mechanical performance. In this thesis, I studied the mechanical function of the left ventricle through different modalities of cardiac magnetic resonance and employed an innovative imaging technique for the assessment of the right ventricle mechanical function during open chest surgery

PREDICTING NECROTIZING ENTEROCOLITIS IN HOSPITALIZED NEONATES

Necrotizing enterocolitis (NEC), a devastating disease of premature bowel, is challenging to predict. The disease is rare, with incompletely understood pathogenesis, rapid onset and progression, and insufficient diagnostic criteria. Using a systematic review of the literature, a cultivated dataset of published neonatal radiographs, and a publicly available neonatal critical care database, this dissertation examines novel approaches to improve predictions of NEC. First, in a review piece, we summarize surgical care for patients with NEC (Chapter 2). We provide a foundational framework to understanding NEC by describing the diverse presentations of the disease and discussing current best practices to reduce NEC-associated morbidity and mortality. Second, we conduct a systematic review of published prognostic models for predicting NEC onset and progression in hospitalized infants (Chapter 3). We find that published models have fair to poor discrimination of NEC outcomes and high risk of bias, limiting model clinical utility. Third, we develop an image classifier to support surgical resident recognition of pneumatosis intestinalis, a radiographic sign of NEC (Chapter 4). We find that a deep convolutional neural network trained on neonatal abdominal radiographs can successfully detect pneumatosis and performs comparably well to senior surgical residents. Fourth, we use the MIMIC III Clinical Database to develop an early warning score for NEC based on routinely available clinical data during an infant's stay in a neonatal intensive care unit (NICU) (Chapter 5). We find that models accurately predict NEC before disease onset, with first NEC risk detection occurring days previously. Fifth, in a perspective piece, we reflect on the promises and challenges of utilizing machine learning methods in NEC prediction and research (Chapter 6). We advocate for policy and practice changes to improve NEC prediction efforts. Overall, this dissertation highlights strengths and limitations of existing NEC prediction models and offers novel solutions to improve predictions of NEC in hospitalized neonates. We hope this dissertation helps researchers in pediatric surgery and neonatology identify steps to improve early detection of NEC, promote timely clinical management, and minimize the high morbidity and mortality of this disease

Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation

In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid–base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes

Management of Degenerative Cervical Myelopathy and Spinal Cord Injury

The present Special Issue is dedicated to presenting current research topics in DCM and SCI in an attempt to bridge gaps in knowledge for both of the two main forms of SCI. The issue consists of fourteen studies, of which the majority were on DCM, the more common pathology, while three studies focused on tSCI. This issue includes two narrative reviews, three systematic reviews and nine original research papers. Areas of research covered include image studies, predictive modeling, prognostic factors, and multiple systemic or narrative reviews on various aspects of these conditions. These articles include the contributions of a diverse group of researchers with various approaches to studying SCI coming from multiple countries, including Canada, Czech Republic, Germany, Poland, Switzerland, United Kingdom, and the United States

Annals of Intensive Care

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