A SYSTEMIC REVIEW ON THE FUTURE OF OBSTETRICS AND GYNECOLOGY: HARNESSING ARTIFICIAL INTELLIGENCE.

There is a burgeoning interest in the utilization of artificial intelligence (AI) within the realm of medical research, which exhibits considerable potential for forthcoming advancements. Obstetrics and gynecology encompass specialized disciplines that are associated with a heightened susceptibility to legal matters and suboptimal clinical outcomes. Multiple challenges exist in these domains, encompassing the comprehension of fetal physiology and the precise prognostication of prenatal and labor monitoring. The field of gynecology encounters intricacies within the realm of molecular biology, particularly in the comprehension of gynecological malignancies. This review aims to explore the potential applications of AI within the field of obstetrics and gynecology. The present study aims to investigate the potential utility of AI in enhancing comprehension of fundamental principles within various domains, with a particular focus on its potential impact on the healthcare sector. In the realm of obstetrics and gynecology, AI exhibits considerable potential in tackling enduring obstacles and aiding healthcare providers in their decision-making processes

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

Minimally Invasive Surgery in Gynecology

The first laparoscopic procedure was performed by 1901 by Georg Kelling in dogs while the first laparoscopic procedure in humans was performed by Hans Chrisitan Jacobaeus in 1910. Minimally invasive surgery offers multiple advantages over conventional laparotomy and is associated with reduced estimated blood loss, a lower incidence of complications and a shorter hospital stay and recovery. Over a century later, the vast majority of surgical procedures in gynecology are performed via minimal invasive technique. These include laparoscopy, minilaparoscopy, robotic surgery, laparoendoscopic single site surgery (LESS) and natural orifices transluminal endoscopic surgery. In this chapter we review these surgical techniques, analyze the main differences among these techniques and comment on their advantages and disadvantages

Topics on Cervical Cancer With an Advocacy for Prevention

Cervical Cancer is one of the leading cancers among women, especially in developing countries. Prevention and control are the most important public health strategies. Empowerment of women, education, "earlier" screening by affordable technologies like visual inspection, and treatment of precancers by cryotherapy/ LEEP are the most promising interventions to reduce the burden of cervical cancer.Dr Rajamanickam Rajkumar had the privilege of establishing a rural population based cancer registry in South India in 1996, as well as planning and implementing a large scale screening program for cervical cancer in 2000. The program was able to show a reduction in the incidence rate of cervical cancer by 25%, and reduction in mortality rate by 35%. This was the greatest inspiration for him to work on cerrvical cancer prevention, and he edited this book to inspire others to initiate such programs in developing countries. InTech - Open Access Publisher plays a major role in this crusade against cancer, and the authors have contributed to it very well

Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis

Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 stateof- the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression

Advances in Minimally Invasive Surgery

The minimally invasive approach in medicine is one of the most common areas of interest in surgery.Advances in Minimally Invasive Surgery describes the latest trends, indications, techniques, and approaches in minimally invasive surgery. It provides step-by-step instructions for both routine and diagnostic procedures via illustrations and video collection

Endometriosis

Endometriosis is a common and serious disease that is estimated to cost the world economy $9.7 billion a year. Most of these costs come from lost productivity at work. As such, it is important to help women receive earlier diagnosis and more effective treatment. This book presents a comprehensive overview of endometriosis, including information on molecular diagnostics and imaging methods for early detection as well as new, less-invasive treatments that preserve women’s fertility

The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

This book is a reprint of the Special Issue entitled "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Artificial intelligence is extending into the world of both digital radiology and digital pathology, and involves many scholars in the areas of biomedicine, technology, and bioethics. There is a particular need for scholars to focus on both the innovations in this field and the problems hampering integration into a robust and effective process in stable health care models in the health domain. Many professionals involved in these fields of digital health were encouraged to contribute with their experiences. This book contains contributions from various experts across different fields. Aspects of the integration in the health domain have been faced. Particular space was dedicated to overviewing the challenges, opportunities, and problems in both radiology and pathology. Clinal deepens are available in cardiology, the hystopathology of breast cancer, and colonoscopy. Dedicated studies were based on surveys which investigated students and insiders, opinions, attitudes, and self-perception on the integration of artificial intelligence in this field

Optimising outcomes for potentially resectable pancreatic cancer through personalised predictive medicine : the application of complexity theory to probabilistic statistical modeling

Survival outcomes for pancreatic cancer remain poor. Surgical resection with adjuvant therapy is the only potentially curative treatment, but for many people surgery is of limited benefit. Neoadjuvant therapy has emerged as an alternative treatment pathway however the evidence base surrounding the treatment of potentially resectable pancreatic cancer is highly heterogeneous and fraught with uncertainty and controversy. This research seeks to engage with conjunctive theorising by avoiding simplification and abstraction to draw on different kinds of data from multiple sources to move research towards a theory that can build a rich picture of pancreatic cancer management pathways as a complex system. The overall aim is to move research towards personalised realistic medicine by using personalised predictive modeling to facilitate better decision making to achieve the optimisation of outcomes. This research is theory driven and empirically focused from a complexity perspective. Combining operational and healthcare research methodology, and drawing on influences from complementary paradigms of critical realism and systems theory, then enhancing their impact by using Cilliers’ complexity theory ‘lean ontology’, an open-world ontology is held and both epistemic reality and judgmental relativity are accepted. The use of imperfect data within statistical simulation models is explored to attempt to expand our capabilities for handling the emergent and uncertainty and to find other ways of relating to complexity within the field of pancreatic cancer research. Markov and discrete-event simulation modelling uncovered new insights and added a further dimension to the current debate by demonstrating that superior treatment pathway selection depended on individual patient and tumour factors. A Bayesian Belief Network was developed that modelled the dynamic nature of this complex system to make personalised prognostic predictions across competing treatments pathways throughout the patient journey to facilitate better shared clinical decision making with an accuracy exceeding existing predictive models.Survival outcomes for pancreatic cancer remain poor. Surgical resection with adjuvant therapy is the only potentially curative treatment, but for many people surgery is of limited benefit. Neoadjuvant therapy has emerged as an alternative treatment pathway however the evidence base surrounding the treatment of potentially resectable pancreatic cancer is highly heterogeneous and fraught with uncertainty and controversy. This research seeks to engage with conjunctive theorising by avoiding simplification and abstraction to draw on different kinds of data from multiple sources to move research towards a theory that can build a rich picture of pancreatic cancer management pathways as a complex system. The overall aim is to move research towards personalised realistic medicine by using personalised predictive modeling to facilitate better decision making to achieve the optimisation of outcomes. This research is theory driven and empirically focused from a complexity perspective. Combining operational and healthcare research methodology, and drawing on influences from complementary paradigms of critical realism and systems theory, then enhancing their impact by using Cilliers’ complexity theory ‘lean ontology’, an open-world ontology is held and both epistemic reality and judgmental relativity are accepted. The use of imperfect data within statistical simulation models is explored to attempt to expand our capabilities for handling the emergent and uncertainty and to find other ways of relating to complexity within the field of pancreatic cancer research. Markov and discrete-event simulation modelling uncovered new insights and added a further dimension to the current debate by demonstrating that superior treatment pathway selection depended on individual patient and tumour factors. A Bayesian Belief Network was developed that modelled the dynamic nature of this complex system to make personalised prognostic predictions across competing treatments pathways throughout the patient journey to facilitate better shared clinical decision making with an accuracy exceeding existing predictive models