A Review of Medical Imaging Innovations that Impacted Patient Care in Recent Decades as Link to Future Trends

Background: Medical Imaging has witnessed a revolution in technological advancement, being in the forefront among other disciplines in the health sector. Most of the earlier modalities that were largely analogue and mechanical have been replaced by automated and digitized technology. Objective: To track the developments and innovations in certain aspects of medical imaging that have impacted positively on patient care. Methods: Relevant literature were searched physically and online for both old and modern technological innovations in medical imaging and patient care. Results: There have been new technologies such as computed tomography, magnetic resonance imaging and the various ramifications of ultrasonography. Innovations in imaging modalities have brought increased diagnostic accuracy, much as examination time has been drastically shortened and radiation dose levels minimized or completely dispensed with. Manufacturing of portable equipment means that technology can now be taken to the patient and more time is dedicated to patient care. Introduction of digital radiography and Picture Archiving and Communication Systems have further impacted positively on efficiency and effectiveness of service delivery. Graduate degree programmes have invigorated radiographers’ drive for the discovery of new and better ways of diagnosis and treatment through research. Conclusion: Innovations in technology have led to miniaturization of equipment making it possible to take services to the critically ill patients, thereby improving patients’ accessibility to medical care. Also patients’ exposure to ionizing radiation has reduced due to improvement in research and development of new modalities using radiant energies other than ionizing radiation.&nbsp

Tuberculosis control, and the where and why of artificial intelligence

Countries aiming to reduce their tuberculosis (TB) burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health) in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient\u2019s care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB

The Reference Image Database to Evaluate Response to Therapy in Lung Cancer (RIDER) Project: A Resource for the Development of Change‐Analysis Software

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110105/1/cptclpt2008161.pd

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Table of Contents

Contains the table of contents

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning and attention mechanisms to study the interactions between different biomedical data to detect and diagnose diseases. We conduct extensive testing on biomedical data. The results show the benefits of using deep learning technologies in the field of artificial intelligence of medical things to diagnose diseases in the healthcare decision-making process. For example, the disease detection rate using the proposed methodology achieves 92%, which is greatly improved compared to the higher-level disease detection models.publishedVersio

Artificial intelligence of medical things for disease detection using ensemble deep learning and attention mechanism

In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning and attention mechanisms to study the interactions between different biomedical data to detect and diagnose diseases. We conduct extensive testing on biomedical data. The results show the benefits of using deep learning technologies in the field of artificial intelligence of medical things to diagnose diseases in the healthcare decision-making process. For example, the disease detection rate using the proposed methodology achieves 92%, which is greatly improved compared to the higher-level disease detection models.publishedVersio

Anatomical Variation and Clinical Diagnosis

In the anatomical sciences, it has long been recognized that the human body displays a range of morphological patterns and arrangements, often termed “anatomical variation”. Variations are relatively common throughout the body and may cause or contribute to significant medical conditions. An understanding of normal anatomical variation is vital for performing a broad range of surgical and other medical procedures and treatment modalities. However, despite their importance to effective diagnosis and treatment, such variations are often overlooked in medical school curricula and clinical practice. Recent advances in imaging techniques and a renewed interest in variation in dissection-based gross anatomy laboratories have facilitated the identification of many such variants. The aim of this Special Issue of Diagnostics is to highlight previously under-recognized anatomical variations and to discuss them in a clinical context. In particular, this Special Issue focuses on variants that have specific implications for diagnosis and treatment and explores their potential consequences. The scope of this Special Issue includes studies on gross anatomy, radiology, surgical anatomy, histology, and neuroanatomy