Prospects of Artificial Intelligence in the Improvement of Healthcare Professions: A Review

In 1956, the development of engineering science led to the birth of the first intelligent machines. This has led to the term Artificial Intelligence (AI) coined by a scientist named John McCarthy. The basic purpose of AI is to minimise human cognitive function. Advanced computer technology allows humans to do comparative critical thinking and simulate intelligent behaviour by producing intelligent modelling to solve boost and uplift cracking problems, imaging knowledge, and making a decision.  Consequently, rapid analytical technique progress, powered by the increasing data availability in healthcare, has directed a paradigm shift in the healthcare system, especially in the analysis of medical imaging in the disease of oncology by detection of brain tumours. It helps the diagnosis of cancer stages based on the abnormal cell growth in the brain. AI is also important in diagnosis and treatment in other medical departments like dermatology, nephrology, ophthalmology, pathology, pulmonary medicine, endocrinology, gastroenterology, and neurology.  In recent years, AI has played a key role in pharmacy, drug delivery, drug discovery, drug formulation development, hospital pharmacy, and poly-pharmacology. The term AI has a broad range of applications in medicine, medical statistics, medical diagnosis, human biology, pharmacy, clinical, and robotics. Automated selective medication uses the scientific task approach of pharmacists and is only possible by the use of AI. Algorithmic tasks reserved by using AI automation and such type of AI demonstration are better than pharmacists in comparison. In general terms of AI, the minimal intervention of humans implies intelligent behaviour through computer models. The invention of robots is deemed the starting point of the AI journey. It started with the introduction of robotic biosynthetic machines utilised to support medical personnel. In the meantime, an AI is capable of analysing complex clinical and medical data where a potentially significant data set relationship can be used for treatment and predicting outcomes in the case study and diagnosis

Exploration of the Relationship Between the Fractal Dimension of Microcalcification Clusters and the Hurst Exponent of Background Tissue Disruption in Mammograms

Breast cancer is one of the most frequent cancers among women worldwide and holds the second place in cancer-related death. Mammography is the most commonly used screening technique, however, the dense nature of some breasts makes the analysis of mammograms challenging for radiologists. The 2D Wavelet Transform Modulus Maxima (WTMM) is one mathematical approach that is used to for the analysis of mammograms. In 2014, a team from the CompuMAINE Lab characterized differences between benign microcalcification clusters (MC) from malignant MC by calculating their fractal dimension, D, with the aid of the 2D WTMM method. In a different implementation of the 2D WTMM method, this same team did research in 2017 where they quantified tissue disruption in breast tissue microenvironment using the Hurst exponent, H. The goal of this study was to further explore the potential relationship between the fractality of MC clusters and tissue disruption in the microenvironment surrounding these clusters. Statistical relationships are explored between the fractal dimension, D, of MC clusters and the Hurst exponent, H measuring tissue disruption. A “2D fractal dimension vs. Hurst exponent plot” was graphed to show this relationship used to distinguish between benign and malignant cases. In the graph, a quadrilateral region extending horizontally from Hurst value of (0.2,0.8) centered at 0.5 and stretching vertically from fractal dimension value of (1.2,1.8) centered 1.5 was identified. Analysis of this region has showed that the 60% of the malignant cases and 21% benign cases are found inside the quadrilateral for CC view and 68% of the malignant cases and 12% of benign cases are found inside the region for MLO view. As a conclusion, based on the outcomes of this study one can hypothesize that with further analyses, loss of tissue homeostasis describing the state of the microenvironment of a breast tissue and the fractal nature of MC clusters have a quantifiable relationship to distinguish benign cases from malignant cases in mammogram analysis

Detección automática de la presencia de patología ocular en retinografías empleando técnicas de procesado de imágenes

La vista es uno de los sentidos de mayor importancia para la vida humana. En los últimos años el número de enfermedades oculares ha aumentado y las predicciones de los científicos es que van a seguir aumentando en los próximos años. Existen enfermedades oculares que se han convertido en importantes causas de pérdida de visión a nivel mundial como la retinopatía diabética (RD), el glaucoma, la degeneración macular asociada a la edad (DMAE) y las cataratas. Estas enfermedades oculares suelen provocar alteraciones en el ojo humano, que pueden detectarse observando el ojo. Una de las técnicas más extendidas para observar el fondo del ojo es la retinografía, que es una imagen digital a color de la retina. Esta imagen es muy útil para el diagnóstico de enfermedades que afectan al ojo como RD y DMAE, entre otras. No obstante, la creciente incidencia de algunas enfermedades oculares y la escasez de oftalmólogos especialistas provoca que el análisis de las retinografías sea una tarea compleja y laboriosa. El objetivo de este Trabajo Fin de Grado (TFG) ha sido el diseño y desarrollo de un método automático para diferenciar entre retinografías patológicas y no patológicas. Este método permitiría ayudar en el diagnóstico y cribado de los pacientes con enfermedades oculares y reducir la carga de trabajo a los oftalmólogos. Para ello, se partió de una base de datos (BD) formada por 1044 imágenes de calidad adecuada para su procesado automático. De ellas, 326 pertenecían a sujetos sanos y a 819 pacientes con algún tipo de patología. Estas imágenes se dividieron en un conjunto de entrenamiento (559 imágenes) y un conjunto de test (585 imágenes). En todos los casos, un oftalmólogo especialista indicó si las imágenes eran normales o patológicas.Grado en Ingeniería de Tecnologías de Telecomunicació

Self Designing Pattern Recognition System Employing Multistage Classification

Recently, pattern recognition/classification has received a considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, etc. In most of these applications, it is desirable to maintain the classification accuracy in the presence of corrupted and/or incomplete data. The quality of a given classification technique is measured by the computational complexity, execution time of algorithms, and the number of patterns that can be classified correctly despite any distortion. Some classification techniques that are introduced in the literature are described in Chapter one. In this dissertation, a pattern recognition approach that can be designed to have evolutionary learning by developing the features and selecting the criteria that are best suited for the recognition problem under consideration is proposed. Chapter two presents some of the features used in developing the set of criteria employed by the system to recognize different types of signals. It also presents some of the preprocessing techniques used by the system. The system operates in two modes, namely, the learning (training) mode, and the running mode. In the learning mode, the original and preprocessed signals are projected into different transform domains. The technique automatically tests many criteria over the range of parameters for each criterion. A large number of criteria are developed from the features extracted from these domains. The optimum set of criteria, satisfying specific conditions, is selected. This set of criteria is employed by the system to recognize the original or noisy signals in the running mode. The modes of operation and the classification structures employed by the system are described in details in Chapter three. The proposed pattern recognition system is capable of recognizing an enormously large number of patterns by virtue of the fact that it analyzes the signal in different domains and explores the distinguishing characteristics in each of these domains. In other words, this approach uses available information and extracts more characteristics from the signals, for classification purposes, by projecting the signal in different domains. Some experimental results are given in Chapter four showing the effect of using mathematical transforms in conjunction with preprocessing techniques on the classification accuracy. A comparison between some of the classification approaches, in terms of classification rate in case of distortion, is also given. A sample of experimental implementations is presented in chapter 5 and chapter 6 to illustrate the performance of the proposed pattern recognition system. Preliminary results given confirm the superior performance of the proposed technique relative to the single transform neural network and multi-input neural network approaches for image classification in the presence of additive noise

Digital Image Processing via Combination of Low-Level and High-Level Approaches.

With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. There is no clear definition how to divide the digital image processing, but normally, digital image processing includes three main steps: low-level, mid-level and highlevel processing. Low-level processing involves primitive operations, such as: image preprocessing to reduce the noise, contrast enhancement, and image sharpening. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. Finally, higher-level processing involves "making sense" of an ensemble of recognised objects, as in image analysis. Based on the theory just described in the last paragraph, this thesis is organised in three parts: Colour Edge and Face Detection; Hand motion detection; Hand Gesture Detection and Medical Image Processing. II In Colour Edge Detection, two new images G-image and R-image are built through colour space transform, after that, the two edges extracted from G-image and R-image respectively are combined to obtain the final new edge. In Face Detection, a skin model is built first, then the boundary condition of this skin model can be extracted to cover almost all of the skin pixels. After skin detection, the knowledge about size, size ratio, locations of ears and mouth is used to recognise the face in the skin regions. In Hand Motion Detection, frame differe is compared with an automatically chosen threshold in order to identify the moving object. For some special situations, with slow or smooth object motion, the background modelling and frame differencing are combined in order to improve the performance. In Hand Gesture Recognition, 3 features of every testing image are input to Gaussian Mixture Model (GMM), and then the Expectation Maximization algorithm (EM)is used to compare the GMM from testing images and GMM from training images in order to classify the results. In Medical Image Processing (mammograms), the Artificial Neural Network (ANN) and clustering rule are applied to choose the feature. Two classifier, ANN and Support Vector Machine (SVM), have been applied to classify the results, in this processing, the balance learning theory and optimized decision has been developed are applied to improve the performance

Detección de carcinomas en las imágenes de mamografía mediante tecnicas de procesamiento de imágenes y reconocimiento de patrones

Una de las principales enfermedades que ocasiona la muerte en el mundo es el CANCER y dentro de los diferentes tipos de cáncer el cáncer de Mama ocupa la segunda posición en la población mundial y la primera en las mujeres [8,10 ] . Dentro del proceso de diagnóstico se utilizan diferentes técnicas y la más utilizada es la mamografía, la cual consiste en una técnica no invasiva mediante la exposición del seno a un haz leve de rayos X generando una imagen digital en dos dimensiones la cual requiere de un análisis efectuado por personal especializado (radiólogos) quienes buscan dos tipos de información de carcinoma: Tumores (masas) y calcificaciones. Las calcificaciones o acumulaciones de calcio que se forman en los ductos de las glándulas mamarias acurde a su tamaño, formas y etiologías ofrecen diferentes propiedades en el diagnóstico de agrupaciones o clúster aislados y pequeños con una alta probabilidad de ser Malignas o Benignas.The use of CAD (Computer Added Detections) it s a one possible second option for radiology reader in early detections of mammography exams. This project used the protocol BIRADS for diagnosis s using image processing and classifications using the MIAS data base of 22 image of microcalfifications, this job search micro calcifications for diagnostic between benign and malign. The methodology used form and size descriptors for feature extraction, selections and classifications. The vector of feature used morphological descriptor how: area, convex area, perim, solidest and eccentricity. The project implement of classifications was decision tree, using solidest and eccentricity for node of decisions. In segmentations the morphological edge detector Top Hat has a good result.Magíster en Ingeniería ElectrónicaMaestrí

Development of a 3D Mouse Atlas Tool for Improved Non-Invasive Imaging of Orthotopic Mouse Models of Pancreatic Cancer.

PhD ThesesPancreatic cancer is the 10th most common cancer in the UK with 10,000 people a year being diagnosed. This form of cancer also has one of the lowest survival rates, with only 5% of patient surviving for 5 years (1). There has not been significant progress in the treatment of pancreatic cancer for the last 30 years (1). Recognition of this historic lack of progress has led to an increase in research effort and funding aimed at developing novel treatments for pancreatic cancer. This in turn has had an inflationary effect on the numbers of animals being used to study the effects of these treatments. Genetically engineered mouse models (GEMMs) are currently thought to be most appropriate for these types of studies as the manner in which the mice develop pancreatic tumours is much closer to that seen in the clinic. One such GEMM is the K-rasLSL.G12D/+;p53R172H/+;PdxCre (KPC) model (2) in which the mouse is born with normal pancreas and then develops PanIN lesions (one of the main lesions linked to pancreatic ductal adenocarcinoma (PDAC) (2)) at an accelerated rate. The KPC model is immune competent and because the tumours develop orthotopically in the pancreas, they have a relevant microenvironment and stromal makeup, suitable for testing of new therapeutic approaches. Unlike the human pancreas which is regular in shape, the mouse pancreas is a soft and spongy organ that has its dimensions defined to a large extent by the position of the organs that surround it, such as the kidney, stomach and spleen (3). This changes as pancreatic tumours develop, with the elasticity of the pancreas decreasing as the tissue becomes more desmoplastic. Because the tumours are deep within the body, disease burden is difficult to assess except by sacrificing groups of animals or by using non-invasive imaging. Collecting data by sacrificing groups of animals at different timepoints results in use of very high numbers per study. This is in addition to the fact that in the KPC model (similar to other GEMMs), fewer than 25% have the desired genetic makeup, meaning that 3-4 animals are destroyed for every one that is put into study (2). Therefore, in order to reduce the numbers of animals used in 5 pancreatic research, a non-invasive imaging tool that allows accurate assessment of pancreatic tumour burden longitudinally over time has been developed. Magnetic resonance imaging (MRI) has been used as it is not operator dependent (allowing it to be used by non-experts) and does not use ionising radiation which is a potential confounding factor when monitoring tumour development. The tool has been developed for use with a low field instrument (1T) which ensures its universal applicability as it will perform even better when used with magnets of field strength higher than 1T. This work has been carried out starting from an existing 3D computational mouse atlas and developing a mathematical model that can automatically detect and segment mouse pancreas as well as pancreatic tumours in MRI images. This has been achieved using multiple image analysis techniques including thresholding, texture analysis, object detection, edge detection, multi-atlas segmentation, and machine learning. Through these techniques, unnecessary information is removed from the image, the area of analysis is reduced, the pancreas is isolated (and then classified healthy or unhealthy), and - if unhealthy - the pancreas is evaluated to identify tumour location and volume. This semi-automated approach aims to aid researchers by reducing image analysis time (especially for non-expert users) and increasing both objectivity and statistical accuracy. It facilitates the use of MRI as a method of longitudinally tracking tumour development and measuring response to therapy in the same animal, thus reducing biological variability and leading to a reduction in group size. The MR images of mice and pancreatic tumours used in this work were obtained through studies already being conducted in order to reduce the number of animals used without having to compromise on the validity of results