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

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems .

International audienceThis paper studies the prediction of the output voltage reduction caused by degradation during nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) which use as input the measures of the fuel cell output voltage during operation. The paper presents the architecture of the ANFIS and studies the selection of its parameters. As the output voltage cannot be represented as a periodical signal, the paper proposes to predict its temporal variation which is then used to construct the prediction of the output voltage. The paper also proposes to split this signal in two components: normal operation and external perturbations. The second component cannot be predicted and then it is not used to train the ANFIS. The performance of the prediction is evaluated on the output voltage of two fuel cells during a long term operation (1000 hours). Validation results suggest that the proposed technique is well adapted to predict degradation in fuel cell systems

Computer image registration techniques applied to nuclear medicine images

Modern medicine has been using imaging as a fundamental tool in a wide range of applications. Consequently, the interest in automated registration of images from either the same or different modalities has increased. In this chapter, computer techniques of image registration are reviewed, and cover both their classification and the main steps involved. Moreover, the more common geometrical transforms, optimization and interpolation algorithms are described and discussed. The clinical applications examined emphases nuclear medicine

Data registration and fusion for cardiac applications

The registration and fusion of information from multiple cardiac image modalities such as magnetic resonance imaging (MRI), X-ray computed tomography (CT), positron emission tomography (PET) and single photon emission computed tomography (SPECT) has been of increasing interest to the medical community as tools for furthering physiological understanding and for diagnostic of ischemic heart diseases. Ischemic heart diseases and their consequence, myocardial infarct, are the leading cause of mortality in industrial countries. In cardiac image registration and data fusion, the combination of structural information from MR images and functional information from PET and SPECT is of special interest in the estimation of myocardial function and viability. Cardiac image registration is a more complex problem than brain image registration. The non-rigid motion of the heart and the thorax structures introduce additional difficulties in registration. In this thesis the goal was develop methods for cardiac data registration and fusion. A rigid registration method was developed to register cardiac MR and PET images. The method was based on the registration of the segmented thorax structures from MR and PET transmission images. The thorax structures were segmented from images using deformable models. A MR short axis registration with PET emission image was also derived. The rigid registration method was evaluated using simulated images and clinical MR and PET images from ten patients with multivessel coronary artery diseases. Also an elastic registration method was developed to register intra-patient cardiac MR and PET images and inter-patient head MR images. In the elastic registration method, a combination of mutual information, gradient information and smoothness of transformation was used to guide the deformation of one image towards another image. An approach for the creation of 3-D functional maps of the heart was also developed. An individualized anatomical heart model was extracted from the MR images. A rigid registration of anatomical MR images and PET metabolic images was carried out using surface based registration, and the registration of MR images with magnetocardiography (MCG) data using external markers. The method resulted in a 3-D anatomical and functional model of the heart that included structural information from the MRI and functional information from the PET and MCG. Different error sources in the registration method of the MR images and MCG data was also evaluated in this thesis. The results of the rigid MR-PET registration method were also used in the comparison of multimodality MR imaging methods to PET.reviewe