1 - USE OF ARTIFICIAL INTELLIGENCE TO PREDICT VOLUME OF BRAIN AND INFARCTION GROWTH RATE; A STEP TOWARDS PRECISION MEDICINE


Book Description

Background: Background. The final human brain infarction volume [IV] and infarction growth rate [IGR] are strong predictors of clinical outcome. IGR is dynamic with wide variations in speed of growth. The conventional mathematical techniques are unable to predict IV and IGR.Patients and Methods: In this, single-center, prospective study data was collected on all acute stroke patients treated by intravenous thrombolysis and/or mechanical thrombectomy between January 2014 and December 2016.Infarct Growth Rate calculation [IGR]. Infarct volumes were measured on the baseline and 24-hour CT. For IGR calculation we assumed the stroke volume to be zero prior to stroke onset.Infarct growth rate 1[IGR1] = u0394 volume (IV CT1u20130)/u0394 time (time CT1- stroke onset time)Second infarct growth rate [IGR2] was measured on second CT [CT2]IGR2 = u0394 volume/ u0394 time = (IV CT2- IV CT1)/ (time CT2-time CT1).To quantify the difference between the estimated IGR and actual IGR mean square error [MSE] was usedufffdufffdufffdufffdufffdufffd=1/ufffdufffd u2211128_(ufffdufffd=0)^(ufffdufffdu22121)u2592(u3016ufffdufffdufffdufffdufffdufffdu3017_(u3016ufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdu3017_ufffdufffd )u2212u3016ufffdufffdufffdufffdufffdufffdu3017_(u3016ufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdufffdu3017_ufffdufffd ) )^2ResultsA total of 134 consecutive patients with an acute ischemic infarction secondary to middle cerebral artery occlusion were treated with a mean time to treatment from symptom onset of 213.27 +/- 227.12 minutes. A bivariate analysis showed the clot burden score [p=0.003], and time to treatment [p=0.78] was negatively/inversely correlated with IGR, while IGR 1 positively correlated with IGR2 [p=0.067]. The IGR2 was significantly higher when the collateral circulation score was low compared to a high score [p=0.001]. An unfavourable modified treatment in cerebral infarction (mTICI) score had a significantly higher IGR2 compared to those who had a favourable mTICI score [p=0.035] (Table 2 methods section). The demographics, clinical and radiological details are provided in the methods section [Table 2 methods section]. A comparison of ANFIS training and testing data [Table 3, methods section] showed no statistically significant difference except better collateral score [p=0.024] in the testing group with lower IGR2 [p=0.03]. The ANFIS based model was able to predict the IGR2 and infarction volume, calculated from the predicted IGR 2, without any statistically significant difference compared to the original data [p=0.001] [Figure 1, Table 2]. The mean square error was 8.95% with an accuracy of 91.05%. In addition, ANFIS-predicted values were in agreement with the original data as shown by skewness, cross-correlation and Cosine Similarity [Table 2].Conclusions : We showed an ANFIS based model to predict second brain IV [IV2] and second IGR [IGR2] depending on first imaging study, using prospectively collected data of acute stroke patients. The model predicted the IGR2 and IV2 without any significant difference to the original data [p=0.001]. We achieved an accuracy of 91.05% in predicting IV2 and IGR2 by combining demographic, clinical and radiological data and then applying ANFIS. Our study has the potential to help in more effective patient selection for treatment, particular therapies like extended hours thrombectomy and hemicraniectomy for malignant brain strokes and predict outcome in ischemic stroke, a step towards precision medicine.




Artificial Intelligence in Healthcare


Book Description

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data




Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare


Book Description

Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer science’s use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book: · Covers broad AI topics in drug development, precision medicine, and healthcare. · Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods. · Introduces the similarity principle and related AI methods for both big and small data problems. · Offers a balance of statistical and algorithm-based approaches to AI. · Provides examples and real-world applications with hands-on R code. · Suggests the path forward for AI in medicine and artificial general intelligence. As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code.




Artificial Intelligence in Medical Imaging


Book Description

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.







Accelerated Path to Cures


Book Description

Accelerated Path to Cures provides a transformative perspective on the power of combining advanced computational technologies, modeling, bioinformatics and machine learning approaches with nonclinical and clinical experimentation to accelerate drug development. This book discusses the application of advanced modeling technologies, from target identification and validation to nonclinical studies in animals to Phase 1-3 human clinical trials and post-approval monitoring, as alternative models of drug development. As a case of successful integration of computational modeling and drug development, we discuss the development of oral small molecule therapeutics for inflammatory bowel disease, from the application of docking studies to screening new chemical entities to the development of next-generation in silico human clinical trials from large-scale clinical data. Additionally, this book illustrates how modeling techniques, machine learning, and informatics can be utilized effectively at each stage of drug development to advance the progress towards predictive, preventive, personalized, precision medicine, and thus provide a successful framework for Path to Cures.




Machine Learning in Cardiovascular Medicine


Book Description

Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. It is suitable for both clinicians and engineers; providing a template for clinicians to understand areas of application of machine learning within cardiovascular research; and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine. Provides an overview of machine learning, both for a clinical and engineering audience Summarize recent advances in both cardiovascular medicine and artificial intelligence Discusses the advantages of using machine learning for outcomes research and image processing Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach







Artificial Intelligence in Medicine


Book Description

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.




Artificial Intelligence in Medicine


Book Description

This book provides a structured and analytical guide to the use of artificial intelligence in medicine. Covering all areas within medicine, the chapters give a systemic review of the history, scientific foundations, present advances, potential trends, and future challenges of artificial intelligence within a healthcare setting. Artificial Intelligence in Medicine aims to give readers the required knowledge to apply artificial intelligence to clinical practice. The book is relevant to medical students, specialist doctors, and researchers whose work will be affected by artificial intelligence.