Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence‐based image analysis

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

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Press “A” for Artificial Intelligence in Agriculture: A Review

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.4.3.387 ◽

2020 ◽

Vol 4 (3) ◽

Author(s):

Yogesh Awasthi

Keyword(s):

Artificial Intelligence ◽

Image Analysis ◽

Significant Role ◽

Pattern Analysis ◽

Cost Effective ◽

Predictive Analysis ◽

Accurate Information ◽

Digital Era ◽

Learning Techniques ◽

Crucial Information

Agriculture is the backbone of the developing country. In old era agriculture was based on the experience which was shared by people to people but in this digital era technology play a very important and significant role in agriculture. Now agriculture become a business hub therefore farmers are focusing on precision farming. They introduced the technology in agriculture to define the accurate information about seed, soil, weather, disease and all factors which affecting the farming. Artificial Intelligence uses predictive analysis, image analysis, learning techniques and Pattern analysis to declare the best cost effective and maximum gain for the agriculturist. The aim of this paper is to provide the crucial information with the help of technology which a farmers can use to harvest the variety of crops as per the demand in world so that they can get maximum benefits.

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Artificial Intelligence for epigenetics: towards personalized medicine

Current Medicinal Chemistry ◽

10.2174/0929867327666201117142006 ◽

2020 ◽

Vol 27 ◽

Author(s):

Giulia De Riso ◽

Sergio Cocozza

Keyword(s):

Biological Sciences ◽

Artificial Intelligence ◽

Personalized Medicine ◽

Molecular Mechanisms ◽

Genomic Sequence ◽

Health Care Interventions ◽

Genome Wide ◽

Genetic And Environmental Factors ◽

Opening Up ◽

Omic Data

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

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Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis

Patterns ◽

10.1016/j.patter.2021.100323 ◽

2021 ◽

Vol 2 (9) ◽

pp. 100323

Author(s):

Farid Nakhle ◽

Antoine L. Harfouche

Keyword(s):

Artificial Intelligence ◽

Image Analysis

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Artificial Intelligence for Document Image Analysis

Document Processing Using Machine Learning ◽

10.1201/9780429277573-1 ◽

2019 ◽

pp. 1-14

Author(s):

Himadri Mukherjee ◽

Payel Rakshit ◽

Ankita Dhar ◽

Sk Md Obaidullah ◽

KC Santosh ◽

...

Keyword(s):

Artificial Intelligence ◽

Image Analysis ◽

Document Image ◽

Document Image Analysis

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3D Image Analysis and Artificial Intelligence for Bone Disease Classification

Journal of Medical Systems ◽

10.1007/s10916-009-9296-3 ◽

2009 ◽

Vol 34 (5) ◽

pp. 815-828 ◽

Cited By ~ 10

Author(s):

Abdurrahim Akgundogdu ◽

Rachid Jennane ◽

Gabriel Aufort ◽

Claude Laurent Benhamou ◽

Osman Nuri Ucan

Keyword(s):

Artificial Intelligence ◽

Image Analysis ◽

Bone Disease ◽

Disease Classification ◽

3D Image ◽

3D Image Analysis

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Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

Journal of Clinical Pathology ◽

10.1136/jclinpath-2020-207351 ◽

2021 ◽

pp. jclinpath-2020-207351

Author(s):

Jenny Fitzgerald ◽

Debra Higgins ◽

Claudia Mazo Vargas ◽

William Watson ◽

Catherine Mooney ◽

...

Keyword(s):

Prostate Cancer ◽

Artificial Intelligence ◽

Patient Outcome ◽

Image Analysis ◽

High Throughput Screening ◽

Personalised Medicine ◽

Digital Pathology ◽

Disease Stage ◽

Breast And Prostate Cancer ◽

Biomarker Evaluation

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

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