Advancement and development in computational chemistry for Colorectal Cancer diagnosis and treatment

2021 ◽  
Vol 02 ◽  
Author(s):  
Samridhi Rawat ◽  
Deepika Paliwal
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Multidisciplinary Approach ◽  
Promising Result ◽  
Excellent Result ◽  
Screening Tests ◽  
Clear Understanding ◽  
Recent Developments ◽  
Aided Diagnosis

: Colorectal cancer is the third most common form of cancer in the world and is a significant cause of death from cancer. To get a cure for colorectal cancer, early diagnosis and screening are necessary, as well as a clear understanding of the molecular basis of the disease. Molecular characterization of mutations associated with cancer provides useful information on the prognosis and reaction to therapy. This review discusses the clinical, molecular, and pathogenic features of the early onset of colorectal cancer, its screening tests, and diagnosis. The recent advances and rapid growth of computational tools in colorectal cancer used for diagnosis as well as screening are outlined in this review, including artificial intelligence, computer aided diagnosis, molecular docking, machine learning, and deep learning. In recent developments, the application of artificial intelligence has proved its excellency and shown a promising result, pointing out the severe need for a multidisciplinary approach to achieve an excellent result.

scholarly journals Application of artificial intelligence in pancreaticobiliary diseases

2021 ◽  
Vol 14 ◽  
pp. 263177452199305
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Zhongheng Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Response To Treatment ◽  
Endoscopic Retrograde ◽  
Artificial Intelligence Techniques ◽  
Computer Aided ◽  
Rapid Pace ◽  
Cystic Pancreatic Lesions ◽  
Aided Diagnosis

The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.

scholarly journals Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

2020 ◽  
Vol 9 (10) ◽  
pp. 3313 ◽  
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Aman Ali ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Narrow Band Imaging ◽  
Screening Methods ◽  
Polyp Detection ◽  
Mortality And Morbidity ◽  
Magnifying Chromoendoscopy

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

scholarly journals Artificial intelligence for identification and characterization of colonic polyps

2021 ◽  
Vol 14 ◽  
pp. 263177452110146
Author(s):  
Nasim Parsa ◽  
Michael F. Byrne
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Computer Aided Design ◽  
Colorectal Polyps ◽  
Optical Biopsy ◽  
Machine Learning Techniques ◽  
Withdrawal Time ◽  
Adenoma Detection ◽  
Computer Aided

Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.

Artificial Intelligence in Computer-Aided Diagnosis

2011 ◽  
pp. 157-160
Author(s):  
Paulo Eduardo Ambrósio
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Daily Activities ◽  
Radiographic Images ◽  
Great Capacity ◽  
Computer Aided ◽  
Medical Radiology ◽  
Aided Diagnosis

Professionals of the medical radiology area depend directly on the process of decision making in their daily activities. This process is mainly based on the analysis of a great amount of information obtained for the evaluation of radiographic images. Some studies demonstrate the great capacity of Artificial Neural Networks (ANN) in support systems for diagnosis, mainly in applications as pattern classification. The objective of this article is to present the development of an ANN-based system, verifying its behavior as a feature extraction and dimensionality reduction tool, for recognition and characterization of patterns, for posterior classification in normal and abnormal patterns.

scholarly journals A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound

Medicine ◽  
2019 ◽  
Vol 98 (3) ◽  
pp. e14146 ◽  
Author(s):  
Hee Jeong Park ◽  
Sun Mi Kim ◽  
Bo La Yun ◽  
Mijung Jang ◽  
Bohyoung Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Neurochemistry of L-Glutamate Transport in the CNS: A Review of Thirty Years of Progress

2001 ◽  
Vol 66 (9) ◽  
pp. 1315-1340 ◽  
Author(s):  
Vladimir J. Balcar ◽  
Akiko Takamoto ◽  
Yukio Yoneda
Keyword(s):  
Molecular Biology ◽  
Glutamate Transport ◽  
Mammalian Brain ◽  
Activity Data ◽  
System A ◽  
Recent Developments ◽  
The Central Nervous System ◽  
Health And Disease ◽  
Disease Analysis

The review highlights the landmark studies leading from the discovery and initial characterization of the Na+-dependent "high affinity" uptake in the mammalian brain to the cloning of individual transporters and the subsequent expansion of the field into the realm of molecular biology. When the data and hypotheses from 1970's are confronted with the recent developments in the field, we can conclude that the suggestions made nearly thirty years ago were essentially correct: the uptake, mediated by an active transport into neurons and glial cells, serves to control the extracellular concentrations of L-glutamate and prevents the neurotoxicity. The modern techniques of molecular biology may have provided additional data on the nature and location of the transporters but the classical neurochemical approach, using structural analogues of glutamate designed as specific inhibitors or substrates for glutamate transport, has been crucial for the investigations of particular roles that glutamate transport might play in health and disease. Analysis of recent structure/activity data presented in this review has yielded a novel insight into the pharmacological characteristics of L-glutamate transport, suggesting existence of additional heterogeneity in the system, beyond that so far discovered by molecular genetics. More compounds that specifically interact with individual glutamate transporters are urgently needed for more detailed investigations of neurochemical characteristics of glutamatergic transport and its integration into the glutamatergic synapses in the central nervous system. A review with 162 references.

Sign in / Sign up

Export Citation Format

Share Document