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.

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 Outcomes of colonoscopy with non-anesthesiologist-administered propofol (NAAP): an equivalence trial

2021 ◽  
Vol 09 (07) ◽  
pp. E1070-E1076
Author(s):  
Marco Alburquerque ◽  
Antonella Smarrelli ◽  
Julio Chevarria Montesinos ◽  
Sergi Ortega Carreño ◽  
Ana Zaragoza Fernandez ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Screening Program ◽  
Complication Rates ◽  
Withdrawal Time ◽  
Equivalence Trial ◽  
Crc Screening ◽  
Intention To Treat ◽  
Adenoma Detection ◽  
Group A ◽  
Cecal Intubation Rate

Abstract Background and study aims Efficacy and safety of NAAP for gastrointestinal endoscopy have been widely documented, although there is no information about the outcomes of colonoscopy when the endoscopist supervises the sedation. In this context, the aim of this trial was to determine the equivalence of adenoma detection rate (ADR) in colorectal cancer (CRC) screening colonoscopies performed with non-anesthesiologist-administered propofol (NAAP) and performed with monitored anesthesia care (MAC). Patients and methods This was a single-blind, non-randomized controlled equivalence trial that enrolled adults from a national CRC screening program (CRCSP). Patients were blindly assigned to undergo either colonoscopy with NAAP or MAC. The main outcome measure was the ADR in CRCSP colonoscopies performed with NAAP. Results We included 315 patients per group. The median age was 59.76 ± 5.81 years; 40.5 % of patients were women. The cecal intubation rate was 97 %, 81.8 % of patients had adequate bowel preparation, withdrawal time was > 6 minutes in 98.7 %, and the median global exploration time was 24.25 ± 8.86 minutes (range, 8–70 minutes). The ADR was 62.9 % and the complication rate (CR) was 0.6 %. Analysis by intention-to-treat showed an ADR in the NAAP group of 64.13 % compared with 61.59 % in the MAC group, a difference (δADR) of 2.54 %, 95 %CI: −0.10 to 0.05. Analysis by per-protocol showed an ADR in the NAAP group of 62.98 %, compared with 61.94 % in the MAC group, δADR: 1.04 %, 95 %CI: −0.09 to 0.07. There was no difference in CR (NAAP: 0,63 vs. MAC: 0.63); P = 1.0. Conclusions ADR in colorectal cancer screening colonoscopies performed with NAAP was equivalent to that in those performed with MAC. Similarly, there was no difference in complication rates.

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.

Use of the designer's experience in systems of computer-aided design and artificial intelligence

2021 ◽  
pp. 89-95
Author(s):  
А.И. Гайкович ◽  
С.И. Лукин ◽  
О.Я. Тимофеев
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Sets ◽  
Computer Aided Design ◽  
Design Tool ◽  
Design System ◽  
Design Problems ◽  
General Arrangement ◽  
Computer Aided ◽  
A Chain ◽  
Aided Design

Процесс создания проекта судна или корабля рассматривается как преобразование информации, содержащейся в техническом задании на проектирование, нормативных документах и знаниях проектанта, в информацию, объем которой позволяет реализовать проект. Проектирование может быть представлено как поиск решения в пространстве задач. Построение цепочки последовательно решаемых задач составляет методику проектирования. Проектные задачи могут быть разбиты на две группы. Первая группа ‒ это полностью формализуемые задачи, для решения которых есть известные алгоритмы. Например, построение теоретического чертежа по известным главным размерениям и коэффициентам формы. Ко второй группе задач можно отнести трудно формализуемые или неформализуемые задачи. Например, к задачам этого типа можно отнести разработку общего расположения корабля. Важнейшим инструментом проектирования современного корабля или судна является система ав­томатизированного проектирования (САПР). Решение САПР задач первой группы не представляет проблемы. Введение в состав САПР задач второй группы подразумевает разработку специального ма­тематического аппарата, базой для которого, которым является искусственный интеллект, использующий теорию нечетких множеств. Однако, настройка искусственных нейронных сетей, создание шкал для функций принадлежности элементов нечетких множеств и функций предпочтений лица принимающего решения, требует участие человека. Таким образом, указанные элементы искусственного интеллекта фиксируют качества проек­танта как специалиста и создают его виртуальный портрет. The process of design a project of a ship is considered as the transformation of information contained in the design specification, regulatory documents and the designer's knowledge into information, the volume of which allows the project to be implemented. Designing can be represented as a search for a solution in the space of problems. The construction of a chain of sequentially solved tasks constitutes the design methodology. Design problems can be divided into two groups. The first group is completely formalizable tasks, for the solution of which there are known algorithms. For example, the construction of ship's surface by known main dimensions and shape coefficients. Tasks of the second group may in­clude those which are difficult to formalize or non-formalizable. For example, tasks of this type can include develop­ment of general arrangement of a ship. The most important design tool of a modern ship or vessel is a computer-aided design system (CAD). The solu­tion of CAD problems of the first group is not a problem. Introduction of tasks of the second group into CAD implies development of a special mathematical apparatus, the basis for which is artificial intelligence, which uses the theory of fuzzy sets. However, the adjustment of artificial neural networks, the creation of scales for membership functions of fuzzy sets elements and functions of preferences of decision maker, requires human participation. Thus, the above elements of artificial intelligence fix the qualities of the designer as a specialist and create his virtual portrait.

Convolutional Neural Networks in Computer-Aided Diagnosis of Colorectal Polyps and Cancer: A Review

2021 ◽  
Author(s):  
Kamyab Keshtkar
Keyword(s):  
Colorectal Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Support Vector ◽  
Training Process ◽  
Computer Aided ◽  
Conventional Models ◽  
Aided Diagnosis

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

Computer-Aided Characterisation Of Colorectal Polyps Using Artificial Intelligence

2021 ◽  
Author(s):  
R Kader ◽  
P Brandao ◽  
O Ahmad ◽  
M Hussein ◽  
S Islam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Polyps ◽  
Computer Aided

scholarly journals Artificial Intelligence in General

2019 ◽  
pp. 1-16
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Design ◽  
Intelligent Systems ◽  
Production Systems ◽  
Knowledge Bases ◽  
Computer Integrated Manufacturing ◽  
Current Status ◽  
Computer Aided ◽  
Control Computer ◽  
History Of

There are many kinds of uses for artificial intelligence (AI) in almost every field. AI is quite often used for control, computer aided design (CAD) and computer aided manufacturing (CAM), machine control, computer integrated manufacturing (CIM), production spot control, factory control, intelligent control, intelligent systems, deep learning, the cloud, knowledge bases, database, management, production systems, statistics, to assist sales forces, environment examination, agriculture, art, livings, daily life, etc. The present AI uses will be reexamined whether there is any matter to be considered further or not in AI research directions and their purposes behind the current status by looking at the history of AI development.

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