A comparison of artificial intelligence algorithms in diagnosing and predicting gastric cancer: a review study

Today, artificial intelligence is considered a powerful tool that can help physicians identify and diagnose and predict diseases. Gastric cancer has been the fourth most common malignancy and the second leading cause of cancer mortality in the world. Thus, timely diagnosis of this type of cancer could effectively control it. This paper compares AI (artificial intelligence) algorithms in diagnosing and predicting gastric cancer based on types of AI algorithms, sample size, accuracy, sensitivity, and specificity.  This narrative-review paper aims to explore AI algorithms in diagnosing and predicting gastric cancer. To achieve this goal, we reviewed English articles published between 2011 and 2021 in PubMed and Science direct databases. According to the reviews conducted on the published papers, the endoscopic method has been the most used method to collect and incorporate samples into designed models. Also, the SVM (support vector machine), convolutional neural network (CNN), and deep-type CNN have been used the most; therefore, we propose the usage of these algorithms in medical subjects, especially in gastric cancer

Использование искусственного интеллекта в цистоскопической диагностике рака мочевого пузыря

Background. At the current stage of science and technology development, artificial intelligence (AI) is being actively developed and gradually introduced into the healthcare system.Aim. To perform a literature review to assess the diagnostic value of AI in the detection of bladder cancer at the cystoscopy stage.Materials and methods. We carried out a bibliographic search of articles in Medline and Embase databases using the keywords “artificial intelligence”, “cystoscopy”, “TURBT”.Results. Automated image processing based on AI can improve the accuracy of cancer diagnosis during cystoscopy. According to the studies presented in the review, the sensitivity of AI system for the detection of bladder cancer via cystoscopy can reach 89.7–95.4 %, while its specificity is 87.8–98.6 %, which exceeds the diagnostic capabilities of standard cystoscopy in white light, the sensitivity and specificity of which, according to recent investigations, are approximately 60 and 70 %, respectively. Despite the promising results of these studies, modern science is currently at the stage of developing and evaluating the performance of various AI methods used to analyze cystoscopy images. To date, it would be premature to introduce and widely use these technologies in healthcare, since there are no prospective clinical studies to assess the effectiveness of AI systems in diagnostic cystoscopy and transurethral resection of bladder cancer.Conclusion. Few studies show that AI-based cystoscopy is a promising approach to improvement of the quality of medical care for bladder cancer. Further research is needed to improve the diagnostic capabilities of AI and introduce the obtained technological data into clinical practice.Введение. На современном этапе развития науки и техники происходят активная разработка и постепенное внедрение в систему здравоохранения технологий искусственного интеллекта (ИИ).Цель работы – обзор литературы для оценки диагностического значения ИИ в выявлении рака мочевого пузыря на этапе цистоскопии.Материалы и методы. Проведен библиографический поиск статей в базах данных Medline и Embase с использованием ключевых слов “artificial intelligence”, “cystoscopy”, “TURBT”.Результаты. Автоматизированная обработка изображений на основе ИИ может повысить точность диагностики рака при цистоскопии. По данным представленных исследований чувствительность цистоскопии при использовании ИИ достигает 89,7–95,4 %, специфичность – 87,8–98,6 %, что превосходит диагностические возможности стандартной цистоскопии в белом свете, чувствительность и специфичность которой составляют примерно 60 и 70 % соответственно. Несмотря на многообещающие результаты данных исследований, современная наука находится лишь на стадии разработки и оценки производительности различных методов ИИ, используемых для анализа цистоскопических изображений. На сегодняшний день рано говорить о внедрении и широком применении данных технологий в здравоохранении, так как отсутствуют проспективные клинические исследования оценки эффективности цистоскопической диагностики и трансуретральной резекции рака мочевого пузыря в сопровождении ИИ.Заключение. Цистоскопия на основе ИИ – перспективное направление (согласно немногочисленным данным литературы) в вопросе повышения качества медицинской помощи при раке мочевого пузыря. Для усовершенствования диагностических возможностей ИИ и внедрения в клиническую практику полученных технологических данных необходимо проведение дальнейших исследований

Artificial intelligence in gastroenterology: a state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.Cellular mechanisms in basic and clinical gastroenterology and hepatolog