Искусственный интеллект при колоректальном раке: обзор

The study objective: the study objective is to examine the use of artificial intelligence (AI) in the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) and discuss the future potential of AI in CRC. Material and Methods. The Web of Science, Scopus, PubMed, Medline, and eLIBRARY databases were used to search for the publications. A study on the application of Artificial Intelligence (AI) to the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) was discovered in more than 100 sources. In the review, data from 83 articles were incorporated. Results. The review article explores the use of artificial intelligence (AI) in medicine, specifically focusing on its applications in colorectal cancer (CRC). It discusses the stages of AI development for CRC, including molecular understanding, image-based diagnosis, drug design, and individualized treatment. The benefits of AI in medical image analysis are highlighted, improving diagnosis accuracy and inspection quality. Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. Ethical and regulatory considerations in integrating AI are mentioned, including patient trust, data security, and liability in AI-assisted surgeries. The review emphasizes the importance of an AI standard system, dataset standardization, and integrating clinical knowledge into AI algorithms. Overall, the article provides an overview of the current research on AI in CRC diagnosis, treatment, and prognosis, discussing its benefits, challenges, and future prospects in improving medical outcomes.Цель исследования - оценка возможностей использования искусственного интеллекта (ИИ) в диагностике, лечении и прогнозировании колоректального рака (КРР), а также обсуждение потенциала ИИ в лечении КРР. Материал и методы. Проведен поиск научных публикаций в поисковых системах Web of Science, Scopus, PubMed, Medline и eLIBRARY. Было просмотрено более 100 источников по применению ИИ для диагностики, лечения и прогнозирования КРР. В обзор включены данные из 83 статей. Результаты. Проведен анализ литературы, посвященной применению искусственного интеллекта в медицине, особое внимание уделено его использованию при колоректальном раке. Обсуждаются этапы развития ИИ при КРР, включая молекулярную верификацию, лучевую диагностику, разработку лекарств и индивидуальное лечение. Подчеркнуты преимущества ИИ в анализе медицинских изображений, таких как КТ, МРТ и ПЭТ, что повышает точность диагностики. Рассматриваются такие проблемы развития ИИ, как стандартизация данных и интерпретируемость алгоритмов машинного обучения. Подчеркивается роль ИИ в выборе оптимальной тактики лечения и повышении эффективности хирургического вмешательства. Учитываются этические и нормативные аспекты ИИ, включая доверие пациентов, безопасность данных и ответственность в проведении операций с использованием ИИ. Обсуждаются преимущества ИИ в диагностике, лечении и прогнозировании колоректального рака, проблемы и перспективы улучшения результатов лечения

Role of Artificial Intelligence in High Throughput Diagnostics for Colorectal Cancer Current Updates

The existence of cancer has been stated as a century’s oldest challenge for the entire human race around theglobe recording a large amount of mortality per year and as per the WHO data nearly 10 million deaths were reported in 2021 worldwide besides others. Colorectal cancer is considered a major threat as this is cancer-related to the colon and rectum with an incidence of 41/1,00,000 recorded annually to overcome this challenge our medical system requires more advanced, accurate and efficient high throughput techniques for the prognosis and effective treatment of this disease. Artificial intelligence’s role in healthcare has been a matter of discussion among experts over the past few years, but more recently the spotlight has focused more specifically on the role that this technology can play in improving patient outcomes and improving the effectiveness of diagnosis and treatment processes. Artificial intelligence refers to a broad category of technologies, including machine learning, natural language processing and deep learning. Exploration of Molecular pathways with characteristics that helps in subtyping of Colorectal Cancer (CRC) leading to specific treatment response or prognosis, for the effective treatment, classification and early detection done using Artificial Intelligence based technologies have shown promising results so far, that it may be utilized to create prediction models in the current environment to distinguish between polyps, metastases, or normal cells in addition to early detection and effective cancer therapy. Nowadays many scientists are putting effort into designing such fabricating models by combining natural language processes and deep learning that can differentiate between non-adenomatous and adenomatous polyps to identify hyper-mutated tumours, genetic mutations and molecular pathways known as IDaRS strategy or iterative draw-and-rank sampling. The review study primarily focuses on the significance of emerging AI-based approaches for the diagnosis, detection, and prognosis of colorectal cancer in light of existing obstacles

Opportunities in cancer imaging: a review of oesophageal, gastric and colorectal malignancies

The incidence of gastrointestinal (GI) malignancy is increasing worldwide. In particular, there is a concerning rise in incidence of GI cancer in younger adults. Direct endoscopic visualisation of luminal tumour sites requires invasive procedures, which are associated with certain risks, but remain necessary because of limitations in current imaging techniques and the continuing need to obtain tissue for diagnosis and genetic analysis; however, management of GI cancer is increasingly reliant on non-invasive, radiological imaging to diagnose, stage, and treat these malignancies. Oesophageal, gastric, and colorectal malignancies require specialist investigation and treatment due to the complex nature of the anatomy, biology, and subsequent treatment strategies. As cancer imaging techniques develop, many opportunities to improve tumour detection, diagnostic accuracy and treatment monitoring present themselves. This review article aims to report current imaging practice, advances in various radiological modalities in relation to GI luminal tumour sites and describes opportunities for GI radiologists to improve patient outcomes

New Techniques in Gastrointestinal Endoscopy

As result of progress, endoscopy has became more complex, using more sophisticated devices and has claimed a special form. In this moment, the gastroenterologist performing endoscopy has to be an expert in macroscopic view of the lesions in the gut, with good skills for using standard endoscopes, with good experience in ultrasound (for performing endoscopic ultrasound), with pathology experience for confocal examination. It is compulsory to get experience and to have patience and attention for the follow-up of thousands of images transmitted during capsule endoscopy or to have knowledge in physics necessary for autofluorescence imaging endoscopy. Therefore, the idea of an endoscopist has changed. Examinations mentioned need a special formation, a superior level of instruction, accessible to those who have already gained enough experience in basic diagnostic endoscopy. This is the reason for what these new issues of endoscopy are presented in this book of New techniques in Gastrointestinal Endoscopy

Deep Learning Based Medical Image Analysis with Limited Data

Deep Learning Methods have shown its great effort in the area of Computer Vision. However, when solving the problems of medical imaging, deep learning’s power is confined by limited data available. We present a series of novel methodologies for solving medical imaging analysis problems with limited Computed tomography (CT) scans available. Our method, based on deep learning, with different strategies, including using Generative Adversar- ial Networks, two-stage training, infusing the expert knowledge, voting based or converting to other space, solves the data set limitation issue for the cur- rent medical imaging problems, specifically cancer detection and diagnosis, and shows very good performance and outperforms the state-of-art results in the literature. With the self-learned features, deep learning based techniques start to be applied to the biomedical imaging problems and various structures have been designed. In spite of its simplity and anticipated good performance, the deep learning based techniques can not perform to its best extent due to the limited size of data sets for the medical imaging problems. On the other side, the traditional hand-engineered features based methods have been studied in the past decades and a lot of useful features have been found by these research for the task of detecting and diagnosing the pulmonary nod- ules on CT scans, but these methods are usually performed through a series of complicated procedures with manually empirical parameter adjustments. Our method significantly reduces the complications of the traditional proce- dures for pulmonary nodules detection, while retaining and even outperforming the state-of-art accuracy. Besides, we make contribution on how to convert low-dose CT image to full-dose CT so as to adapting current models on the newly-emerged low-dose CT data

Massive training artificial immune recognition system for lung nodules detection

In the early detection and diagnosis of lung nodule, computer aided detection (CAD) has become crucial to assist radiologists in interpreting medical images and decision making. However, some limitations have been found in the existing CAD algorithms for detecting lung nodules, such as imprecision classification due to inaccurate segmentation and lengthy computation time. In this research, Massive Training Artificial Immune Recognition System (MTAIRS) is proposed to detect lung nodules on Computed Tomography (CT) scans. MTAIRS is developed based on the pixel machine learning and artificial immune-based system-Artificial Immune Recognition System (AIRS). Two versions of proposed algorithms have been investigated in the study: MTAIRS 1 and MTAIRS 2. Since segmentation and feature calculation are not implemented in the pixel-based machine learning, the loss of information can be avoided during the data training in MTAIRS 1 and MTAIRS 2. The experiment and analysis find that MTAIRS 1 and MTAIRS 2 have successfully reduced the computation time and accomplished good accuracy in the detection of lung nodules on CT scans compared to other well-known pixel-based classification algorithms. Furthermore, MTAIRS 1 and MTAIRS 2 are investigated to improve their performance in eliminating the false positives. A weighted non-linear affinity function is employed in the training of MTAIRS 1 and MTAIRS 2 to replace Euclidean distance in affinity measurement. The enhanced algorithms named, E-MTAIRS 1 and E-MTAIRS 2 are capable to reduce the false positives in the non-nodule classification while maintaining the accuracy in nodule detection. In order to further provide comparative analysis of pixel-based classification algorithms in lung nodules detection, a pixel-based evaluation method of Kullback Leibler (KL) divergence is proposed in this study. Based on the pixel-based quantitative analysis, MTAIRS 1 performs better in the elimination of false positives, while MTAIRS 2 in lung nodules detection. The average detection accuracy for both MTAIRS algorithms is 95%

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC