427 research outputs found

    Π˜ΡΠΊΡƒΡΡΡ‚Π²Π΅Π½Π½Ρ‹ΠΉ ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ ΠΏΡ€ΠΈ ΠΊΠΎΠ»ΠΎΡ€Π΅ΠΊΡ‚Π°Π»ΡŒΠ½ΠΎΠΌ Ρ€Π°ΠΊΠ΅: ΠΎΠ±Π·ΠΎΡ€

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    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.ЦСль исслСдования - ΠΎΡ†Π΅Π½ΠΊΠ° возмоТностСй использования искусствСнного ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚Π° (ИИ) Π² диагностикС, Π»Π΅Ρ‡Π΅Π½ΠΈΠΈ ΠΈ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ»ΠΎΡ€Π΅ΠΊΡ‚Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ Ρ€Π°ΠΊΠ° (КРР), Π° Ρ‚Π°ΠΊΠΆΠ΅ обсуТдСниС ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»Π° ИИ Π² Π»Π΅Ρ‡Π΅Π½ΠΈΠΈ КРР. ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π» ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½ поиск Π½Π°ΡƒΡ‡Π½Ρ‹Ρ… ΠΏΡƒΠ±Π»ΠΈΠΊΠ°Ρ†ΠΈΠΉ Π² поисковых систСмах Web of Science, Scopus, PubMed, Medline ΠΈ eLIBRARY. Π‘Ρ‹Π»ΠΎ просмотрСно Π±ΠΎΠ»Π΅Π΅ 100 источников ΠΏΠΎ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡŽ ИИ для диагностики, лСчСния ΠΈ прогнозирования КРР. Π’ ΠΎΠ±Π·ΠΎΡ€ Π²ΠΊΠ»ΡŽΡ‡Π΅Π½Ρ‹ Π΄Π°Π½Π½Ρ‹Π΅ ΠΈΠ· 83 статСй. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· Π»ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΡƒΡ€Ρ‹, посвящСнной ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡŽ искусствСнного ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚Π° Π² ΠΌΠ΅Π΄ΠΈΡ†ΠΈΠ½Π΅, особоС Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡƒΠ΄Π΅Π»Π΅Π½ΠΎ Π΅Π³ΠΎ использованию ΠΏΡ€ΠΈ ΠΊΠΎΠ»ΠΎΡ€Π΅ΠΊΡ‚Π°Π»ΡŒΠ½ΠΎΠΌ Ρ€Π°ΠΊΠ΅. ΠžΠ±ΡΡƒΠΆΠ΄Π°ΡŽΡ‚ΡΡ этапы развития ИИ ΠΏΡ€ΠΈ КРР, Π²ΠΊΠ»ΡŽΡ‡Π°Ρ ΠΌΠΎΠ»Π΅ΠΊΡƒΠ»ΡΡ€Π½ΡƒΡŽ Π²Π΅Ρ€ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΡŽ, Π»ΡƒΡ‡Π΅Π²ΡƒΡŽ диагностику, Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΡƒ лСкарств ΠΈ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡƒΠ°Π»ΡŒΠ½ΠΎΠ΅ Π»Π΅Ρ‡Π΅Π½ΠΈΠ΅. ΠŸΠΎΠ΄Ρ‡Π΅Ρ€ΠΊΠ½ΡƒΡ‚Ρ‹ прСимущСства ИИ Π² Π°Π½Π°Π»ΠΈΠ·Π΅ мСдицинских ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, Ρ‚Π°ΠΊΠΈΡ… ΠΊΠ°ΠΊ КВ, МРВ ΠΈ ПЭВ, Ρ‡Ρ‚ΠΎ ΠΏΠΎΠ²Ρ‹ΡˆΠ°Π΅Ρ‚ Ρ‚ΠΎΡ‡Π½ΠΎΡΡ‚ΡŒ диагностики. Π Π°ΡΡΠΌΠ°Ρ‚Ρ€ΠΈΠ²Π°ΡŽΡ‚ΡΡ Ρ‚Π°ΠΊΠΈΠ΅ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ развития ИИ, ΠΊΠ°ΠΊ стандартизация Π΄Π°Π½Π½Ρ‹Ρ… ΠΈ ΠΈΠ½Ρ‚Π΅Ρ€ΠΏΡ€Π΅Ρ‚ΠΈΡ€ΡƒΠ΅ΠΌΠΎΡΡ‚ΡŒ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠΎΠ² машинного обучСния. ΠŸΠΎΠ΄Ρ‡Π΅Ρ€ΠΊΠΈΠ²Π°Π΅Ρ‚ΡΡ Ρ€ΠΎΠ»ΡŒ ИИ Π² Π²Ρ‹Π±ΠΎΡ€Π΅ ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½ΠΎΠΉ Ρ‚Π°ΠΊΡ‚ΠΈΠΊΠΈ лСчСния ΠΈ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΠΈ эффСктивности хирургичСского Π²ΠΌΠ΅ΡˆΠ°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Π°. Π£Ρ‡ΠΈΡ‚Ρ‹Π²Π°ΡŽΡ‚ΡΡ этичСскиС ΠΈ Π½ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½Ρ‹Π΅ аспСкты ИИ, Π²ΠΊΠ»ΡŽΡ‡Π°Ρ Π΄ΠΎΠ²Π΅Ρ€ΠΈΠ΅ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ², Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡ‚ΡŒ Π΄Π°Π½Π½Ρ‹Ρ… ΠΈ ΠΎΡ‚Π²Π΅Ρ‚ΡΡ‚Π²Π΅Π½Π½ΠΎΡΡ‚ΡŒ Π² ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΉ с использованиСм ИИ. ΠžΠ±ΡΡƒΠΆΠ΄Π°ΡŽΡ‚ΡΡ прСимущСства ИИ Π² диагностикС, Π»Π΅Ρ‡Π΅Π½ΠΈΠΈ ΠΈ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ»ΠΎΡ€Π΅ΠΊΡ‚Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ Ρ€Π°ΠΊΠ°, ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ ΠΈ пСрспСктивы ΡƒΠ»ΡƒΡ‡ΡˆΠ΅Π½ΠΈΡ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² лСчСния

    Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

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    Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, and associated interval colorectal cancers after colonoscopy are of concern. Optical biopsy, whereby in-vivo classification of polyps based on enhanced imaging replaces histopathology, has not been incorporated into routine practice because it is limited by interobserver variability and generally only meets accepted standards in expert settings. Real-time decision-support software has been developed to detect and characterise polyps, and also to offer feedback on the technical quality of inspection. Some of the current algorithms, particularly with recent advances in artificial intelligence techniques, match human expert performance for optical biopsy. In this Review, we summarise the evidence for clinical applications of computer-aided diagnosis and artificial intelligence in colonoscopy

    Decomposition of color wavelet with higher order statistical texture and convolutional neural network features set based classification of colorectal polyps from video endoscopy

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    Gastrointestinal cancer is one of the leading causes of death across the world. The gastrointestinal polyps are considered as the precursors of developing this malignant cancer. In order to condense the probability of cancer, early detection and removal of colorectal polyps can be cogitated. The most used diagnostic modality for colorectal polyps is video endoscopy. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of the polyp and thus improve the accuracy of diagnosis results. The proposed method first detects polyp and non-polyp then illustrates an automatic polyp classification technique from endoscopic video through color wavelet with higher-order statistical texture feature and Convolutional Neural Network (CNN). Gray Level Run Length Matrix (GLRLM) is used for higher-order statistical texture features of different directions (Ɵ = 0o, 45o, 90o, 135o). The features are fed into a linear support vector machine (SVM) to train the classifier. The experimental result demonstrates that the proposed approach is auspicious and operative with residual network architecture, which triumphs the best performance of accuracy, sensitivity, and specificity of 98.83%, 97.87%, and 99.13% respectively for classification of colorectal polyps on standard public endoscopic video databases

    Enhancing endoscopic navigation and polyp detection using artificial intelligence

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    Colorectal cancer (CRC) is one most common and deadly forms of cancer. It has a very high mortality rate if the disease advances to late stages however early diagnosis and treatment can be curative is hence essential to enhancing disease management. Colonoscopy is considered the gold standard for CRC screening and early therapeutic treatment. The effectiveness of colonoscopy is highly dependent on the operator’s skill, as a high level of hand-eye coordination is required to control the endoscope and fully examine the colon wall. Because of this, detection rates can vary between different gastroenterologists and technology have been proposed as solutions to assist disease detection and standardise detection rates. This thesis focuses on developing artificial intelligence algorithms to assist gastroenterologists during colonoscopy with the potential to ensure a baseline standard of quality in CRC screening. To achieve such assistance, the technical contributions develop deep learning methods and architectures for automated endoscopic image analysis to address both the detection of lesions in the endoscopic image and the 3D mapping of the endoluminal environment. The proposed detection models can run in real-time and assist visualization of different polyp types. Meanwhile the 3D reconstruction and mapping models developed are the basis for ensuring that the entire colon has been examined appropriately and to support quantitative measurement of polyp sizes using the image during a procedure. Results and validation studies presented within the thesis demonstrate how the developed algorithms perform on both general scenes and on clinical data. The feasibility of clinical translation is demonstrated for all of the models on endoscopic data from human participants during CRC screening examinations

    Multi-level feature fusion network combining attention mechanisms for polyp segmentation

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    Clinically, automated polyp segmentation techniques have the potential to significantly improve the efficiency and accuracy of medical diagnosis, thereby reducing the risk of colorectal cancer in patients. Unfortunately, existing methods suffer from two significant weaknesses that can impact the accuracy of segmentation. Firstly, features extracted by encoders are not adequately filtered and utilized. Secondly, semantic conflicts and information redundancy caused by feature fusion are not attended to. To overcome these limitations, we propose a novel approach for polyp segmentation, named MLFF-Net, which leverages multi-level feature fusion and attention mechanisms. Specifically, MLFF-Net comprises three modules: Multi-scale Attention Module (MAM), High-level Feature Enhancement Module (HFEM), and Global Attention Module (GAM). Among these, MAM is used to extract multi-scale information and polyp details from the shallow output of the encoder. In HFEM, the deep features of the encoders complement each other by aggregation. Meanwhile, the attention mechanism redistributes the weight of the aggregated features, weakening the conflicting redundant parts and highlighting the information useful to the task. GAM combines features from the encoder and decoder features, as well as computes global dependencies to prevent receptive field locality. Experimental results on five public datasets show that the proposed method not only can segment multiple types of polyps but also has advantages over current state-of-the-art methods in both accuracy and generalization ability
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