Enhancing endoscopic navigation and polyp detection using artificial intelligence

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

Deep learning to find colorectal polyps in colonoscopy: A systematic literature review

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.This work was partially supported by PICCOLO project. This project has received funding from the European Union's Horizon2020 Research and Innovation Programme under grant agreement No. 732111. The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein. The authors would also like to thank Dr. Federico Soria for his support on this manuscript and Dr. José Carlos Marín, from Hospital 12 de Octubre, and Dr. Ángel Calderón and Dr. Francisco Polo, from Hospital de Basurto, for the images in Fig. 4

Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches

The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is important in the formulation of appropriate recommendations that will reduce colorectal cancer. With all standard colon cancer tests can be recognized agitatedly, the treatment of colorectal cancer is more efficient. The intelligent computer assisted diagnosis (CAD) is the most powerful technique for recognition of colorectal cancer in recent advances. It is a lot to reduce the level of interference nature has contributed considerably to the advancement of the quality of cancer treatment. To enhance diagnostic accuracy intelligent CAD has a research always active, ongoing with the deep learning and machine learning approaches with the associated convolutional neural network (CNN) scheme

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

Object Detection in medical imaging

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsArtificial Intelligence, assisted by deep learning, has emerged in various fields of our society. These systems allow the automation and the improvement of several tasks, even surpassing, in some cases, human capability. Object detection methods are used nowadays in several areas, including medical imaging analysis. However, these methods are susceptible to errors, and there is a lack of a universally accepted method that can be applied across all types of applications with the needed precision in the medical field. Additionally, the application of object detectors in medical imaging analysis has yet to be thoroughly analyzed to achieve a richer understanding of the state of the art. To tackle these shortcomings, we present three studies with distinct goals. First, a quantitative and qualitative analysis of academic research was conducted to gather a perception of which object detectors are employed, the modality of medical imaging used, and the particular body parts under investigation. Secondly, we propose an optimized version of a widely used algorithm to overcome limitations commonly addressed in medical imaging by fine-tuning several hyperparameters. Thirdly, we develop a novel stacking approach to augment the precision of detections on medical imaging analysis. The findings show that despite the late arrival of object detection in medical imaging analysis, the number of publications has increased in recent years, demonstrating the significant potential for growth. Additionally, we establish that it is possible to address some constraints on the data through an exhaustive optimization of the algorithm. Finally, our last study highlights that there is still room for improvement in these advanced techniques, using, as an example, stacking approaches. The contributions of this dissertation are several, as it puts forward a deeper overview of the state-of-the-art applications of object detection algorithms in the medical field and presents strategies for addressing typical constraints in this area.A Inteligência Artificial, auxiliada pelo deep learning, tem emergido em diversas áreas da nossa sociedade. Estes sistemas permitem a automatização e a melhoria de diversas tarefas, superando mesmo, em alguns casos, a capacidade humana. Os métodos de detecção de objetos são utilizados atualmente em diversas áreas, inclusive na análise de imagens médicas. No entanto, esses métodos são suscetíveis a erros e falta um método universalmente aceite que possa ser aplicado em todos os tipos de aplicações com a precisão necessária na área médica. Além disso, a aplicação de detectores de objetos na análise de imagens médicas ainda precisa ser analisada minuciosamente para alcançar uma compreensão mais rica do estado da arte. Para enfrentar essas limitações, apresentamos três estudos com objetivos distintos. Inicialmente, uma análise quantitativa e qualitativa da pesquisa acadêmica foi realizada para obter uma percepção de quais detectores de objetos são empregues, a modalidade de imagem médica usada e as partes específicas do corpo sob investigação. Num segundo estudo, propomos uma versão otimizada de um algoritmo amplamente utilizado para superar limitações comumente abordadas em imagens médicas por meio do ajuste fino de vários hiperparâmetros. Em terceiro lugar, desenvolvemos uma nova abordagem de stacking para aumentar a precisão das detecções na análise de imagens médicas. Os resultados demostram que, apesar da chegada tardia da detecção de objetos na análise de imagens médicas, o número de publicações aumentou nos últimos anos, evidenciando o significativo potencial de crescimento. Adicionalmente, estabelecemos que é possível resolver algumas restrições nos dados por meio de uma otimização exaustiva do algoritmo. Finalmente, o nosso último estudo destaca que ainda há espaço para melhorias nessas técnicas avançadas, usando, como exemplo, abordagens de stacking. As contribuições desta dissertação são várias, apresentando uma visão geral em maior detalhe das aplicações de ponta dos algoritmos de detecção de objetos na área médica e apresenta estratégias para lidar com restrições típicas nesta área