• Artificial intelligence
• Cancer
• Clinical trial
• Deep learning
• Detection
• Machine learning
• Treatment

• Humans
• Neoplasms/diagnosis
• Neoplasms/therapy
• Neoplasms/diagnostic imaging
• Artificial Intelligence
• Machine Learning
• Medical Oncology/methods
• Neural Networks, Computer
• Deep Learning

• Computer-aided diagnosis systems
• Deep learning
• Endoscopic images
• Gastrointestinal diseases
• Image classification

• Deep Learning
• Humans
• Gastrointestinal Diseases/diagnosis
• Gastrointestinal Diseases/classification
• Gastrointestinal Diseases/diagnostic imaging
• Diagnosis, Computer-Assisted/methods
• Algorithms

• 62233016 National Natural Science Foundation of China
• 2024C04023 Science and Technology Program of Zhejiang Province
• LQ23F010024 Natural Science Foundation of Zhejiang Province

• Colorectal cancer
• Integrated CNNs
• K-Means clustering
• Support vector machines
• Transformers

• Humans
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/diagnostic imaging
• Unsupervised Machine Learning
• Colonoscopy/methods
• Supervised Machine Learning
• Support Vector Machine
• Cluster Analysis
• Neural Networks, Computer
• Algorithms
• Colonic Polyps/diagnostic imaging
• Colonic Polyps/diagnosis

• (TU-DSPP-2024-14) Taif University, Taif, Saudi Arabia

• CKHK-22 dataset
• Colorectal Carcinoma (CRC)
• Ensemble models
• Integrated CNNs
• Vision Transformers
• XGBoost

• Humans
• Deep Learning
• Colorectal Neoplasms/diagnosis
• Neural Networks, Computer
• Diagnosis, Computer-Assisted/methods
• Early Detection of Cancer/methods
• Image Processing, Computer-Assisted/methods
• Databases, Factual

• Humans
• Algorithms
• Colonoscopy/methods
• Image Interpretation, Computer-Assisted/methods

• CADe/x
• artificial intelligence
• colonoscopy
• colorectal polyps
• convolutional neural networks
• deep learning
• screening

• Artificial intelligence
• Datasets
• Endoscopy
• Review

• Humans
• Artificial Intelligence
• Colonoscopy/methods
• Capsule Endoscopy/methods
• Intestine, Small
• Diagnostic Imaging

• Science and Technology Plan of Suzhou City
• National Natural Science Foundation of China
• Youth Program of Suzhou Health Committee
• Suzhou Clinical Center of Digestive Diseases

• Humans
• Colonic Diseases/diagnostic imaging
• Colonoscopy

• Humans
• Urinary Bladder/diagnostic imaging
• Endoscopy
• Light
• Urinary Bladder Neoplasms/diagnostic imaging
• Urinary Bladder Neoplasms/pathology
• Narrow Band Imaging/methods

• CKHK-22 mixed dataset
• bidirectional long short-term memory (BiLSTM)
• colorectal cancer
• ensemble fusion CNNs
• feature fusion
• support vector machine (SVM)

• Attention mechanism
• Colon cancer
• Colon lesions classification
• Dual branch network
• Endoscopic images
• Gaussian loss function

• colorectal cancer
• deep learning
• endoscopic

• National Key R &D Program of China

• Biomedical imaging
• Cost function
• Image segmentation
• Machine learning

• Humans
• Image Processing, Computer-Assisted/methods
• Neural Networks, Computer

• BRC-1215-20014 Department of Health
• C9685/A25177 Cancer Research UK
• MC_PC_21013 Medical Research Council
• MC/PC/21013 Medical Research Council
• 215733/Z/19/Z Wellcome Trust
• MR/V023799/1 Medical Research Council
• Mark Foundation For Cancer Research
• National Institute of Health Research
• Cambridge Mathematics of Information in Healthcare
• UK Research and Innovation Future Leaders Fellowship
• European Research Council Innovative Medicines Initiative
• AI for Health Imaging Award

• AI
• CADe
• CADx
• colonoscopy
• lesion characterization
• polyp detection

• colorectal cancer
• convolutional neural network (CNN)
• deep learning
• polyp detection
• polyp localization

• artificial intelligence
• colorectal cancer
• deep learning
• machine learning
• radiomics

• COVID-19
• Deep learning
• Image-segmentation
• Transformer
• U-net

Computer-aided polyp segmentation is a crucial task that supports gastroenterologists in examining and resecting anomalous tissue in the gastrointestinal tract. The disease polyps grow mainly in the colorectal area of the gastrointestinal tract and in the mucous membrane, which has protrusions of micro-abnormal tissue that increase the risk of incurable diseases such as cancer. So, the early examination of polyps can decrease the chance of the polyps growing into cancer, such as adenomas, which can change into cancer. Deep learning-based diagnostic systems play a vital role in diagnosing diseases in the early stages. A deep learning method, Graft-U-Net, is proposed to segment polyps using colonoscopy frames. Graft-U-Net is a modified version of UNet, which comprises three stages, including the preprocessing, encoder, and decoder stages. The preprocessing technique is used to improve the contrast of the colonoscopy frames. Graft-U-Net comprises encoder and decoder blocks where the encoder analyzes features, while the decoder performs the features’ synthesizing processes. The Graft-U-Net model offers better segmentation results than existing deep learning models. The experiments were conducted using two open-access datasets, Kvasir-SEG and CVC-ClinicDB. The datasets were prepared from the large bowel of the gastrointestinal tract by performing a colonoscopy procedure. The anticipated model outperforms in terms of its mean Dice of 96.61% and mean Intersection over Union (mIoU) of 82.45% with the Kvasir-SEG dataset. Similarly, with the CVC-ClinicDB dataset, the method achieved a mean Dice of 89.95% and an mIoU of 81.38%.

Colorectal cancer has a high incidence rate in all countries around the world, and the survival rate of patients is improved by early detection. With the development of object detection technology based on deep learning, computer-aided diagnosis of colonoscopy medical images becomes a reality, which can effectively reduce the occurrence of missed diagnosis and misdiagnosis. In medical image recognition, the assumption that training samples follow independent identical distribution (IID) is the key to the high accuracy of deep learning. However, the classification of medical images is unbalanced in most cases. This paper proposes a new loss function named the double-balanced loss function for the deep learning model, to improve the impact of datasets on classification accuracy. It introduces the effects of sample size and sample difficulty to the loss calculation and deals with both sample size imbalance and sample difficulty imbalance. And it combines with deep learning to build the medical diagnosis model for colorectal cancer. Experimentally verified by three colorectal white-light endoscopic image datasets, the double-balanced loss function proposed in this paper has better performance on the imbalance classification problem of colorectal medical images.

• Colonoscopy
• Data augmentation
• Deep learning
• Generative adversarial networks
• Polyps
• Segmentation

Accurate in-vivo optical characterization of colorectal polyps is key to select the optimal treatment regimen during colonoscopy. However, reported accuracies vary widely among endoscopists. We developed a novel intelligent medical device able to seamlessly operate in real-time using conventional white light (WL) endoscopy video stream without virtual chromoendoscopy (blue light, BL). In this work, we evaluated the standalone performance of this computer-aided diagnosis device (CADx) on a prospectively acquired dataset of unaltered colonoscopy videos. An international group of endoscopists performed optical characterization of each polyp acquired in a prospective study, blinded to both histology and CADx result, by means of an online platform enabling careful video assessment. Colorectal polyps were categorized by reviewers, subdivided into 10 experts and 11 non-experts endoscopists, and by the CADx as either “adenoma” or “non-adenoma”. A total of 513 polyps from 165 patients were assessed. CADx accuracy in WL was found comparable to the accuracy of expert endoscopists (CADx_WL/Exp; OR 1.211 [0.766–1.915]) using histopathology as the reference standard. Moreover, CADx accuracy in WL was found superior to the accuracy of non-expert endoscopists (CADx_WL/NonExp; OR 1.875 [1.191–2.953]), and CADx accuracy in BL was found comparable to it (CADx_BL/CADx_WL; OR 0.886 [0.612–1.282]). The proposed intelligent device shows the potential to support non-expert endoscopists in systematically reaching the performances of expert endoscopists in optical characterization.

• Humans
• Image Processing, Computer-Assisted/methods
• Neural Networks, Computer

• big data
• codesign
• coprocessors
• fast machine learning
• heterogeneous computing
• machine learning for science
• particle physics

• R01 HL131750 NHLBI NIH HHS

• colorectal cancer
• convolutional neural network (CNN)
• deep learning
• polyp detection
• polyp localization

• DPI2017-87494-R Ministerio de Ciencia y Competitividad, Gobierno de España
• PDC2021-121644-I00 Ministerio de Ciencia e Innovación, Gobierno de España
• ED431C2018/55-GRC Consellería de Educación, Universidades e Formación Profesional, Xunta de Galicia
• 2020.00515.CEECIND Fundação para a Ciência e a Tecnologia (FCT), Portugal
• ED481A-2019/299 Xunta de Galicia
• "María Zambrano" Postdoctoral Fellowship trough Universidade de Vigo Ministerio de Universidades, Gobierno de España

Colorectal cancer presents one of the most elevated incidences of cancer worldwide. Colonoscopy relies on histopathology analysis of hematoxylin-eosin (H&E) images of the removed tissue. Novel techniques such as multi-photon microscopy (MPM) show promising results for performing real-time optical biopsies. However, clinicians are not used to this imaging modality and correlation between MPM and H&E information is not clear. The objective of this paper is to describe and make publicly available an extensive dataset of fully co-registered H&E and MPM images that allows the research community to analyze the relationship between MPM and H&E histopathological images and the effect of the semantic gap that prevents clinicians from correctly diagnosing MPM images. The dataset provides a fully scanned tissue images at 10x optical resolution (0.5 µm/px) from 50 samples of lesions obtained by colonoscopies and colectomies. Diagnostics capabilities of TPF and H&E images were compared. Additionally, TPF tiles were virtually stained into H&E images by means of a deep-learning model. A panel of 5 expert pathologists evaluated the different modalities into three classes (healthy, adenoma/hyperplastic, and adenocarcinoma). Results showed that the performance of the pathologists over MPM images was 65% of the H&E performance while the virtual staining method achieved 90%. MPM imaging can provide appropriate information for diagnosing colorectal cancer without the need for H&E staining. However, the existing semantic gap among modalities needs to be corrected.

• Colorectal Polyps
• Convolutional Neural Network (CNN)
• Dataset
• Multiphoton Microscopy (MPM)
• Optical Biopsy

Confocal microscopy image analysis is a useful method for neoplasm diagnosis. Many ambiguous cases are difficult to distinguish with the naked eye, thus leading to high inter-observer variability and significant time investments for learning this method. We aimed to develop a deep learning-based neoplasm classification model that classifies confocal microscopy images of 10× magnified colon tissues into three classes: neoplasm, inflammation, and normal tissue. ResNet50 with data augmentation and transfer learning approaches was used to efficiently train the model with limited training data. A class activation map was generated by using global average pooling to confirm which areas had a major effect on the classification. The proposed method achieved an accuracy of 81%, which was 14.05% more accurate than three machine learning-based methods and 22.6% better than the predictions made by four endoscopists. ResNet50 with data augmentation and transfer learning can be utilized to effectively identify neoplasm, inflammation, and normal tissue in confocal microscopy images. The proposed method outperformed three machine learning-based methods and identified the area that had a major influence on the results. Inter-observer variability and the time required for learning can be reduced if the proposed model is used with confocal microscopy image analysis for diagnosis.

• Artificial intelligence
• Colorectal cancer
• Colorectal polyps
• Deep learning
• Machine learning
• Medical images

• Artificial Intelligence
• Colonic Polyps/diagnosis
• Colonoscopy
• Colorectal Neoplasms/diagnosis
• Early Detection of Cancer
• Humans

This paper presents an active locomotion capsule endoscope system with 5D position sensing and real-time automated polyp detection for small-bowel and colon applications. An electromagnetic actuation system (EMA) consisting of stationary electromagnets is utilized to remotely control a magnetic capsule endoscope with multi-degree-of-freedom locomotion. For position sensing, an electronic system using a magnetic sensor array is built to track the position and orientation of the magnetic capsule during movement. The system is integrated with a deep learning model, named YOLOv3, which can automatically identify colorectal polyps in real-time with an average precision of 85%. The feasibility of the proposed method concerning active locomotion and localization is validated and demonstrated through in vitro experiments in a phantom duodenum. This study provides a high-potential solution for automatic diagnostics of the bowel and colon using an active locomotion capsule endoscope, which can be applied for a clinical site in the future.

• active locomotion capsule endoscope
• capsule localization
• deep learning
• magnetic capsule endoscope
• polyp detection

• CAD system
• Deep learning
• Gastric polyp
• Gastrointestinal endoscopy
• YOLOv4

• AdaBoost ensemble learning
• colorectal cancer (CRC)
• deep residual network
• polyps
• principal component analysis

• Artificial Intelligence
• Colonic Polyps/diagnostic imaging
• Diagnosis, Computer-Assisted
• Humans
• Machine Learning
• Neural Networks, Computer

The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

• artificial intelligence
• colonoscopy
• colorectal cancer
• computer-aided detection
• computer-aided diagnosis
• precision oncology
• robotic-assisted surgery
• screening
• therapy

• Adenoma
• Artificial Intelligence
• Colonoscopy
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/epidemiology
• Colorectal Neoplasms/therapy
• Early Detection of Cancer
• Female
• Humans
• Male

• Colonoscopy
• deep learning
• domain adaptation
• endoscopy
• jigsaw
• lesion classification
• out-of-distribution detection
• semi-supervised
• unsupervised

• R21 EB024700 NIBIB NIH HHS
• T32 GM007057 NIGMS NIH HHS