Artificial Intelligence in Gastrointestinal Endoscopy.

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully

Esophageal Abnormality Detection Using DenseNet Based Faster R-CNN With Gabor Features

Early detection of esophageal abnormalities can help in preventing the progression of the disease into later stages. During esophagus examination, abnormalities are often overlooked due to the irregular shape, variable size, and the complex surrounding area which requires a significant effort and experience. In this paper, a novel deep learning model which is based on faster region-based convolutional neural network (Faster R-CNN) is presented to automatically detect abnormalities in the esophagus from endoscopic images. The proposed detection system is based on a combination of Gabor handcrafted features with the CNN features. The densely connected convolutional networks (DenseNets) architecture is embraced to extract the CNN features providing a strengthened feature propagation between the layers and alleviate the vanishing gradient problem. To address the challenges of detecting abnormal complex regions, we propose fusing extracted Gabor features with the CNN features through concatenation to enhance texture details in the detection stage. Our newly designed architecture is validated on two datasets (Kvasir and MICCAI 2015). Regarding the Kvasir, the results show an outstanding performance with a recall of 90.2% and a precision of 92.1% with a mean of average precision (mAP) of 75.9%. While for the MICCAI 2015 dataset, the model is able to surpass the state-of-the-art performance with 95% recall and 91% precision with mAP value of 84%. The experimental results demonstrate that the system is able to detect abnormalities in endoscopic images with good performance without any human intervention

Focal Spot, Winter 1976

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Focal Spot, Summer 1996

https://digitalcommons.wustl.edu/focal_spot_archives/1073/thumbnail.jp

Automatic Esophageal Abnormality Detection and Classification

Esophageal cancer is counted as one of the deadliest cancers worldwide ranking the sixth among all types of cancers. Early esophageal cancer typically causes no symp- toms and mainly arises from overlooked/untreated premalignant abnormalities in the esophagus tube. Endoscopy is the main tool used for the detection of abnormalities, and the cell deformation stage is confirmed by taking biopsy samples. The process of detection and classification is considered challenging for several reasons such as; different types of abnormalities (including early cancer stages) can be located ran- domly throughout the esophagus tube, abnormal regions can have various sizes and appearances which makes it difficult to capture, and failure in discriminating between the columnar mucosa from the metaplastic epithelium. Although many studies have been conducted, it remains a challenging task and improving the accuracy of auto- matically classifying and detecting different esophageal abnormalities is an ongoing field. This thesis aims to develop novel automated methods for the detection and classification of the abnormal esophageal regions (precancerous and cancerous) from endoscopic images and videos. In this thesis, firstly, the abnormality stage of the esophageal cell deformation is clas- sified from confocal laser endomicroscopy (CLE) images. The CLE is an endoscopic tool that provides a digital pathology view of the esophagus cells. The classifica- tion is achieved by enhancing the internal features of the CLE image, using a novel enhancement filter that utilizes fractional integration and differentiation. Different imaging features including, Multi-Scale pyramid rotation LBP (MP-RLBP), gray level co-occurrence matrices (GLCM), fractal analysis, fuzzy LBP and maximally stable extremal regions (MSER), are calculated from the enhanced image to assure a robust classification result. The support vector machine (SVM) and random forest (RF) classifiers are employed to classify each image into its pathology stage. Secondly, we propose an automatic detection method to locate abnormality regions from high definition white light (HD-WLE) endoscopic images. We first investigate the performance of different deep learning detection methods on our dataset. Then we propose an approach that combines hand-designed Gabor features with extracted convolutional neural network features that are used by the Faster R-CNN to detect abnormal regions. Moreover, to further improve the detection performance, we pro- pose a novel two-input network named GFD-Faster RCNN. The proposed method generates a Gabor fractal image from the original endoscopic image using Gabor filters. Then features are learned separately from the endoscopic image and the gen- erated Gabor fractal image using the densely connected convolutional network to detect abnormal esophageal regions. Thirdly, we present a novel model to detect the abnormal regions from endoscopic videos. We design a 3D Sequential DenseConvLstm network to extract spatiotem- poral features from the input videos that are utilized by a region proposal network and ROI pooling layer to detect abnormality regions in each frame throughout the video. Additionally, we suggest an FS-CRF post-processing method that incorpor- ates the Conditional Random Field (CRF) on a frame-based level to recover missed abnormal regions in neighborhood frames within the same clip. The methods are evaluated on four datasets: (1) CLE dataset used for the classific- ation model, (2) Publicly available dataset named Kvasir, (3) MICCAI’15 Endovis challenge dataset, Both datasets (2) and (3) are used for the evaluation of detection model from endoscopic images. Finally, (4) Gastrointestinal Atlas dataset used for the evaluation of the video detection model. The experimental results demonstrate promising results of the different models and have outperformed the state-of-the-art methods

Yale-New Haven Magazine, Summer 2002

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Proteomics

Biomedical research has entered a new era of characterizing a disease or a protein on a global scale. In the post-genomic era, Proteomics now plays an increasingly important role in dissecting molecular functions of proteins and discovering biomarkers in human diseases. Mass spectrometry, two-dimensional gel electrophoresis, and high-density antibody and protein arrays are some of the most commonly used methods in the Proteomics field. This book covers four important and diverse areas of current proteomic research: Proteomic Discovery of Disease Biomarkers, Proteomic Analysis of Protein Functions, Proteomic Approaches to Dissecting Disease Processes, and Organelles and Secretome Proteomics. We believe that clinicians, students and laboratory researchers who are interested in Proteomics and its applications in the biomedical field will find this book useful and enlightening. The use of proteomic methods in studying proteins in various human diseases has become an essential part of biomedical research

Towards real-world clinical colonoscopy deep learning models for video-based bowel preparation and generalisable polyp segmentation

Colorectal cancer is the most prevalence type of cancers within the digestive system. Early screening and removal of precancerous growths in the colon decrease mortality rate. The golden standard screening type for colon is colonoscopy which is conducted by a medical expert (i.e., colonoscopist). Nevertheless, due to human biases, fatigue, and experience level of the colonoscopist, colorectal cancer missing rate is negatively affected. Artificial intelligence (AI) methods hold immense promise not just in automating colonoscopy tasks but also enhancing the performance of colonoscopy screening in general. The recent development of intense computational GPUs enabled a computational-demanding AI method (i.e., deep learning) to be utilised in various medical applications. However, given the gap between the clinical-practice and the proposed deep learning models in the literature, the actual effectiveness of such methods is questionable. Hence, this thesis highlights such gaps that arises from the separation between the theoretical and practical aspect of deep learning methods applied to colonoscopy. The aim is to evaluate the current state of deep learning models applied in colonoscopy from a clinical angle, and accordingly propose better evaluation strategies and deep learning models. The aim is translated into three distinct objectives. The first objective is to develop a systematic evaluation method to assess deep learning models from a clinical perspective. The second objective is to develop a novel deep learning architecture that leverages spatial information within colonoscopy videos to enhance the effectiveness of deep learning models on real-clinical environments. The third objective is to enhance the generalisability of deep learning models on unseen test images by developing a novel deep learning framework. To translate these objectives into practice, two critical colonoscopy tasks, namely, automatic bowel preparation and polyp segmentation are attacked. In both tasks, subtle overestimations are found in the literature and discussed in the thesis theoretically and demonstrated empirically. These overestimations are induced by improper validation sets that would not appear or represent the real-world clinical environment. Arbitrary dividing colonoscopy datasets to do deep learning evaluation can result in producing similar distributions, hence, achieving unrealistic results. Accordingly, these factors are considered in the thesis to avoid such subtle overestimation. For the automatic bowel preparation task, colonoscopy videos that closely resemble clinical settings are considered as input and accordingly it necessitates the design of the proposed model as well as evaluation experiments. The proposed model’s architecture is designed to utilise both temporal and spatial information within colonoscopy videos using Gated Recurrent Unit (GRU) and a proposed Multiplexer unit, respectively. Meanwhile for the polyp segmentation task, the efficiency of current deep learning models is tested in terms of their generalisation capabilities using unseen test sets from different medical centres. The proposed framework consists of two connected models. The first model is responsible for gradually transforming textures of input images and arbitrary change their colours. Meanwhile the second model is a segmentation model that outlines polyp regions. Exposing the segmentation model to such transformed images acquires the segmentation model texture/colour invariant properties, hence, enhances the generalisability of the segmentation model. In this thesis, rigorous experiments are conducted to evaluate the proposed models against the state-of-the-art models. The yielded results indicate that the proposed models outperformed the state-of-the-art models under different settings

The Selection Strategy for Circulating Tumor Cells (CTCs) Isolation and Enumeration: Technical Features, Methods, and Clinical Applications

The key aim of the proposed chapter is to provide readers a brief description for the most important parts of the field of circulating tumor cells (CTCs): the core techniques, including negative and positive selection‐based CTC isolation, and the differences between them. Most importantly, we will also review the clinical applications and important findings in clinical trials. The evidence‐based review will not only help clinicians use CTCs to predict recurrence and foresee the disease‐related outcomes but also to inspire the researchers in this field to conduct further investigations

Towards real-world clinical colonoscopy deep learning models for video-based bowel preparation and generalisable polyp segmentation

Colorectal cancer is the most prevalence type of cancers within the digestive system. Early screening and removal of precancerous growths in the colon decrease mortality rate. The golden standard screening type for colon is colonoscopy which is conducted by a medical expert (i.e., colonoscopist). Nevertheless, due to human biases, fatigue, and experience level of the colonoscopist, colorectal cancer missing rate is negatively affected. Artificial intelligence (AI) methods hold immense promise not just in automating colonoscopy tasks but also enhancing the performance of colonoscopy screening in general. The recent development of intense computational GPUs enabled a computational-demanding AI method (i.e., deep learning) to be utilised in various medical applications. However, given the gap between the clinical-practice and the proposed deep learning models in the literature, the actual effectiveness of such methods is questionable. Hence, this thesis highlights such gaps that arises from the separation between the theoretical and practical aspect of deep learning methods applied to colonoscopy. The aim is to evaluate the current state of deep learning models applied in colonoscopy from a clinical angle, and accordingly propose better evaluation strategies and deep learning models. The aim is translated into three distinct objectives. The first objective is to develop a systematic evaluation method to assess deep learning models from a clinical perspective. The second objective is to develop a novel deep learning architecture that leverages spatial information within colonoscopy videos to enhance the effectiveness of deep learning models on real-clinical environments. The third objective is to enhance the generalisability of deep learning models on unseen test images by developing a novel deep learning framework. To translate these objectives into practice, two critical colonoscopy tasks, namely, automatic bowel preparation and polyp segmentation are attacked. In both tasks, subtle overestimations are found in the literature and discussed in the thesis theoretically and demonstrated empirically. These overestimations are induced by improper validation sets that would not appear or represent the real-world clinical environment. Arbitrary dividing colonoscopy datasets to do deep learning evaluation can result in producing similar distributions, hence, achieving unrealistic results. Accordingly, these factors are considered in the thesis to avoid such subtle overestimation. For the automatic bowel preparation task, colonoscopy videos that closely resemble clinical settings are considered as input and accordingly it necessitates the design of the proposed model as well as evaluation experiments. The proposed model’s architecture is designed to utilise both temporal and spatial information within colonoscopy videos using Gated Recurrent Unit (GRU) and a proposed Multiplexer unit, respectively. Meanwhile for the polyp segmentation task, the efficiency of current deep learning models is tested in terms of their generalisation capabilities using unseen test sets from different medical centres. The proposed framework consists of two connected models. The first model is responsible for gradually transforming textures of input images and arbitrary change their colours. Meanwhile the second model is a segmentation model that outlines polyp regions. Exposing the segmentation model to such transformed images acquires the segmentation model texture/colour invariant properties, hence, enhances the generalisability of the segmentation model. In this thesis, rigorous experiments are conducted to evaluate the proposed models against the state-of-the-art models. The yielded results indicate that the proposed models outperformed the state-of-the-art models under different settings