33 research outputs found

    Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey

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    The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis. There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus. Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers, therefore curative endoscopic therapy can be offered. Research in this area of artificial intelligence is expanding and the future looks promising. In this review article we will review current advances in artificial intelligence in the oesophagus and future directions for development

    A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks

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    BACKGROUND AND AIMS: Seattle protocol biopsies for Barrett's Esophagus (BE) surveillance are labour intensive with low compliance. Dysplasia detection rates vary, leading to missed lesions. This can potentially be offset with computer aided detection. We have developed convolutional neural networks (CNNs) to identify areas of dysplasia and where to target biopsy. METHODS: 119 Videos were collected in high-definition white light and optical chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and non-dysplastic BE (NDBE). We trained an indirectly supervised CNN to classify images as dysplastic/non-dysplastic using whole video annotations to minimise selection bias and maximise accuracy. The CNN was trained using 148,936 video frames (31 dysplastic patients, 31 NDBE, two normal esophagus), validated on 25,161 images from 11 patient videos and tested on 264 iscan-1 images from 28 dysplastic and 16 NDBE patients which included expert delineations. To localise targeted biopsies/delineations, a second directly supervised CNN was generated based on expert delineations of 94 dysplastic images from 30 patients. This was tested on 86 i-scan one images from 28 dysplastic patients. FINDINGS: The indirectly supervised CNN achieved a per image sensitivity in the test set of 91%, specificity 79%, area under receiver operator curve of 93% to detect dysplasia. Per-lesion sensitivity was 100%. Mean assessment speed was 48 frames per second (fps). 97% of targeted biopsy predictions matched expert and histological assessment at 56 fps. The artificial intelligence system performed better than six endoscopists. INTERPRETATION: Our CNNs classify and localise dysplastic Barrett's Esophagus potentially supporting endoscopists during surveillance

    Improving the endoscopic detection of early oesophageal neoplasia

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    The endoscopic detection of oesophageal cancer is complex; largely owing to the subtle appearances of early oesophageal lesions on endoscopy, as well as clinician experience. Early detection is vital, since lesions confined to the mucosal or superficial layers of the submucosa can be treated with endoscopic eradication therapies to good effect. Conversely, patients presenting with late stage oesophageal cancer have very poor outcomes. Improving the detection of oesophageal cancer requires a multifaceted approach. Since the symptoms patients present with are often vague until the disease has progressed beyond the point that it is curable, developing a way to risk stratify or rationalise patient access to endoscopy, based on objective markers of the presence of serious underlying pathology, is vital to allow adequate resource provision in the modern UK endoscopy unit. In patients who do undergo endoscopy there remains a significant mis-rate of cancers in those with de-novo oesophageal cancer as well as those enrolled in Barrett’s oesophagus surveillance programs. We postulate that advanced imaging technologies, in combination with artificial intelligence systems, may improve the diagnostic performance of endoscopists assessing for oesophageal cancers. This body of work presents a comprehensive review of the literature surrounding the epidemiology, detection, classification and endoscopic treatment modalities for both squamous cell and adenocarcinomas of the oesophagus. It also presents four studies undertaken with the overarching aim of improving the endoscopic detection of oesophageal cancer. The first study presents a methodology for the quantification of a biomarker from gastric aspirate samples and an assessment of whether differences in expression levels can be used to predict the presence of neoplasia in patients with or without Barrett’s oesophagus. The second study investigates the role of a novel, advanced endoscopic imaging technology and whether it improves the diagnostic performance of expert and trainee endoscopists assessing Barrett’s oesophagus for the presence of dysplasia or adenocarcinoma. The final two studies present a significant body of work assessing the feasibility and diagnostic performance of a novel artificial intelligence system designed as part of this thesis, for the detection and characterisation of squamous cell cancer of the oesophagus based on microvascular patterns

    Role of artificial intelligence in diagnosing Barrett’s esophagus-related neoplasia

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    Barrett’s esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett’s esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett’s esophagus and elaborate on potential artificial intelligence in the future

    Artificial intelligence in endoscopy: the challenges and future directions

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    Artificial intelligence based approaches, in particular deep learning, have achieved state-of-the-art performance in medical fields with increasing number of software systems being approved by both Europe and United States. This paper reviews their applications to early detection of oesophageal cancers with a focus on their advantages and pitfalls. The paper concludes with future recommendations towards the development of a real-time, clinical implementable, interpretable and robust diagnosis support systems

    Developments in molecular and advanced endoscopic imaging in esophageal cancer

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    Esophageal cancer, including esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), shows high incidence and poor prognosis. The early detection and endoscopic treatment of (pre)malignant lesions of esophageal cancer significantly improve disease outcomes of patients. However, the high-definition white-light endoscopy followed by random biopsy is reported with a non-ignorable miss rate. The development of advanced endoscopic techniques, such as fluorescence molecular endoscopy (FME) and endocytoscopy, can aid endoscopists in diagnosing early (pre)malignant lesions in vivo. FME realizes wide-field molecular imaging under endoscopy, which serves as a red flag technique for endoscopists by fluorescently highlighting the disease-specific molecule. In Chapter 3 and 4, we identified suitable target proteins and developed near-infrared fluorescent tracers for FME to detect ESCC and EAC at an early stage. In Chapter 6, we investigated the feasibility of assessing pathological response of EAC patients after neoadjuvant chemoradiotherapy by Bevacizumab-800CW guided FME.Endocytoscopy is a pin-point imaging technique that provides endoscopists with magnified optical cellular morphology and subcellular characteristics, referred to as an optical biopsy. In Chapter 5, we developed a classification criteria, an online training module for clinicians and a computer-aided diagnosis (CAD) algorithm based on in vivo images of fourth-generation endocytoscopy to distinguish dysplastic from non-dysplastic Barrett's esophagus tissue. We further investigated the interaction of this CAD algorithm with the clinicians

    A deep-learning approach to aid in diagnosing Barrett’s oesophagus related dysplasia

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    Barrett's oesophagus is the only known precursor to oesophagus carcinoma. Histologically, it is defined as a condition of columnar cells replacing the standard squamous lining. Those altered cells are prone to cytological and architectural abnormalities, known as dysplasia. The dysplastic degree varies from low to high grade and can evolve into invasive carcinoma or adenocarcinoma. Thus, detecting high-grade and intramucosal carcinoma during the surveillance of Barrett's oesophagus patients is vital so they can be treated by surgical resection. Unfortunately, the achieved interobserver agreement for grading dysplasia among pathologists is only fair to moderate. Nowadays, grading Barrett's dysplasia is limited to visual examination by pathologists for glass or virtual slides. This work aims to diagnose different grades of dysplasia in Barrett’s oesophagus, particularly high-grade dysplasia, from virtual histopathological slides of oesophagus tissue. In the first approach, virtual slides were analysed at a low magnification to detect regions of interest and predict the grade of dysplasia based on the analysis of the virtual slides at 10X magnification. Transfer learning was employed to partially fine-tune two deep-learning networks using healthy and Barrett’s oesophagus tissue. Then, the two networks were connected. The proposed model achieved 0.57 sensitivity, 0.79 specificity and moderate agreement with a pathologist. On the contrary, the second approach processed the slides at a higher magnification (40X magnification). It adapted novelty detection and local outlier factor alongside transfer learning to solve the multiple instances learning problem. It increased the performance of the diagnosis to 0.84 sensitivity and 0.92 specificity, and the interobserver agreement reached a substantial level. Finally, the last approach mimics the pathologists’ procedure to diagnose dysplasia, relying on both magnifications. Thus, their behaviours during the assessment were analysed. As a result, it was found that employing a multi-scale approach to detect dysplastic tissue using a low magnification level (10X magnification) and grade dysplasia at a higher level (40X magnification). The proposed computer-aided diagnosis system was built using networks from the first two approaches. It scored 0.90 sensitivity, 0.94 specificity and a substantial agreement with the pathologist and a moderate agreement with the other expert

    Artificial intelligence in gastroenterology: a state-of-the-art review

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    The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.Cellular mechanisms in basic and clinical gastroenterology and hepatolog
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