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

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

Endoscopic multimodal imaging in Barrett's oesophagus

The incidence of oesophageal adenocarcinoma (OA) has increased exponentially in the western world over the past few decades. Barrett's oesophagus (BO) is a well known precursor of OA with a risk approximately 20 times more than that of background population. Regular endoscopic surveillance in patients with BO is recommended by most of the national gastroenterological societies. The advantage of Barrett's surveillance is to identify early subtle lesions which could then be managed early to avoid symptomatic and advanced cancers. The detection of such early lesions are challenging as they could be flat and inconspicuous on routine endoscopic examination. In the absence of any lesions, four quadrant biopsies every 1-2 cm of the whole length of Barrett's oesophagus is advised. This technique would map only 5-10% of the surface area of Barrett's segment and hence it is associated with significant sampling error. The improvement in electronics over the past decade has led to the production of endoscopes with better charged coupled devices and image enhancement techniques by altering the spectrum of light. This thesis examines the role of multi modal imaging in Barrett's oesophagus with a focus on detecting dysplasia and early cancer (EC). Firstly, the role of high definition (HD) imaging in routine clinical setting was studied using data from patients who have undergone Barrett's· surveillance. The yield of dysplasia by HD endoscopy was compared to standard definition (SD) endoscopy in this study. The role of narrow band imaging (NBI) with magnification in characterising abnormal lesions detected during BO surveillance was evaluated by performing a meta- analysis of clinical studies. The role of autofluorescence imaging (AFI) in Barrett's oesophagus was examined in detail with a view to understand the biological basis of autofluorescence and to improve the specificity of this technique as it is associated with significant false positive results in clinical studies. A meta-analysis was performed to identify whether AFI has a clinical advantage over white light endoscopy in detecting Barrett's dysplasia and the inter-observer reliability of this technology was studied using AFI expert and AFI non-expert endoscopists. An objective method of measuring the autofluorescence intensity was proposed as a ratio of the red to the green colour tone (AF ratio) of the area of interest. When the AF ratio of the lesion was divided by the AF ratio of the background mucosa, an AF index is obtained. A pilot study was performed to identify a cut-off value of AF index to differentiate high grade dysplasia (HGD) and EC from non-dysplastic BO. Finally, the biological basis of AF intensity was examined using APCmin mouse colonic models. This study looked into the AF ratio of the colonic mucosal lesions and correlated it with the amount of collagen and elastin in the submucosal tissue. Collagen and elastin are known to be the strongest fluorophores of the gastrointestinal tract and the question addressed is whether the low AF intensity associated with dysplastic lesions is due to the thickening of mucosa or to a reduction of collagen and elastin

Endoscopic multimodal imaging in Barrett's oesophagus

The incidence of oesophageal adenocarcinoma (OA) has increased exponentially in the western world over the past few decades. Barrett's oesophagus (BO) is a well known precursor of OA with a risk approximately 20 times more than that of background population. Regular endoscopic surveillance in patients with BO is recommended by most of the national gastroenterological societies. The advantage of Barrett's surveillance is to identify early subtle lesions which could then be managed early to avoid symptomatic and advanced cancers. The detection of such early lesions are challenging as they could be flat and inconspicuous on routine endoscopic examination. In the absence of any lesions, four quadrant biopsies every 1-2 cm of the whole length of Barrett's oesophagus is advised. This technique would map only 5-10% of the surface area of Barrett's segment and hence it is associated with significant sampling error. The improvement in electronics over the past decade has led to the production of endoscopes with better charged coupled devices and image enhancement techniques by altering the spectrum of light. This thesis examines the role of multi modal imaging in Barrett's oesophagus with a focus on detecting dysplasia and early cancer (EC). Firstly, the role of high definition (HD) imaging in routine clinical setting was studied using data from patients who have undergone Barrett's· surveillance. The yield of dysplasia by HD endoscopy was compared to standard definition (SD) endoscopy in this study. The role of narrow band imaging (NBI) with magnification in characterising abnormal lesions detected during BO surveillance was evaluated by performing a meta- analysis of clinical studies. The role of autofluorescence imaging (AFI) in Barrett's oesophagus was examined in detail with a view to understand the biological basis of autofluorescence and to improve the specificity of this technique as it is associated with significant false positive results in clinical studies. A meta-analysis was performed to identify whether AFI has a clinical advantage over white light endoscopy in detecting Barrett's dysplasia and the inter-observer reliability of this technology was studied using AFI expert and AFI non-expert endoscopists. An objective method of measuring the autofluorescence intensity was proposed as a ratio of the red to the green colour tone (AF ratio) of the area of interest. When the AF ratio of the lesion was divided by the AF ratio of the background mucosa, an AF index is obtained. A pilot study was performed to identify a cut-off value of AF index to differentiate high grade dysplasia (HGD) and EC from non-dysplastic BO. Finally, the biological basis of AF intensity was examined using APCmin mouse colonic models. This study looked into the AF ratio of the colonic mucosal lesions and correlated it with the amount of collagen and elastin in the submucosal tissue. Collagen and elastin are known to be the strongest fluorophores of the gastrointestinal tract and the question addressed is whether the low AF intensity associated with dysplastic lesions is due to the thickening of mucosa or to a reduction of collagen and elastin

Endoscopic Assessment and Treatment of Barrett’s Oesophagus

Oesophageal cancer worldwide is the eighth commonest cancer and carries a poor prognosis. Barrett’s oesophagus is the only known risk factor for oesophageal adenocarcinoma. Cancer progresses along a metaplasia-dysplasia pathway. Dysplastic changes may be seen on endoscopic assessment. This thesis presents evidence that i-Scan virtual chromoendoscopy together with acetic acid chromoendoscopy can improve dysplasia detection using a simple classification system. Superficial lesions, without deeper invasion (low and high grade dysplasia, early cancers) have a low risk of distant metastasis. Endoscopic resection and ablation techniques have been demonstrated to have an excellent efficacy and safety profile. The current standard of care for early Barrett’s neoplasia is endoscopic management rather than surgical intervention. Surgery for oesophageal cancer is centred in specialist units due to improved outcomes in high volume centres. The UK radiofrequency ablation registry collects outcomes for patients undergoing endoscopic therapy for Barrett’s neoplasia. This thesis demonstrates that there is no difference in dysplasia or intestinal metaplasia resolution rates or dysplasia recurrence between low and high volume centres. Learning curve analysis suggests that there is a change point at 18 cases, when the observed successful treatment rate of the centre becomes better than the expected rate. Centres should complete 20 cases before competency can be achieved. Treatment of Barrett’s neoplasia involves endoscopic resection of visible lesions. Due to the high risk of metachronous lesions, the remaining Barrett’s epithelium undergoes field ablation, commonly with radiofrequency ablation. Following successful treatment the risk of dysplasia recurrence is 6%. The risk increases with increasing length of the initial Barrett segment and with increasing age. The risk of untreated islands of Barrett’s IM is unknown but this thesis demonstrates that it does not seem to confer an increased risk of recurrence and may not require further ablation if unresponsive to treatment

University of South Alabama College of Medicine Annual Report for 2015-2016

This Annual Report of the College of Medicine catalogues recent accomplishments of our faculty, students, residents, fellows and staff in teaching, research, patient care, scholarly and community service activities during the 2015-16 academic year.https://jagworks.southalabama.edu/com_report/1000/thumbnail.jp

Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early dysplasia in Barrett's Esophagus (BE). VLE generates hundreds of high-resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome eort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images with known histology. Our contribution is twofold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, with superior classication accuracy and execution speed, compared to earlier work. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images with known histology. Optimal classication accuracy is obtained by applying Adaptive Boosting with decision trees and using our modied Haralick features, yielding an area under the receiver operating characteristic of 0.91 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than features with comparable performance

Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early dysplasia in Barrett's Esophagus (BE). VLE generates hundreds of high-resolution, grayscale, cross-sectional images of the esophagus.\u3cbr/\u3eHowever, at present, classifying these images is a time consuming and cumbersome eort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images with known histology. Our contribution is twofold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, with superior classication accuracy and execution speed, compared to earlier work. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images with known histology. Optimal classication accuracy is obtained by applying Adaptive Boosting with decision trees and using our modied Haralick features, yielding an area under the receiver operating characteristic of 0.91 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than features with comparable performance. Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early neoplasia in Barrett’s Esophagus (BE). VLE generates hundreds of high resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome effort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images. Our contribution is threefold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, having superior classification accuracy and speed, compared to earlier work. Third, we evaluate automated parameter tuning by applying simple grid search and feature selection methods. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images. Optimal classification accuracy is obtained by applying a support vector machine and using our modified Haralick features and optimal image cropping, obtaining an area under the receiver operating characteristic of 0.95 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than alternative features with comparable performance