Tu2004 ARTIFICIAL INTELLIGENCE NETWORK TO AID THE DIAGNOSIS OF EARLY ESOPHAGEAL SQUAMOUS CELL CARCINOMA AND ESOPHAGEAL INFLAMMATIONS IN WHITE LIGHT ENDOSCOPIC IMAGES

Tang, Dehua; Wang, Xuying; Wang, Lei; He, Guogeng; Fu, Yiwei; Li, Xianhong; Zhang, Yao; Guo, Huimin; Zhu, Hao; Xu, Guifang; Zou, Xiaoping

doi:10.1016/j.gie.2019.03.1148

Cited by 6 publications

(3 citation statements)

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“…Further study of an endocytoscopy trained CNN (GoogLeNet) demonstrated sensitivity of 92.6% on a test database of 55 patients (27 patients with neoplasia) . Performance of a retrospectively trained CNN demonstrated was excellent (sensitivity 97% and specificity 94%) for both the detection of early SCC and differentiation from inflammation over 948 test images . The preliminary performance of CNNs trained on static images on unseen videos was able to detect SCC in eight of 10 patients.…”

Section: Artificial Intelligence/computer‐assisted Diagnosismentioning

confidence: 95%

“…21 Performance of a retrospectively trained CNN demonstrated was excellent (sensitivity 97% and specificity 94%) for both the detection of early SCC and differentiation from inflammation over 948 test images. 22 The preliminary performance of CNNs trained on static images on unseen videos was able to detect SCC in eight of 10 patients. The positive predictive value was limited at 42.1% reflecting a higher false positive rate which may be improved with further training.…”

Section: Squamous Cell Neoplasia (Scc)mentioning

confidence: 98%

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The future of endoscopy: Advances in endoscopic image innovations

Gulati

Patel

Emmanuel

et al. 2019

Digestive Endoscopy

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The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy.

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Section: Artificial Intelligence/computer‐assisted Diagnosismentioning

confidence: 95%

Section: Squamous Cell Neoplasia (Scc)mentioning

confidence: 98%

The future of endoscopy: Advances in endoscopic image innovations

Gulati

Patel

Emmanuel

et al. 2019

Digestive Endoscopy

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“…A retrospective CNN demonstrated excellent diagnostic performance for the detection of early SCC and differentiation from inflammation. 17 The preliminary performance of CNNs on unseen videos having been developed on static images was able to detect SCC in 8 of 10 patients. 18…”

Section: Upper Gastrointestinal Endoscopymentioning

confidence: 99%

Artificial intelligence in luminal endoscopy

Gulati

Emmanuel

Patel

et al. 2020

Clin Med Insights Gastroenterol

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Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett’s, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice.

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