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

Hussein, Mohamed; Puyal, Juana González‐Bueno; Mountney, Peter; Lovat, Laurence; Haidry, Rehan

doi:10.3748/wjg.v26.i38.5784

Cited by 9 publications

(8 citation statements)

References 46 publications

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“…AI systems are being used (eg, I-scan and CADDIE) in BO to find dysplasia current methods miss. [41][42][43] Lesion detection in BO is notoriously challenging for the uninitiated, and AI may provide some guidance; however, it requires a skilled workforce to use and interpret the signals from AI devices and to counsel patients about the potential limitations as responsibility remains with the operator. 44 Machine learning tools could help to teach a dedicated workforce how to detect dysplasia.…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Developing patient-orientated Barrett’s oesophagus services: the role of dedicated services

Ratcliffe

Britton

Hamdy

et al. 2022

BMJ Open Gastroenterol

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IntroductionBarrett’s oesophagus (BO) is common and is a precursor to oesophageal adenocarcinoma with a 0.33% per annum risk of progression. Surveillance and follow-up services for BO have been shown to be lacking, with studies showing inadequate adherence to guidelines and patients reporting a need for greater disease-specific knowledge. This review explores the emerging role of dedicated services for patients with BO.MethodsA literature search of PubMed, MEDLINE, Embase, Emcare, HMIC, BNI, CiNAHL, AMED and PsycINFO in regard to dedicated BO care pathways was undertaken.ResultsProspective multicentre and randomised trials were lacking. Published cohort data are encouraging with improvements in guideline adherence with dedicated services, with one published study showing significant improvements in dysplasia detection rates. Accuracy of allocation to surveillance endoscopy has been shown to hold cost savings, and a study of a dedicated clinic showed increased discharges from unnecessary surveillance. Training modalities for BO surveillance and dysplasia detection exist, which could be used to educate a BO workforce. Qualitative and quantitative studies have shown patients report high levels of cancer worry and poor disease-specific knowledge, but few studies have explored follow-up care models despite being a patient and clinician priority for research.ConclusionsCost–benefit analysis for dedicated services, considering both financial and environmental impacts, and more robust clinical data must be obtained to support this model of care in the wider health service. Greater understanding is needed of the root causes for poor guideline adherence, and disease-specific models of care should be designed around clinical and patient-reported outcomes to address the unmet needs of patients with BO.

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Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Developing patient-orientated Barrett’s oesophagus services: the role of dedicated services

Ratcliffe

Britton

Hamdy

et al. 2022

BMJ Open Gastroenterol

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“… 23 IPCL is commonly identified using chromoendoscopy or advanced endoscopic imaging with NBI. 24 The precise classification in real-time is subjective, inconsistent and reliant on experienced endoscopists. With the increasing volume of work in endoscopy, AI models have been developed to improve the diagnostic accuracy of IPCLs and possibly mitigate the need for expert endoscopists.…”

Section: Resultsmentioning

confidence: 99%

The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis

Guidozzi

Menon

Chidambaram

et al. 2023

Diseases of the Esophagus

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Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma—the pooled sensitivity and specificity were 91.2% (84.3–95.2%) and 80% (64.3–89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8–96.4) and 86.9% (81.7–90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.

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“…4 Despite advances in endoscopic imaging, BE dysplasia is still missed. 5 A metaanalysis showed that amongst adults with NDBE at index endoscopy and prolonged follow up, 25% of EAC's are diagnosed within a year of the index procedure. They were classified as a missed diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…This is time consuming and may suffer from sampling error 3 and poor compliance 4 . Despite advances in endoscopic imaging, BE dysplasia is still missed 5 . A meta‐analysis showed that amongst adults with NDBE at index endoscopy and prolonged follow up, 25% of EAC's are diagnosed within a year of the index procedure.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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

Cited by 9 publications

References 46 publications

Developing patient-orientated Barrett’s oesophagus services: the role of dedicated services

Developing patient-orientated Barrett’s oesophagus services: the role of dedicated services

The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis

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

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