Sa2026 EXPLOITING INTERVARIABLITY OF EXPERT ANNOTATIONS FOR EARLY BARRETT'S CANCER IN WHITE LIGHT ENDOSCOPY LEADS TO BETTER LOCALIZATION PERFORMANCE OF AI ALGORITHMS

Background: Diagnosis of portal hypertensive gastropathy (PHG) is based on endoscopic criteria. I-scan technology, a new technique of virtual chromoendoscopy, increases the diagnostic accuracy for lesions in the gastrointestinal tract. Aim: To establish the role of i-scan endoscopy in the diagnosis of PHG. Method: In this prospective study, endoscopic examination was conducted first by using white light and after that i-scan 1 and i-scan 2 technology in a group of 50 consecutive cirrhotic patients. The endoscopic diagnostic criteria for PHG followed the Baveno criteria. The interobserver agreement between white light endoscopy and i-scan endoscopy was determined using Cohen’s kappa statistics. Results: Forty-five of the 50 patients met the diagnostic criteria for PHG when examined by i-scan endoscopy and 39 patients were diagnosed with PHG by white light endoscopy. The strength of agreement between the two methods for the diagnosis of PHG was moderate (k=0.565; 95%CI 0.271-0.859; p<0.001). I-scan 1 classified the mosaic pattern better than classic endoscopy; i-scan 2 described better the red spots. Conclusion: I-scan examination increased the diagnostic sensitivity of PHG. The diagnostic criteria (mosaic pattern and red spots) were easier to observe endoscopically using i-scan than in white light.Abbreviations: FICE: Fuji Intelligent chromoendoscopy; GAVE: gastric antral vascular ectasia; NBI: narrow band imaging; PHG: portal hypertensive gastropathy; PHT: portal hypertension; UGIB: upper gastrointestinal bleeding.

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The Impact of Narrow-band Imaging on the Pre- and Intra- operative Assessments of Neoplastic and Preneoplastic Laryngeal Lesions. A Systematic Review

International Archives of Otorhinolaryngology ◽

10.1055/s-0040-1719119 ◽

2020 ◽

Author(s):

Carmelo Saraniti ◽

Enzo Chianetta ◽

Giuseppe Greco ◽

Norhafiza Mat Lazim ◽

Barbara Verro

Keyword(s):

White Light ◽

Diagnostic Tool ◽

Narrow Band ◽

Narrow Band Imaging ◽

Resection Margins ◽

Predictive Values ◽

White Light Endoscopy ◽

Laryngeal Lesions ◽

Sensitivity Specificity ◽

The Impact

Introduction Narrow-band imaging is an endoscopic diagnostic tool that, focusing on superficial vascular changes, is useful to detect suspicious laryngeal lesions, enabling their complete excision with safe and tailored resection margins. Objectives To analyze the applications and benefits of narrow-band imaging in detecting premalignant and malignant laryngeal lesions through a comparison with white-light endoscopy. Data Synthesis A literature search was performed in the PubMed, Scopus and Web of Science databases using strict keywords. Then, two authors independently analyzed the articles, read the titles and abstracts, and read completely only the relevant studies according to certain eligibility criteria. In total, 14 articles have been included in the present review; the sensitivity, specificity, positive and negative predictive values, and accuracy of pre- and/or intraoperative narrow-band imaging were analyzed. The analysis showed that narrow-band imaging is better than white-light endoscopy in terms of sensitivity, specificity, positive and negative predictive values, and accuracy regarding the ability to identify cancer and/or precancerous laryngeal lesions. Moreover, the intraoperative performance of narrow-band imaging resulted more effective than the in-office performance. Conclusion Narrow-band imaging is an effective diagnostic tool to detect premalignant and malignant laryngeal lesions and to define proper resection margins. Moreover, narrow-band imaging is useful in cases of leukoplakia that may cover a possible malignant lesion and that cannot be easily assessed with white-light endoscopy. Finally, a shared, simple and practical classification of laryngeal lesions, such as that of the European Laryngological Society, is required to identify a shared lesion management strategy. Key Points

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Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽

10.1055/a-1311-8570 ◽

2020 ◽

Author(s):

Alanna Ebigbo ◽

Robert Mendel ◽

Tobias Rückert ◽

Laurin Schuster ◽

Andreas Probst ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Pilot Study ◽

White Light ◽

Submucosal Invasion ◽

Learning System ◽

Endoscopic Images ◽

Barrett’S Cancer ◽

Significant Difference ◽

Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

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