Detecting colon polyps in endoscopic images using artificial intelligence constructed with automated collection of annotated images from an endoscopy reporting system

2021 ◽  
Author(s):  
Keisuke Hori ◽  
Hiroaki Ikematsu ◽  
Yoichi Yamamoto ◽  
Hiroki Matsuzaki ◽  
Nobuyoshi Takeshita ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Reporting System ◽  
Colon Polyps ◽  
Endoscopic Images

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

Endoscopy ◽  
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.

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

scholarly journals Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis

2020 ◽  
Vol 08 (11) ◽  
pp. E1584-E1594
Author(s):  
Babu P. Mohan ◽  
Shahab R. Khan ◽  
Lena L. Kassab ◽  
Suresh Ponnada ◽  
Parambir S. Dulai ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Predictive Value ◽  
Colorectal Neoplasia ◽  
Meta Analysis ◽  
Final Analysis ◽  
Esophageal Neoplasia ◽  
Endoscopic Images ◽  
Gastrointestinal Lesions ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Abstract Background and study aims Recently, a growing body of evidence has been amassed on evaluation of artificial intelligence (AI) known as deep learning in computer-aided diagnosis of gastrointestinal lesions by means of convolutional neural networks (CNN). We conducted this meta-analysis to study pooled rates of performance for CNN-based AI in diagnosis of gastrointestinal neoplasia from endoscopic images. Methods Multiple databases were searched (from inception to November 2019) and studies that reported on the performance of AI by means of CNN in the diagnosis of gastrointestinal tumors were selected. A random effects model was used and pooled accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Pooled rates were categorized based on the gastrointestinal location of lesion (esophagus, stomach and colorectum). Results Nineteen studies were included in our final analysis. The pooled accuracy of CNN in esophageal neoplasia was 87.2 % (76–93.6) and NPV was 92.1 % (85.9–95.7); the accuracy in lesions of stomach was 85.8 % (79.8–90.3) and NPV was 92.1 % (85.9–95.7); and in colorectal neoplasia the accuracy was 89.9 % (82–94.7) and NPV was 94.3 % (86.4–97.7). Conclusions Based on our meta-analysis, CNN-based AI achieved high accuracy in diagnosis of lesions in esophagus, stomach, and colorectum.

Prospective Evaluation of a New Artificial Intelligence System For Detection of Colon Polyps

2021 ◽  
Author(s):  
C Zippelius ◽  
J Schedel ◽  
D Brookman-Amissah ◽  
K Muehlenberg ◽  
W Schorr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colon Polyps ◽  
Prospective Evaluation ◽  
Artificial Intelligence System ◽  
Intelligence System

scholarly journals Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy (Preprint)

2020 ◽  
Author(s):  
Chang Seok Bang ◽  
Jae Jun Lee ◽  
Gwang Ho Baik
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Helicobacter Pylori ◽  
Diagnostic Test ◽  
Meta Analysis ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Endoscopic Images ◽  
Number Of Patients ◽  
H Pylori

BACKGROUND <i>Helicobacter pylori</i> plays a central role in the development of gastric cancer, and prediction of <i>H pylori</i> infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of <i>H pylori</i> infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification. OBJECTIVE This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of <i>H pylori</i> infection using endoscopic images. METHODS Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of <i>H pylori</i> infection and with application of AI for the prediction of <i>H pylori</i> infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of <i>H pylori</i> infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with <i>H pylori</i> infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images. CONCLUSIONS An AI algorithm is a reliable tool for endoscopic diagnosis of <i>H pylori</i> infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome. CLINICALTRIAL PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957

scholarly journals Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images

2018 ◽  
Vol 21 (4) ◽  
pp. 653-660 ◽  
Author(s):  
Toshiaki Hirasawa ◽  
Kazuharu Aoyama ◽  
Tetsuya Tanimoto ◽  
Soichiro Ishihara ◽  
Satoki Shichijo ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Convolutional Neural Network ◽  
Endoscopic Images
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