Abstract
Initial results from THE PAIGE PROJECT Portsmouth’s Project on Artificial Intelligence in Gastrointestinal Endoscopy Introduction Endoscopic detection of early Barrett’s neoplasia remains very challenging, with significant inter-observer variation in identifying and assessing these lesions. Artificial intelligence is proposed to help with computer aided detection in this field and could have significant clinical and cost implications. We aim to develop and validate a deep learning (DL) algorithm using Convolutional Neural Networks (CNN) for detection of Barrett’s neoplasia. Methods We collected 132 high definition white light endoscopy images from 46 lesions of histologically confirmed Barrett’s neoplasia. These images were marked and annotated using specially designed software, and reviewed by two experts on advanced assessment and management of Barrett’s neoplasia. Another 119 images of non dysplastic Barrett’s were collected from 20 patients and used as control. Both dysplastic and non dysplastic images were divided into three datasets and used for training, validation and testing of CNN algorithm. We used SegNet segmentation architecture. Graphic processing unit used was ‘GeForce RTX 2080 Ti. We collected metrics on processing speed, sensitivity, specificity and global accuracy at different score thresholds. Results Image processing speed by the algorithm was 33 ms/image. This is much faster than the average human visual response latency which is estimated at 70–100 ms. The algorithm was able to detect Barrett’s neoplasia with sensitivity of 93%, specificity of 78% and global accuracy of 83% (see figure (1) below for examples of algorithm detection). Conclusions We developed and validated an early AI algorithm with high sensitivity and reasonable specificity when compared with PIVI criteria. The ultra short image processing time would suggest this algorithm may be suitable for real time detection of Barrett’s neoplasia. We will develop this model further for use during real time endoscopy.
Highlights
A novel dilatation device, BougieCap (Ovesco, Germany), allows both tactile and optic feedback of the dilatation procedure without the need for fluoroscopy
We aim to develop and validate a deep learning (DL) algorithm using Convolutional Neural Networks (CNN) for detection of Barrett’s neoplasia
These images were marked and annotated using specially designed software, and reviewed by two experts on advanced assessment and management of Barrett’s neoplasia. Another 119 images of non dysplastic Barrett’s were collected from 20 patients and used as control. Both dysplastic and non dysplastic images were divided into three datasets and used for training, validation and testing of CNN algorithm
Summary
A novel dilatation device, BougieCap (Ovesco, Germany), allows both tactile and optic feedback of the dilatation procedure without the need for fluoroscopy. The aim of this study was to assess the safety and efficacy of this device in a prospective cohort of patients. The practice of endoscopic submucosal dissection (ESD) for treatment of early gastrointestinal neoplasia has been increasing in the West, the uptake has been slow due to a long learning curve and higher complication rate. We aim to analyse UK ESD practice through the development of the first UK national ESD registry. Mucosal E. coli are increased in Crohn’s disease (CD). They replicate within macrophages and are inaccessible to penicillins and gentamicin. Hydroxychloroquine is used with doxycycline to treat Whipple’s disease. It raises macrophage intra-vesicular pH and inhibits replication of bacteria that require acidic pH. Ciprofloxacin and doxycycline are effective against E. coli within macrophages
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