scholarly journals Deep Learning Algorithm for Diagnose Endoscopic and Histological Images With Ulcerative Colitis

Author(s):  
Yan Ye ◽  
Xudong Luo ◽  
Qiong Nan ◽  
Yanhong Liu ◽  
Yinglei Miao ◽  
...  

Abstract The goal of treatment for ulcerative colitis is to achieve histological and endoscopic remission. Aiming at the problem that the observer will be affected by subjective factors in the endoscopic evaluation of ulcerative colitis and the cumbersome diagnosis process of histological images, this paper aims to develop a computer-assisted diagnosis system for real-time, objective diagnosis of endoscopic images and use the trained CNN model to predict histological images of patients with ulcerative colitis. Diagnosing endoscopic remission of ulcerative colitis, the accuracy of the CNN is 97.04% (95% CI,96.26%:97.62%). Diagnosing the severity of endoscopic inflammation in patients with ulcerative colitis, the accuracy of the CNN is 90.15% (95% CI, 89.49%:90.82%). The accuracy of predicting histological remission was 91.28%. The kappa coefficient between the CNN model and the biopsy results was 82.56%. The proposed computer-aided diagnosis system can effectively evaluate the inflammation of endoscopic images of patients with ulcerative colitis and predict the remission of histological images with high accuracy and consistency.

2019 ◽  
Vol 89 (2) ◽  
pp. 416-421.e1 ◽  
Author(s):  
Tsuyoshi Ozawa ◽  
Soichiro Ishihara ◽  
Mitsuhiro Fujishiro ◽  
Hiroaki Saito ◽  
Youichi Kumagai ◽  
...  

2017 ◽  
Vol 30 (6) ◽  
pp. 796-811 ◽  
Author(s):  
Afsaneh Jalalian ◽  
Syamsiah Mashohor ◽  
Rozi Mahmud ◽  
Babak Karasfi ◽  
M. Iqbal Saripan ◽  
...  

2015 ◽  
Vol 29 (8) ◽  
pp. 659-665 ◽  
Author(s):  
Mitsuru Koizumi ◽  
Noriaki Miyaji ◽  
Taisuke Murata ◽  
Kazuki Motegi ◽  
Kenta Miwa ◽  
...  

2012 ◽  
Vol 35 (5) ◽  
pp. 1077-1088 ◽  
Author(s):  
Diane M. Renz ◽  
Joachim Böttcher ◽  
Felix Diekmann ◽  
Alexander Poellinger ◽  
Martin H. Maurer ◽  
...  

2021 ◽  
Vol 15 (Supplement_1) ◽  
pp. S008-S009
Author(s):  
B Verstockt ◽  
C Jorissen ◽  
E Hoefkens ◽  
N Lembrechts ◽  
L Pouillon ◽  
...  

Abstract Background Treating beyond endoscopic remission, aiming for histological remission, has shown to reduce relapse and hospitalization rates in patients with ulcerative colitis (UC). However, very little is known on how histological remission associates with patient reported outcomes (PROMs). Methods PROMs (Simple clinical colitis activity index [SCCAI], IBD disk and Visual Analogue Scales [VAS]) were prospectively collected through a digital questionnaire in all patients with UC undergoing colonoscopy between July 21st 2020-Jan 21st 2021. Mayo endoscopic sub score and UCEIS were determined, as well as the Nancy histologic index (NHI) of the most affected area. Endoscopic remission was defined as Mayo endoscopic sub score 0 and UCEIS 0; histologic remission as NHI 0, absence of active inflammation as NHI ≤ 1. PRO2 remission was defined as stool frequency ≤ 1 (absolute stool frequency ≤ 3 OR 1–2 stools more than usual) and rectal bleeding score of 0. Results Fifty-six paired assessments were collected in 48 unique patients (Table 1), with a histologic, endoscopic and PRO-2 remission rate of 23.2%, 28.6% and 38.2% respectively. Patients with histologic remission or absence of histologic inflammation had a significantly lower overall IBD disability (p=0.007, p=0.003) and disease activity score (p=0.003, p<0.001), as compared to patients without. In line, NHI correlated with the overall IBD disk (r=0.40, p=0.002) and SCCAI score (r=0.50, p<0.001). Many individual components of both scores (abdominal pain, arthralgia, impact on education and work/interpersonal interactions/sexual function, regulation of defecation, blood loss, general wellbeing, joint pain, numbers of stools during night/day, urgency) differed significantly between patients with and without histologic remission. VAS scores assessing general wellbeing (r=0.33, p=0.01), impact on daily activities (r=0.41, p=0.002), UC-related symptoms (r=0.42, p=0.001) and worries (r=0.40, p=0.002) correlated with histology. Quartile analysis of the overall IBD disk and SCCAI scores confirmed the highest likelihood for histologic remission in patients with the lowest scores (Q1-Q2 vs Q3-Q4 39.3% vs 7.1%, p=0.01; 40.0% vs 9.7%, p=0.01) (Figure 1). Nevertheless, the overall accuracy of the IBD disk (0.75) or SCCAI score (0.76) for histologic remission is lower (p<0.05) than the accuracy of the Mayo endoscopic (0.90) or UCEIS (0.90) score. Table 1: Baseline features Abstract OP09 – Figure 1: Quartile analysis Conclusion In patients with UC, PROMs for disability and clinical disease activity reflect histologic disease activity and should therefore be further explored in (trial) endpoint discussions. However, they cannot fully replace endoscopic and histologic findings, and should be considered complementary.


2021 ◽  
Author(s):  
Ayumi Koyama ◽  
Dai Miyazaki ◽  
Yuji Nakagawa ◽  
Yuji Ayatsuka ◽  
Hitomi Miyake ◽  
...  

Abstract Corneal opacities are an important cause of blindness, and its major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the “face” of an infected cornea, we have adapted a deep learning architecture used for facial recognition and applied it to determine a probability score for a specific pathogen causing keratitis. To record the diverse features and mitigate the uncertainty, batches of probability scores of 4 serial images taken from many angles or fluorescence staining were learned for score and decision level fusion using a gradient boosting decision tree. A total of 4306 slit-lamp images and 312 images obtained by internet publications on keratitis by bacteria, fungi, acanthamoeba, and herpes simplex virus (HSV) were studied. The created algorithm had a high overall accuracy of diagnosis, e.g., the accuracy/area under the curve (AUC) for acanthamoeba was 97.9%/0.995, bacteria was 90.7%/0.963, fungi was 95.0%/0.975, and HSV was 92.3%/0.946, by group K-fold validation, and it was robust to even the low resolution web images. We suggest that our hybrid deep learning-based algorithm be used as a simple and accurate method for computer-assisted diagnosis of infectious keratitis.


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