High Accuracy in Classifying Endoscopic Severity in Ulcerative Colitis Using Convolutional Neural Network

医学 卷积神经网络 人工智能 溃疡性结肠炎 接收机工作特性 二元分类 限制 模式识别(心理学) 可靠性(半导体) 深度学习 人工神经网络 疾病 机器学习 病理 计算机科学 内科学 机械工程 功率(物理) 物理 量子力学 支持向量机 工程类
作者
Bobby Lo,Zhuoyuan Liu,Flemming Bendtsen,Christian Igel,Ida Vind,Johan Burisch
出处
期刊:The American Journal of Gastroenterology [Lippincott Williams & Wilkins]
卷期号:117 (10): 1648-1654 被引量:17
标识
DOI:10.14309/ajg.0000000000001904
摘要

The evaluation of endoscopic disease severity is a crucial component in managing patients with ulcerative colitis (UC). However, endoscopic assessment suffers from substantial intraobserver and interobserver variations, limiting the reliability of individual assessments. Therefore, we aimed to develop a deep learning model capable of distinguishing active from healed mucosa and differentiating between different endoscopic disease severity degrees.One thousand four hundred eighty-four unique endoscopic images from 467 patients were extracted for classification. Two experts classified all images independently of one another according to the Mayo endoscopic subscore (MES). In cases of disagreement, a third expert classified the images. Different convolutional neural networks were considered for automatically classifying UC severity. Five-fold cross-validation was used to develop and select the final model. Afterward, unseen test data sets were used for model evaluation.In the most challenging task-distinguishing between all categories of MES-our final model achieved a test accuracy of 0.84. When evaluating this model on the binary tasks of distinguishing MES 0 vs 1-3 and 0-1 vs 2-3, it achieved accuracies of 0.94 and 0.93 and areas under the receiver operating characteristic curves of 0.997 and 0.998, respectively.We have developed a highly accurate, new, automated way of evaluating endoscopic images from patients with UC. We have demonstrated how our deep learning model is capable of distinguishing between all 4 MES levels of activity. This new automated approach may optimize and standardize the evaluation of disease severity measured by the MES across centers no matter the level of medical expertise.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
孤岛发布了新的文献求助10
2秒前
认真念梦发布了新的文献求助10
2秒前
Zhy发布了新的文献求助10
5秒前
5秒前
星辰大海应助Dc采纳,获得30
7秒前
科研通AI2S应助孤岛采纳,获得10
7秒前
8秒前
replay驳回了乐乐应助
8秒前
机灵的友儿完成签到,获得积分10
9秒前
yzhwzh完成签到,获得积分10
10秒前
Wayne72完成签到,获得积分0
10秒前
文二目分完成签到 ,获得积分10
11秒前
11秒前
12秒前
JamesPei应助谈舒怡采纳,获得10
12秒前
东东发布了新的文献求助10
12秒前
14秒前
ller发布了新的文献求助10
14秒前
廖廖发布了新的文献求助20
15秒前
小小付发布了新的文献求助10
17秒前
67777发布了新的文献求助10
18秒前
18秒前
21秒前
21秒前
21秒前
LQX2141发布了新的文献求助10
22秒前
SYLH应助guozizi采纳,获得30
22秒前
GlockieZhao完成签到,获得积分10
23秒前
所所应助67777采纳,获得10
25秒前
25秒前
王染墨发布了新的文献求助10
26秒前
BPM发布了新的文献求助10
26秒前
27秒前
27秒前
英俊的铭应助晚来天欲雪采纳,获得10
27秒前
小小付完成签到,获得积分20
28秒前
29秒前
量子星尘发布了新的文献求助10
30秒前
30秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3976177
求助须知:如何正确求助?哪些是违规求助? 3520366
关于积分的说明 11202745
捐赠科研通 3256847
什么是DOI,文献DOI怎么找? 1798509
邀请新用户注册赠送积分活动 877704
科研通“疑难数据库(出版商)”最低求助积分说明 806516