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 工程类 物理 支持向量机 功率(物理) 机械工程 量子力学
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
Singularity举报debuffv求助涉嫌违规
刚刚
852应助含蓄绿兰采纳,获得10
刚刚
Sherry完成签到,获得积分10
刚刚
隐形曼青应助平常的念柏采纳,获得10
1秒前
ppc524发布了新的文献求助50
2秒前
huajianjiuxing完成签到,获得积分10
2秒前
2秒前
在水一方应助巫凝天采纳,获得30
2秒前
善学以致用应助就这样采纳,获得10
2秒前
2秒前
3秒前
爆米花应助dudu10000采纳,获得10
3秒前
麦田的守望者完成签到,获得积分10
3秒前
llyy123完成签到,获得积分10
3秒前
123发布了新的文献求助10
4秒前
5秒前
123发布了新的文献求助10
5秒前
5秒前
5秒前
5秒前
SciGPT应助meng采纳,获得10
6秒前
狂野友梅完成签到,获得积分10
6秒前
6秒前
文艺寄灵完成签到,获得积分10
6秒前
6秒前
听风者发布了新的文献求助10
7秒前
欣喜的成败完成签到,获得积分20
7秒前
panpan111完成签到,获得积分10
8秒前
TL完成签到,获得积分10
8秒前
wu完成签到 ,获得积分10
8秒前
8秒前
我想@科研发布了新的文献求助10
9秒前
9秒前
过pass发布了新的文献求助10
9秒前
zy发布了新的文献求助10
9秒前
10秒前
Vivian完成签到,获得积分10
10秒前
倩迷谜发布了新的文献求助10
11秒前
小荇发布了新的文献求助10
12秒前
小二郎应助ShangNiNE采纳,获得10
12秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 360
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 3970683
求助须知:如何正确求助?哪些是违规求助? 3515337
关于积分的说明 11178055
捐赠科研通 3250580
什么是DOI,文献DOI怎么找? 1795357
邀请新用户注册赠送积分活动 875790
科研通“疑难数据库(出版商)”最低求助积分说明 805166