Diagnosis and Characterization of Non-Alcoholic Fatty Liver Disease Based on Artificial Intelligence.
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 14,046
- 主要终点
- Sensitivity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score
研究概览
简要总结
A key element in the diagnosis of non-alcoholic fatty liver disease (NAFLD) is the differentiation of non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver (NAFL) and the staging of the liver fibrosis, given that patients with NASH and advanced fibrosis are those at greatest risk of developing hepatic complications and cardiovascular disease. There are still no available non-invasive methods that allow for correct diagnosis and staging of NAFLD. The implementation of Artificial Intelligence (AI) techniques based on artificial neural networks and deep learning systems (Deep Learning System) as a tool for medical diagnoses represents a bona fide technological revolution that introduces an innovative approach to improving health processes.
详细描述
The objectives of this observational study are the following:
- To design a predictive model of significant liver disease due to NAFLD, based on clustering or clustering algorithms (AI)
- To apply and validate this model to classify patients according to the severity of the disease in such a manner as to provide more effective management of these patients from Primary Care to Hospital Care through process and resource optimization
- To develop a Deep Learning System based on convolutional neuronal networks for automatic recognition of images in a cohort of subjects with digitized liver biopsies, and to undertake pairwise analysis that allows for correct and exact classification of biopsies from subjects with NASH.
Design:
An observational study of the determination and validation of diagnostic predictive models of NAFLD.
The study has four phases:
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 19 Years 至 74 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Subjects aged 19-74 belonging to the ETHON cohort or registered in the Hepamet Spanish registry of NAFLD or the European NAFLD registry
排除标准
- •Subjects that not fulfill the inclusion criteria and those who did not sign informed consent to participate in the ETHON cohort or to be registered in the mentioned registers.
结局指标
主要结局
Sensitivity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score
时间窗: From october of 2019 to march of 2021
Specificity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score
时间窗: From october of 2019 to march of 2021
Number of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms
时间窗: From october of 2019 to march of 2021
Percentage of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms
时间窗: From october of 2019 to march of 2021
Negative predictive Value in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score.
时间窗: From october of 2019 to march of 2021
Kappa coefficient of concordance about NASH diagnosis between AI algorithms and histologic diagnosis.
时间窗: From october of 2019 to march of 2021
Kappa coefficient of concordance about NASH diagnosis between AI algorithms and the Hepamet non-invasive score.
时间窗: From october of 2019 to march of 2021
ROC curve at various threshold settings obtained through the algorithms for NASH diagnosis and staging
时间窗: From october of 2019 to march of 2021
Positive predictive value in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score.
时间窗: From october of 2019 to march of 2021
次要结局
未报告次要终点