Investigate the Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiographs and to Compare Its Performance With Certified Radiologists
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 1,881
- 主要终点
- the diagnosis of pneumoconiosis
研究概览
简要总结
Pneumoconiosis is relatively prevalent in low/middle-income countries, and it remains a challenging task to accurately and reliably diagnose pneumoconiosis. The investigators implemented a deep learning solution and clarified the potential of deep learning in pneumoconiosis diagnosis by comparing its performance with two certified radiologists. The deep learning demonstrated a unique potential in classifying pneumoconiosis.
详细描述
The investigators retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, the investigators applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC).
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •industrial workers with a history of exposure to dust and underwent DR screening of pneumoconiosis from 2015 to 2018
排除标准
- •patients with poor image quality
- •patients with incomplete clinical data
结局指标
主要结局
the diagnosis of pneumoconiosis
时间窗: up to 6 months
The diagnosis and staging of pneumoconiosis were made by an expert panel consisting of certified radiologists and occupational physicians. The diagnosis of pneumoconiosis was confirmed by medical history and previous medical records(chest X-rays and pulmonary function testing).
次要结局
未报告次要终点