Validation of the Utility of an Artificial System for the Large-scale Screening of Scoliosis Using Back Images
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 500
- 试验地点
- 1
- 主要终点
- The proportion of accurate, mistaken and miss detection of the intelligent visual acuity diagnostic system.
研究概览
简要总结
Traditional school scoliosis screening approaches remains debatable due to unnecessary referal and excessive cost. Deep learning algorithms have proven to be powerful tools for the detection of multiple diseases; however, the application of such methods in scoliosis screening requires further assessment and validation. Here, the investigators develop an artificial system for the automated screening of scoliosis using disrobed back images, and conduct clinical trial to validate if the diagnostic system can offsetting the shortcomings of human doctors.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 10 Years 至 22 Years（Child, Adult）
- 接受健康志愿者
- 是
入选标准
- •1.Patients included both pretreatment back photos and whole spine (C7-S1) standing X-ray or ultrasound images (for healthy population);
- •All the documents are clear to be recognized by naked eyes;
- •Back photos and are taken at the same time (not >1month); 4.Patients were consider as idiopathic scoliosis according to clinical photos.
排除标准
- •Patients were considered as non-idiopathic scoliosis for obvious abnormal features of trunck,such as Cafe-au-Lait spots for neurofibromatosis, Spider finger, Abnormal hair spot of back, pelvic tilt, lower limb discrepancy and so on; 2.The taken time between back photo and X-ray or ultrasound was more than 1month; 3.The clinical photos and images were not clear;
- •The X-ray film or ultrasound images not including whole spine (C7-S1).
研究组 & 干预措施
Eligible patients for AI test.
Device: An artificial system for the screening of scoliosis
干预措施: An artificial system for the screening of scoliosis (Device)
结局指标
主要结局
The proportion of accurate, mistaken and miss detection of the intelligent visual acuity diagnostic system.
时间窗: Up to 5 years
次要结局
未报告次要终点
研究者
Haotian Lin
Clinical Professor
Sun Yat-sen University