A Machine Learning Approach to Infantile Spasms Recognition in Video Recordings
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 61
- 试验地点
- 2
- 主要终点
- Model Negative Predictive Value
研究概览
简要总结
Infantile spasms are a type of seizure linked to developmental issues. Unfortunately, they are often misdiagnosed, causing delays in treatment. The purpose of this study is to develop a computer program that can reliably differentiate infantile spasms from similar, yet benign movements in videos. This computer program will learn from videos taken by parents of study participants. Quickly recognizing and treating infantile spasms is crucial for ensuring the best developmental outcomes.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
入排标准
- 年龄范围
- — 至 2 Years（Child）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Participant age less than 24 months
- •Participant evaluated in the Johns Hopkins Outpatient Center, Johns Hopkins Pediatric Emergency Department or Johns Hopkins Inpatient Units due to spells of abnormal movement or seizure
- •Participant evaluated by a pediatric neurologist during the outpatient or inpatient visit at Johns Hopkins Hospital
- •At least one video recording of the spell of abnormal movement produced by the parent/guardian available for provider review
排除标准
- •Poor video recording quality
- •Entire patient is not in frame
结局指标
主要结局
Model Negative Predictive Value
时间窗: 2 years
Proportion of negative classifications which were correct in the test dataset.
Model Sensitivity (Recall)
时间窗: 2 years
Proportion of true positives which the model classified correctly in the test dataset.
Model Positive Predictive Value (Precision)
时间窗: 2 years
Proportion of positive classifications which were correct in the test dataset.
Model Specificity
时间窗: 2 years
Proportion of true negatives which the model classified correctly in the test dataset.
次要结局
未报告次要终点