Multimodal Imaging in Vitreo-retinal Surgery and Macular Dystrophies: Biomarkers of Morpho-Functional Recovery by Artificial Intelligence
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- Predictivity of morphological-functional radiomic data
研究概览
简要总结
The aim of the study is to identify morphological and functional biomarkers of post-operative recovery after vitreoretinal surgery, using decisional support systems (DSS), based on multimodal big-data analysis by means of machine learning techniques in daily clinical practice
详细描述
The aim of the study is to identify morphological and functional biomarkers of post-operative recovery after vitreoretinal surgery. Identifying the biomarkers and assessing the predictivity of recovery will make it possible to highlight the categories of patients who can benefit most from surgical treatment, and to target the patient more precisely for personalised medicine and surgery. The introduction of new decisional support systems (DSS), based on multimodal big-data analysis through machine learning techniques in daily clinical practice, is providing new useful information in patient assessment for personalised surgery.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Months 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •All patients to undergo vitreo-retinal surgery for:
- •Macular hole
- •Epiretinal membranes
- •Retinal detachment
- •Macular dystrophies (retinal pre-prosthesis)
排除标准
- •Patients under 18 years of age will be excluded; patients in whom morphological examinations cannot be performed due to poor cooperation or opacity of the dioptric media (e.g. corneal pathology). Quality of morphological images inadequate for post acquisition processing (<6/10).
结局指标
主要结局
Predictivity of morphological-functional radiomic data
时间窗: 3 years
Rate of predictivity of morphological-functional radiomic data to establish the grade of recovery in the post-operative period by means of an artificial intelligence (AI) machine learning model.
次要结局
- Correlating with the age of patients(3 years)
- Correlate with age of onset of disease(3 years)
- Identify predictive differences according to diagnosis(3 years)
研究者
RIZZO STANISLAO
Professor
Fondazione Policlinico Universitario Agostino Gemelli IRCCS