Performance Evaluation of Artificial Intelligence Assisted Diabetic Retinopathy Grading in the Leuven University Hospital: Can Technology Improve the Resident?
试验速览
- 阶段
- 不适用
- 入组人数
- 139
- 主要终点
- does AI augment diagnostic performance of resident
研究概览
简要总结
To evaluate the added value of the use of artificial intelligence in the diagnosis of referable diabetic retinopathy in a teaching hospital setting
详细描述
Diabetes mellitus is one of the major health challenges of our era. It is estimated that 642 million people will be diagnosed with this disease worldwide by 2040. Diabetes is a disease effecting the entire body and comes with many possible complications due to its' effect on the microvasculature. The most prevalent of these complications is diabetic retinopathy which is caused by both microvascular and neural damage.
According to studies in the United States by the Eye Diseases Prevalence Research group, about 40% of patients present with some degree of retinopathy. 8% of patients even have vision-threatening diabetic retinopathy.
Diabetic retinopathy is one of the main causes of blindness in our current society. However annual screening and timely referral for treatment can prevent this from occurring. The best illustration is the fact that since the implementation of a nationwide screening program, diabetes mellitus is no longer the leading cause of blindness in the UK.
Therefore, many countries have organized some sort of screening program. However, there are big organizational differences between countries. This can range from an annual dilated fundoscopy by an ophthalmologist (as is the case in Belgium) to non-mydriatic fundus photographs evaluated by a trained grader who is not a (para)medic.
Even with the most efficient screening pathway possible, the increase of patient numbers will become a problem since the human factor in the screening pathway (doctor, optometrist, trained grader,...) cannot increase its' capacity with the same speed. The current system will reach its limits at one point or another. Furthermore, it is known that a significant proportion of diabetes patients do not comply with the recommended annual screening. These problems will result in longer waiting lists, underdiagnosis because of overworked doctors, long waiting lists and possibly lack of high quality care.
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 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •- Diagnosis of diabetes mellitus
- •Age > 18 years old
- •Patient is capable of giving informed consent
- •Fluent in written and oral Dutch, or interpreter present
排除标准
- •- History of treatment for diabetic retinopathy or diabetic macular edema (laser or intravitreal injections)
- •Participant is contraindicated for imaging by fundus imaging systems used in the study
结局指标
主要结局
does AI augment diagnostic performance of resident
时间窗: 4 months
sensitivity and specificity
次要结局
- does physical consultation augment diagnostic performance of AI(6 months)
研究者
JulieJacob
prof.de. Julie Jacob
Universitaire Ziekenhuizen KU Leuven
