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临床试验/NCT05645341
NCT05645341已完成不适用

Artificial Intelligence-assisted Screening of Malignant Pigmented Tumors on the Ocular Surface

Sun Yat-sen University1 个研究点 分布在 1 个国家目标入组 535 人开始时间: 2022年12月5日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
发起方
入组人数
535
试验地点
1
主要终点
Area under the curve (AUC)

研究概览

简要总结

Rare diseases generally refer to diseases whose prevalence rate is lower than 1 / 10 000 and the number of patients is less than 140000. Rare diseases are generally faced with the dilemma of a lack of qualified doctors, difficulty in large-scale screening, and a lack of rapid and effective channels for medical treatment. Studies have shown that 42% of patients say they have been misdiagnosed, and each patient with a rare disease needs to go through an average of eight doctors in seven years to see a corresponding rare disease specialist. More importantly, most rare diseases seriously affect the health and quality of life of patients. The ocular surface malignant tumor is a typical rare disease, and its incidence is less than 1 / 100000. The ocular surface not only affects the patient's appearance, but also damages the visual function, and the malignant tumor may even affect life. These uncommon malignant tumors are often hidden in the common black nevus on the eye surface, which is easy to be ignored and has great potential risks. With the improvement of people's living standards, people start to pay attention to rare diseases.

In recent years, the rapid development of digital technology has also provided new opportunities for the prevention and treatment of rare diseases. Our team established the database of rare ophthalmopathy in China in the early stage, which provided a solid foundation for the digitization of precious clinical data. This study intends to develop an intelligent screening system for ocular surface malignant tumors, using the mobile phone for real-world verification and scale screening, and explore it to improve the ability of doctors to diagnose and treat rare diseases. This study is expected to improve the ability to screen malignant tumors on the ocular surface and provide a novel model for the universal screening of rare diseases.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Prospective

入排标准

性别
All
接受健康志愿者

入选标准

  • Dark-brown lesions on the ocular surface are found: i.e. ocular surface malignant melanoma, ocular basal cell carcinoma, conjunctival nevus, eyelid nevus, sclera pigmentation, benign eyelid keratosis

排除标准

  • Non-pigmented ocular surface tumors: pterygium, corneal dermoid tumor, meibomian gland cyst, cataract, blepharitis, etc.
  • The image quality does not meet the clinical requirements.

结局指标

主要结局

Area under the curve (AUC)

时间窗: 2024.1

Measure of the ability of a binary classifier to distinguish between malignent and benign.

次要结局

  • Screening coverage(2024.1)
  • Sensitivity, specificity and accuracy(2024.1)
  • Referral efficiency(2024.1)
  • Human-machine collaboration performance(2024.1)

研究者

发起方
Sun Yat-sen University
申办方类型
Other
责任方
Principal Investigator
主要研究者

Haotian Lin

Clinical Professor

Sun Yat-sen University

研究点 (1)

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