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Completed

NCT Number: NCT05645341

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

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.

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Key information

Sex eligibility

All sexes

Study type

Observational

Primary location

Zhognshan Ophthalmic Center, Sun Yat-sen University

Guangzhou, Guangdong, 510060, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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

Exclusion criteria

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

Treatment and study plan

screening system for ocular surface malignant tumors

Diagnostic Test

Develop an intelligent screening system for ocular surface malignant tumors, apply it to the mobile terminal for real-world verification and large-scale general screening, and test its effect on assisting doctors in the diagnosis and treatment of rare diseases.

Primary outcomes

  1. Area under the curve (AUC)

    Time frame: 2024.1

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

Secondary outcomes

  1. Sensitivity, specificity and accuracy

    Time frame: 2024.1

    The study will assess the sensitivity and specificity of the CaptureTumor (CaT) system under various conditions.

  2. Screening coverage

    Time frame: 2024.1

    Count the number of people who have successfully received and read knowledge about ocular surface pigmented tumors on each offline and online platform.

  3. Referral efficiency

    Time frame: 2024.1

    For cases where the system judges that it is necessary to go to the hospital for further diagnosis and treatment, two or more researchers will conduct a diagnostic review first. If further diagnosis and treatment is really needed, the subject will be contacted and told to go to the hospital for treatment by phone, text message, etc., and continue to follow up. Finally, the duration of diagnosis (screening time to pathological diagnosis time), visit distance, number of visits before diagnosis, and the proportion of referred patients in all subjects were counted.

  4. Human-machine collaboration performance

    Time frame: 2024.1

    Doctors with different seniority were asked to diagnose the test set with and without assistance from the intelligent screening system, and the accuracy in the two cases were calculated and compared.

Sponsors and collaborators

Lead sponsor

Sun Yat-sen University

Other

Registry information

Important dates

Study start
2022
Primary completion
2023
Study completion
2023
First posted
Dec 9, 2022
Registry last updated
Aug 5, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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