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

NCT05645341 | Completed DMC

• 1 other identifier: 2022KYPJ067
• Study Type: observational
• Target: 535
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Orbital Neoplasms; Conjunctival Neoplasms; Eye Neoplasms

Keywords

Mobile health; Artificaial intelligence medicine; Ocular surface malignant tumor

Outcome Measures

Primary Outcomes (1)

• Area under the curve (AUC)

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

  2024.1

Secondary Outcomes (4)

• Sensitivity, specificity and accuracy

  2024.1

• Screening coverage

  2024.1

• Referral efficiency

  2024.1

• Human-machine collaboration performance

  2024.1

Study Arms (1)

Eligible participants for smartphone-based ocular surface tumors diagnosis

Diagnostic Test: screening system for ocular surface malignant tumors

Interventions

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.

Eligible participants for smartphone-based ocular surface tumors diagnosis

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Through the offline specialist clinics, online popular science, news reports, and other channels, we will promote and inform the crowd about the relevant knowledge of pigmented tumors on the ocular surface, so that they can judge by themselves that they are eligible to join the group, and freely decide whether to participate in this study or not.

You may qualify if:

• 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

You may not qualify if:

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

Sponsors & Collaborators

• Sun Yat-sen University: lead

Study Sites (1)

Zhognshan Ophthalmic Center, Sun Yat-sen University

Guangzhou, Guangdong, 510060, China

Location

MeSH Terms

Conditions

Orbital Neoplasms; Conjunctival Neoplasms; Eye Neoplasms

Condition Hierarchy (Ancestors)

Skull Neoplasms; Bone Neoplasms; Neoplasms by Site; Neoplasms; Bone Diseases; Musculoskeletal Diseases; Eye Diseases; Orbital Diseases; Conjunctival Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: December 2, 2022
• First Posted: December 9, 2022
• Study Start: December 5, 2022
• Primary Completion: June 5, 2023
• Study Completion: June 5, 2023
• Last Updated: August 5, 2024

Record last verified: 2024-08

Locations

CN (1)