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.

Study Overview

• Status: Recruiting
• Conditions: Conjunctival Neoplasms; Eye Neoplasms; Orbital Neoplasms
• Intervention / Treatment: Diagnostic test: screening system for ocular surface malignant tumors

• Study Type: Observational
• Enrollment (Estimated): 400

Contacts and Locations

• Study Contact: Name: Haotian Lin, M.D., Ph.D; Phone Number: 8613802793086; Email: haot.lin@hotmail.com
• Study Contact Backup: Name: Ruixin Wang, M.D., Ph.D; Phone Number: 8615360458084; Email: ruiruiw413@aliyun.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510060
      • Recruiting
      • Zhognshan Ophthalmic Center, Sun Yat-sen University
      • Contact: Haotian Lin, M.D., Ph.D; Phone Number: 8613802793086; Email: haot.lin@hotmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Eligible participants for smartphone-based ocular surface tumors diagnosis	Diagnostic test: screening system for ocular surface malignant tumors; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Area under the curve (AUC)	Measure of the ability of a binary classifier to distinguish between malignent and benign.	2024.1

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Screening coverage	Count the number of people who have successfully received and read knowledge about ocular surface pigmented tumors on each offline and online platform.	2024.1
Referral efficiency	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.	2024.1
Human-machine collaboration performance	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.	2024.1
Sensitivity, specificity and accuracy	The study will assess the sensitivity and specificity of the CaptureTumor (CaT) system under various conditions.	2024.1

Collaborators and Investigators

• Sponsor: Sun Yat-sen University

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2021
• Primary Completion (Estimated): January 1, 2024
• Study Completion (Estimated): January 1, 2024

Study Registration Dates

• First Submitted: December 2, 2022
• First Submitted That Met QC Criteria: December 2, 2022
• First Posted (Actual): December 9, 2022

Study Record Updates

• Last Update Posted (Actual): October 17, 2023
• Last Update Submitted That Met QC Criteria: October 13, 2023
• Last Verified: October 1, 2023

More Information

Terms related to this study

• Keywords: Mobile health; Artificaial intelligence medicine; Ocular surface malignant tumor
• Additional Relevant MeSH Terms: Neoplasms by Site; Eye Diseases; Musculoskeletal Diseases; Bone Diseases; Orbital Diseases; Conjunctival Diseases; Skull Neoplasms; Bone Neoplasms; Neoplasms; Conjunctival Neoplasms; Eye Neoplasms; Orbital Neoplasms
• Other Study ID Numbers: 2022KYPJ067

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No