Conjunctival Neoplasms, Eye Neoplasms, Orbital Neoplasms
Conditions
Keywords
Mobile health, Artificaial intelligence medicine, Ocular surface malignant tumor
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.
Interventions
DIAGNOSTIC_TESTscreening 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.
Sponsors
Sun Yat-sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area under the curve (AUC) | 2024.1 | Measure of the ability of a binary classifier to distinguish between malignent and benign. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity, specificity and accuracy | 2024.1 | The study will assess the sensitivity and specificity of the CaptureTumor (CaT) system under various conditions. |
| Screening coverage | 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. |
| Referral efficiency | 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. |
| Human-machine collaboration performance | 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. |
Countries
China
Outcome results
None listed