Smartphone-Based Proactive Self-Screening for Ocular Surface Malignancies

Importance: Ocular surface malignancies pose risks to vision and survival yet are frequently misdiagnosed as benign lesions because of their subtle presentation and the lack of widely accessible screening tools, potentially resulting in treatment delays and the need for extensive surgical intervention. Objective: To develop and validate a smartphone-based, media-facilitated artificial intelligence (AI) system for proactive self-screening of ocular surface malignancies in the general population. Design, Setting, and Participants: A nonrandomized clinical trial was conducted across China from December 2022 to June 2023. A deep learning model was initially trained and validated using 12 years of multicenter slitlamp images. The system was then optimized for smartphone-based photography and deployed through a widely disseminated mobile application. Data analysis was performed from July 2023 to June 2024. Intervention: Participants used the CaptureTumor standardized smartphone application, incorporating real-time AI-guided photography instructions, to capture images of suspected lesions. The application provided immediate binary (benign vs malignant) and multiclass risk stratification and triaged high-risk cases for expedited clinical referral. Main Outcomes and Measures: The primary outcome was area under the receiver operating characteristic curve (AUC) for differentiating malignant from benign lesions. Secondary outcomes included sensitivity, specificity, and the number of histopathologically confirmed malignancies detected. Results: Multimedia outreach via television, social media, and internet hospitals reached 256 053 individuals, with 614 completing at-home self-screening through the app. Of these participants, the median (IQR) age was 46 (11) years (range, 4-87 years); 301 (49%) were female and 313 (51%) male. After optimizing the image quality, the smartphone-based CaptureTumor achieved an AUC of 0.905 (95% CI, 0.837-0.973), comparable with the performance of the slitlamp-based model (AUC = 0.945; 95% CI, 0.918-0.972). During real-world screening, 20 malignancies were pathologically confirmed among the 614 participants, with 19 of 20 participants (95%) newly diagnosed, and no cases requiring enucleation. At the population level, CaptureTumor demonstrated an AUC of 0.977 (95% CI, 0.964-0.990), a sensitivity of 89.3% (95% CI, 86.7%-91.9%), and a specificity of 95.9% (95% CI, 94.2%-97.6%). Conclusions and Relevance: This trial found that the integration of smartphone-enabled imaging, AI-driven diagnostics, and targeted media outreach established a scalable, accessible, and potentially clinically effective strategy for population-level screening of rare ocular malignancies. This closed-loop mobile health model potentially addresses gaps in early detection and equitable care delivery for vision- and life-threatening rare diseases. Trial Registration: ClinicalTrials.gov Identifier: NCT05645341.