JMIR mHealth and uHealth · 2026
Mobile AI-Assisted Oral Lesion Triage for Community Oral Cancer Screening: Prospective Field Evaluation Study.
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Liu Hao-Yun, Huang Yu-Cheng, Chen Si-Wei, Wei Ling-Ying, Chou Cheng-Ying, Lee Jang-Jaer, Chen Shyh-Jye, Lee Chien-Chang
Аннотация
BACKGROUND: AI-based tools for oral cancer screening have shown promising performance in curated image datasets, but their performance in real-world mobile community screening workflows remains less clear.
OBJECTIVE: This study evaluated the prospective field performance of a parameter-locked, mobile AI-assisted oral lesion triage system embedded in a human-supervised community oral cancer screening workflow.
METHODS: We conducted a prospective community-based field evaluation in eastern Taiwan from June 17 to December 31, 2025. Trained nonspecialist personnel acquired standard white-light intraoral images using handheld mobile devices during routine screening. The AI system incorporated on-site image quality assessment and lesion-level risk stratification into green, yellow, and red triage categories. Technically inadequate images after repeated acquisition attempts were classified as ungradable and excluded from performance analysis. Three board-certified oral and maxillofacial specialists established an operational clinical reference standard through structured image review and consensus adjudication. The primary outcome was lesion-level identification of high-risk lesions requiring specialist referral, defined as red versus nonred triage. Diagnostic performance was estimated with Wilson 95% CIs; precision-recall performance and preconsensus specialist agreement were also assessed.
RESULTS: Among 602 screened participants, 4283 interpretable intraoral images were included in expert adjudication and lesion-level analysis. The AI system flagged 68 red lesions; 12 (17.6%) were confirmed as high risk, and 4 additional high-risk lesions were identified during expert review, yielding 16 high-risk events for the primary red-triage analysis. For high-risk lesion identification, sensitivity was 75% (12/16; 95% CI 50.5%-89.8%) and positive predictive value was 17.6% (12/68; 95% CI 10.4%-28.4%); specificity, negative predictive value, and accuracy were 98.6% (3928/3984), 99.9% (3928/3932), and 98.5% (3940/4000), respectively. Area under the receiver operating characteristic curve values were 0.957 for red, 0.931 for yellow, and 0.964 for green triage; precision-recall analysis showed area under the precision-recall curve values of 0.8235, 0.9052, and 0.9476, respectively. Preconsensus specialist agreement was high, with a Fleiss κ of 0.921 and a Gwet AC1 of 0.981.
CONCLUSIONS: This mobile AI-assisted oral lesion triage system was feasibly integrated into a real-world community screening workflow operated by trained nonspecialist personnel. In this low-prevalence field setting, red triage showed moderate sensitivity and modest positive predictive value, supporting its use as human-supervised referral-prioritization support rather than autonomous diagnosis. Further multicenter studies with blinded assessment, participant-level outcomes, workflow-efficiency measures, and longitudinal follow-up are needed before clinical impact and scalability can be determined.
OBJECTIVE: This study evaluated the prospective field performance of a parameter-locked, mobile AI-assisted oral lesion triage system embedded in a human-supervised community oral cancer screening workflow.
METHODS: We conducted a prospective community-based field evaluation in eastern Taiwan from June 17 to December 31, 2025. Trained nonspecialist personnel acquired standard white-light intraoral images using handheld mobile devices during routine screening. The AI system incorporated on-site image quality assessment and lesion-level risk stratification into green, yellow, and red triage categories. Technically inadequate images after repeated acquisition attempts were classified as ungradable and excluded from performance analysis. Three board-certified oral and maxillofacial specialists established an operational clinical reference standard through structured image review and consensus adjudication. The primary outcome was lesion-level identification of high-risk lesions requiring specialist referral, defined as red versus nonred triage. Diagnostic performance was estimated with Wilson 95% CIs; precision-recall performance and preconsensus specialist agreement were also assessed.
RESULTS: Among 602 screened participants, 4283 interpretable intraoral images were included in expert adjudication and lesion-level analysis. The AI system flagged 68 red lesions; 12 (17.6%) were confirmed as high risk, and 4 additional high-risk lesions were identified during expert review, yielding 16 high-risk events for the primary red-triage analysis. For high-risk lesion identification, sensitivity was 75% (12/16; 95% CI 50.5%-89.8%) and positive predictive value was 17.6% (12/68; 95% CI 10.4%-28.4%); specificity, negative predictive value, and accuracy were 98.6% (3928/3984), 99.9% (3928/3932), and 98.5% (3940/4000), respectively. Area under the receiver operating characteristic curve values were 0.957 for red, 0.931 for yellow, and 0.964 for green triage; precision-recall analysis showed area under the precision-recall curve values of 0.8235, 0.9052, and 0.9476, respectively. Preconsensus specialist agreement was high, with a Fleiss κ of 0.921 and a Gwet AC1 of 0.981.
CONCLUSIONS: This mobile AI-assisted oral lesion triage system was feasibly integrated into a real-world community screening workflow operated by trained nonspecialist personnel. In this low-prevalence field setting, red triage showed moderate sensitivity and modest positive predictive value, supporting its use as human-supervised referral-prioritization support rather than autonomous diagnosis. Further multicenter studies with blinded assessment, participant-level outcomes, workflow-efficiency measures, and longitudinal follow-up are needed before clinical impact and scalability can be determined.