Development and professional validation of an App to support Oral Cancer Screening

Area of Science:

• Oncology
• Medical Informatics
• Public Health

Background:

• Oral cancer remains a significant global health concern.
• Early detection is crucial for improving patient outcomes and survival rates.
• Identifying individuals at high risk can facilitate timely intervention.

Purpose of the Study:

• To develop and validate a mobile application for identifying oral cancer risk factors.
• To create an algorithm within the app to estimate oral cancer development risk.
• To assess the usability and acceptability of the developed application.

Main Methods:

• Developed the Oral Cancer Screening (OCS) app incorporating oral cancer predictors and risk estimation algorithm.
• Validated the algorithm using simulated clinical cases with 40 oral diagnostic professionals.
• Assessed app usability (System Usability Score) and acceptability (Technology Acceptance Model).
• Statistical analyses included Cronbach's alpha, Friedman/Dunn tests, Spearman correlation, ROC curve, Chi-square, and Fisher's exact tests.

Main Results:

• The OCS app achieved high usability (84.63±10.66) and acceptability (84.75±10.62) scores among professionals.
• Factors like clinical dentistry practice and prior risk factor orientation enhanced acceptability.
• Higher education roles positively impacted app usability.
• The validated algorithm demonstrated 84.8% sensitivity and 58.4% specificity for high-risk detection.

Conclusions:

• The Oral Cancer Screening (OCS) application is an effective tool for identifying oral cancer risk factors.
• The app possesses adequate sensitivity, usability, and acceptability for clinical use.
• OCS has the potential to aid in the early detection of oral lesions, contributing to better patient management.