Machine Learning–Enabled Smartphone CRISPR-Cas12a Lateral Flow Platform for Sensitive Detection of Circulating HPV DNA

Abstract Persistent infection with high-risk human papillomavirus (HPV) is the primary cause of cervical cancer and other HPV-related malignancies. Effective screening and early detection of HPV, particularly in point-of-care (POC) settings, can reduce disease progression and associated mortality. Although PCR-based assays provide high sensitivity, their dependence on centralized laboratory infrastructure limits accessibility in POC settings. CRISPR-Cas diagnostics enable programmable, isothermal detection of HPV with lateral flow assay (LFA) readouts; however, visual interpretation of faint bands can be subjective and inconsistent. Our objective was to develop a machine learning (ML)-enhanced, smartphone-native CRISPR-LFA platform for highly sensitive and reliable detection of HPV DNA in plasma. A smartphone-based diagnostic system integrating CRISPR-LFA with a ML framework was developed using standardized image acquisition within a light-controlled enclosure. Radiomics-inspired strip features were extracted and analyzed using a multivariable logistic regression model. A total of 150 plasma samples were used for model development and 60 independent samples for validation. An optimized model was developed that had 96.7% sensitivity and 100% specificity for detection of HPV DNA. The smartphone-enabled CRISPR platform demonstrated higher sensitivity than visual interpretation, particularly for faint-band results, and reduced false positives. Validation in the independent cohort confirmed the robustness of the assay. Performance remained stable across smartphone models, lighting conditions, and operators, and on-device inference enabled reliable operation. In sum, the smartphone-integrated CRISPR-LFA platform can facilitate accurate and reliable detection of plasma HPV DNA in POC settings and has the potential to enhance early detection, prevention, and treatment of cervical cancer.