Development and Clinical Validation of Lightweight, Multimodal Machine Learning Models for Smartphone-Based Cataract Detection and Classification

Abstract Globally, cataract remains the leading cause of blindness, affecting over 100 million people, with a disproportionate burden in low- and middle-income countries where access to ophthalmologists is limited. Although cataract surgery can restore vision almost immediately, timely diagnosis and referral remains a major barrier to care. We developed and prospectively evaluated lightweight, multimodal machine learning models capable of classifying lens status on a smartphone, enabling accessible screening in low-resource environments. We trained and evaluated both early and late fusion model architectures to classify lens status as clear, immature cataract, mature cataract, or pseudophakia using 6,794 anterior segment eye images captured using Scout™ smartphone-based diffuse illumination system paired with clinical data (age, visual acuity, and pinhole acuity) from 2,956 patients from three eye hospitals in India. The early fusion model, which jointly integrates image and clinical features via a learnable gating mechanism, achieved superior performance (AUROC=0.98) compared to late fusion. Model interpretation using feature importance and Grad-CAM revealed that early fusion effectively balanced visual and clinical parameters, mirroring ophthalmologist diagnostic reasoning. Prospective on-device evaluation in 210 patients at the Aravind Eye Hospital demonstrated equivalent performance, achieving an AUROC of 0.96, confirming robustness and real-time feasibility on mobile hardware. These results demonstrate the first prospectively validated, on-device, multimodal cataract machine learning model, demonstrating the feasibility of instant, offline cataract classification and referral in low-resource environments. This advance has potential to broaden cataract screening, allowing minimally trained workers to screen and refer patients, and enabling earlier diagnosis, referral, and treatment in underserved populations. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study was funded by the National Eye Institute (R21EY034343, K23EY032988), Johns Hopkins University, Stephen F Raab and Mariellen Brickley-Raab Rising Professorship in Ophthalmology, Boone Pickens Rising Professorship in Ophthalmology, National Academy of Medicine Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of Johns Hopkins University School of Medicine gave ethical approval for this work. Ethics committee/IRB of Aravind Eye Hospital gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Funding: National Eye Institute (R21EY034343, K23EY032988), Johns Hopkins University, Stephen F Raab and Mariellen Brickley-Raab Rising Professorship in Ophthalmology, Boone Pickens Rising Professorship in Ophthalmology, National Academy of Medicine Competing interests: The authors have declared no competing interests. Data Availability All data produced in the present work are contained in the manuscript.