Single Yes-or-No Question May Screen for Low Vision

Why It Matters

The approach enables rapid, low‑cost identification of vision impairment in settings where full clinical exams are impractical, supporting public‑health surveillance and tele‑health triage.

Key Takeaways

• Single yes/no question yields AUC 0.85 for low vision
• Two-question combo improves screening accuracy further
• Method enables remote, large‑scale vision assessments
• Study based on 385 adults across four U.S. centers
• Survey items derived from everyday visual tasks

Pulse Analysis

The detection of low vision and legal blindness traditionally relies on clinical visual‑acuity tests that demand specialized equipment and trained personnel. In community‑based studies or tele‑health programs, these resources are often unavailable, prompting researchers to explore self‑report tools that can be administered remotely. While generic questionnaires have suffered from poor specificity, the recent work by Wu et al. demonstrates that a single, functionally‑oriented yes‑or‑no question can approach the diagnostic performance of a full eye exam, offering a pragmatic alternative for large‑scale screening initiatives.

The Ohio State team evaluated 385 participants using a 100‑item visual‑function inventory grounded in everyday tasks such as reading menus or keyboard keys. By applying item‑response theory, they identified the ten most predictive questions, achieving an average area‑under‑the‑curve of 0.81 for low vision and 0.77 for legal blindness. The top question—reading a fast‑food menu—reached an AUC of 0.85, indicating strong discrimination. Adding a second complementary question raised the combined AUC further, confirming that a brief two‑item set can capture subtle acuity deficits without a physical exam.

These findings have immediate relevance for public‑health surveillance, epidemiological research, and remote clinical triage. Health systems can embed the two‑question screen into electronic health records or mobile apps, flagging individuals who require comprehensive ophthalmic evaluation while conserving resources. Moreover, the approach aligns with the growing emphasis on patient‑reported outcomes and scalable digital health tools. Future work should validate the items across diverse populations, explore language adaptations, and integrate the screen with AI‑driven risk models to refine referral pathways and ultimately reduce vision‑related disability.