Use of Mobile Apps for Visual Acuity Assessment: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Vision impairments (VIs) and blindness are major global public health issues. A visual acuity (VA) test is one of the most crucial standard psychophysical tests of visual function and has been widely used in a broad range of health care domains, especially in many clinical settings. In recent years, there has been increasing research on mobile app-based VA assessment designed to allow people to test their VA at any time and any location. OBJECTIVE: The goal of the review was to assess the accuracy and reliability of using mobile VA measurement apps. METHODS: We searched PubMed, Embase, Cochrane Library, and Google Scholar for relevant articles on mobile apps for VA assessment published between January 1, 2008, and July 1, 2020. Two researchers independently inspected and selected relevant studies. Eventually, we included 22 studies that assessed tablet or smartphone apps for VA measurement. We then analyzed sensitivity, specificity, and accuracy in the 6 papers we found through a meta-analysis. RESULTS: Most of the 22 selected studies can be considered of high quality based on the Quality Assessment of Diagnostic Accuracy Studies-2. In a meta-analysis of 6 studies involving 24,284 participants, we categorized the studies based on the age groups of the study participants (ie, aged 3-5 years, aged 6-22 years, and aged 55 years and older), examiner (ie, professional and nonprofessional examiners), and the type of mobile devices (ie, smartphone, iPad). In the group aged 3 to 5 years, the pooled sensitivity for VA app tests versus clinical VA tests was 0.87 (95% CI 0.79-0.93; P=.39), and the pooled specificity was 0.78 (95% CI 0.70-0.85; P=.37). In the group aged 6 to 22 years, the pooled sensitivity for VA app tests versus clinical VA tests was 0.86 (95% CI 0.84-0.87; P<.001), and the pooled specificity for VA app tests versus clinical VA tests was 0.91 (95% CI 0.90-0.91; P=.27). In the group aged 55 years and older, the pooled sensitivity for VA app tests versus clinical VA tests was 0.85 (95% CI 0.55-0.98), and the pooled specificity for VA app tests versus clinical VA tests was 0.98 (95% CI 0.95-0.99). We found that the nonprofessional examiner group (AUC 0.93) had higher accuracy than the professional examiner group (AUC 0.87). In the iPad-based group, the pooled sensitivity for VA app tests versus clinical VA tests was 0.86, and the pooled specificity was 0.79. In the smartphone-based group, the pooled sensitivity for VA app tests versus clinical VA tests was 0.86 (P<.001), and the pooled specificity for VA app tests versus clinical VA tests was 0.91 (P<.001). CONCLUSIONS: In this study, we conducted a comprehensive review of the research on existing mobile apps for VA tests to investigate their diagnostic value and limitations. Evidence gained from this study suggests that mobile app-based VA tests can be useful for on-demand VI detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".