Development and Validation of the First Smart TV-Based Visual Acuity Test: A Prospective Study

Abstract

(1) Background: While smartphones are among the primary devices used in telemedical applications, smart TV healthcare apps are not prevalent despite smart TVs' penetrance in home settings. The present study's objective was to develop and validate the first smart TV-based visual acuity (VA) test (Democritus Digital Visual Acuity Test (DDiVAT)) that allows a reliable VA self-assessment. (2) Methods: This is a prospective validation study. DDiVAT introduces several advanced features for reliable VA self-testing; among them: automatic calibration, voice recognition, voice guidance, automatic calculation of VA indexes, and a smart TV-based messaging system. Normal and low vision participants were included in the validation. DDiVAT VA results (VA_DDiVAT) were compared against the ones from: (a) the gold-standard conventional ETDRS (VA_ETDRS), and, (b) an independent ophthalmologist who monitored the self-examination testing (VA_RES). Comparisons were performed by noninferiority test (set at 2.5-letters) and intraclass correlation coefficients (ICCs). DDiVAT's test-retest reliability was assessed within a 15-day time-window. (3) Results: A total of 300 participants (185 and 115 with normal and low vision, respectively) responded to ETDRS and DDiVAT. Mean difference in letters was -0.05 for VA_ETDRS-VA_RES, 0.62 for VA_RES-VA_DDiVAT, and 0.67 for VA_ETDRS-VA_DDiVAT, significantly lower than the 2.5 letter noninferiority margin. ICCs indicated an excellent level of agreement, collectively and for each group (0.922-0.996). All displayed letters in DDiVAT presented a similar difficulty. The overall accuracy of the voice recognition service was 96.01%. ICC for VA_DDiVAT test-retest was 0.957. (4) Conclusions: The proposed DDiVAT presented non-significant VA differences with the ETDRS, suggesting that it can be used for accurate VA self-assessment in telemedical settings, both for normal and low-vision patients.

Keywords: home; smart TV; smartphone; telemedicine; validation; visual acuity test.

Figure 1

DDiVAT System-as-a-Service (SAS) explained.

Figure 2

DDiVAT’s unified modeling language (UML) diagram for the voice recognition service.

Figure 3

DDiVAT Control Flow chart.

Figure 4

DDiVAT color-based navigation: (a) DDiVAT smart TV-app; (b) Smart TV remote control; (c) DDiVAT SP-app.

Figure 5

logMAR 1 verification step: (a) DDiVAT’s TV-app verification screen; (b) DDiVAT’s SP-app verification screen.

Figure 6

Bland-Altman plots comparing VA_ETDRS and VA_RES in NVG (blue) and LVG (red).

Figure 7

Bland-Altman plots comparing VA_ETDRS and VA_DDiVAT in NVG (blue) and LVG (red).

Figure 8

Bland-Altman plots comparing VA_RES and VA_DDiVAT in NVG (blue) and LVG (red).

Figure 9

Noninferiority analysis (95% CI and mean difference value as “*”, using a 2.5-letter margin), of VA_ETDRS vs. VA_RES (denoted as “∆”), VA_ETDRS vs. VA_DDiVAT (denoted as “o”), and VA_RES vs. VA_DDiVAT (denoted as “□”) for NVG (black), LVG (red), and all patients (blue).

Figure 10

Confusion matrix of the letters displayed in DDiVAT’s TV-app (Letters_TV) versus the letters read by the participants (Letters_Par).

Figure 11

Percentage of correct identification for each letter by the examinees.

Figure 12

Confusion matrix of the letters read by participants (Letters_Par) versus the letters recognized by the smartphone (Letters_DDiVAT).

Figure 13

Percentage of letters correctly identified by the voice recognition service (sensitivity). The number of each letter’s appearance is shown at the top of each column. Blue bars: Latin letters used in the test and appearing in TV-app; orange bars: letters not included in the test, thus not appearing in TV-app.