[Study of the reliability in one dimensional size measurement with digital slit lamp microscope].

PURPOSE: To study the reliability of digital slit lamp microscope as a tool for quantitative analysis in one dimensional size measurement.

METHODS: Three single-blinded observers acquired and repeatedly measured the images with a size of 4.00 mm and 10.00 mm on the vernier caliper, which simulatated the human eye pupil and cornea diameter under China-made digital slit lamp microscope in the objective magnification of 4 times, 10 times, 16 times, 25 times, 40 times and 4 times, 10 times, 16 times, respectively. The correctness and precision of measurement were compared.

RESULTS: The images with 4 mm size were measured by three investigators and the average values were located between 3.98 to 4.06. For the images with 10.00 mm size, the average values fell within 10.00 ~ 10.04. Measurement results of 4.00 mm images showed, except A4, B25, C16 and C25, significant difference was noted between the measured value and the true value. Regarding measurement results of 10.00 mm iamges indicated, except A10, statistical significance was found between the measured value and the true value. In terms of comparing the results of the same size measured at different magnifications by the same investigator, except for investigators A's measurements of 10.00 mm dimension, the measurement results by all the remaining investigators presented statistical significance at different magnifications. Compared measurements of the same size with different magnifications, measurements of 4.00 mm in 4-fold magnification had no significant difference among the investigators', the remaining results were statistically significant. The coefficient of variation of all measurement results were less than 5%; as magnification increased, the coefficient of variation decreased.

CONCLUSION: The measurement of digital slit lamp microscope in one-dimensional size has good reliability,and should be performed for reliability analysis before used for quantitative analysis to reduce systematic errors.