The Effects of Ambient Lighting in Chest Radiology Reading Rooms
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Under typical dark chest radiography reading room conditions, a radiologist’s pupils contract and dilate as their visual focus intermittently shifts between the high luminance monitor and the darker background wall, resulting in increased visual fatigue and degradation of diagnostic performance. A controlled increase of ambient lighting may minimize these visual adjustments and potentially improve comfort and accuracy. This study was designed to determine the effect of a controlled increase of ambient lighting on chest radiologist nodule detection performance. Four chest radiologists read 100 radiographs (50 normal and 50 containing a subtle nodule) under low (E = 1 lx) and elevated (E = 50 lx) ambient lighting levels on a DICOM-calibrated, medical-grade liquid crystal display. Radiologists were asked to identify nodule locations and rate their detection confidence. A receiver operating characteristic (ROC) analysis of radiologist results was performed and area under ROC curve (AUC) values calculated for each ambient lighting level. Additionally, radiologist selection times under both illuminance conditions were determined. Average AUC values did not significantly differ (p > 0.05) between ambient lighting levels (estimated mean difference = −0.03; 95% CI, (−0.08, 0.03)). Average selection times decreased or remained constant with increased illuminance. The most considerable decreases occurred for false positive identification times (35.4 ± 18.8 to 26.2 ± 14.9 s) and true positive identification times (29.7 ± 18.3 to 24.5 ± 15.5 s). No performance differences were statistically significant. Study findings suggest that a controlled increase of ambient lighting within darkly lit chest radiology reading rooms, to a level more suitable for performance of common radiological tasks, does not appear to have a statistically significant effect on nodule detection performance.
KeywordsChest radiographs Image perception Visual perception
The authors would like to thank Dr. Michael Flynn for the use of his software. Additionally, the authors are grateful to Dr. Robert Saunders, Xiang Li, Takashi Matsui, and Brian Cote for their thoughtful questions and considerable assistance.
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