Mr. Mohamed Amin Mohamed Ali Profile

Mohamed Amin Mohamed Ali

at Univ College Dublin

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Proceedings Article | 13 March 2009 Paper

A novel teaching tool using dynamic cues improves visualisation of chest lesions by naive observers

M. Mohamed Ali, R. Toomey, J. Ryan, F. Cuffe, P. Brennan

Proceedings Volume 7263, 726304 (2009) https://doi.org/10.1117/12.813510

KEYWORDS: Diagnostics, Statistical analysis, Receivers, Copper, Medicine, Visualization, Radiology, Radiography, Chest, Chest imaging

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Introduction Dynamic cueing is an effective way of stimulating perception of regions of interest within radiological images. This study explores the impact of a novel teaching tool using dynamic cueing for lesion detection on plain chest radiographs. Materials and methods Observer performance studies were carried out where 36 novices examined 30 chest images in random order. Half of these contained between one and three simulated pulmonary nodules. Three groups were investigated: A (control: no teaching tool), B (retested immediately after undergoing the teaching tool) and C (retested a week after undergoing the teaching tool). The teaching tool involved dynamically displaying the same images with and without lesions. Results were compared using Receiver Operating Characteristics (ROC), sensitivity and specificity analyses. Results The second reading showed significantly greater area under the ROC curve (Az value) (p<0.0001) and higher sensitivity value (p=0.004) compared to the first reading for Group B. No differences between readings were demonstrated for groups A or C. When the magnitudes of the above changes were compared between Group B and the other two groups, greater changes in Az value for Group B were noted (B vs. A:p=0.0003, B vs. C:p=0.0005). For sensitivity, when Group B was compared to Group A, the magnitude of the change was significantly greater (p=0.0029) whereas when Group B was compared to Group C, the magnitude change demonstrated a level approaching significance (p=0.0768). Conclusions The novel teaching tool improves identification of pulmonary nodular lesions on chest radiographs in the short term.

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