Abstract
Purpose
Although a novel deep learning software was proposed using post-processed images obtained by the fusion between X-ray images of normal post-operative radiography and surgical sponge, the association of the retained surgical item detectability with human visual evaluation has not been sufficiently examined. In this study, we investigated the association of retained surgical item detectability between deep learning and human subjective evaluation.
Methods
A deep learning model was constructed from 2987 training images and 1298 validation images, which were obtained from post-processing of the image fusion between X-ray images of normal post-operative radiography and surgical sponge. Then, another 800 images were used, i.e., 400 with and 400 without surgical sponge. The detection characteristics of retained sponges between the model and a general observer with 10-year clinical experience were analyzed using the receiver operator characteristics.
Results
The following values from the deep learning model and observer were, respectively, derived: Cutoff values of probability were 0.37 and 0.45; areas under the curves were 0.87 and 0.76; sensitivity values were 85% and 61%; and specificity values were 73% and 92%.
Conclusion
For the detection of surgical sponges, we concluded that the deep learning model has higher sensitivity, while the human observer has higher specificity. These characteristics indicate that the deep learning system that is complementary to humans could support the clinical workflow in operation rooms for prevention of retained surgical items.
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Acknowledgements
We thank Editage (www.editage.com) for the English language editing.
Funding
This work was supported by the Japanese Grant of The Clinical Research Promotion Foundation (2020).
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MK, HW, TS, TK, RM, HH were involved in study conception and design and analysis and interpretation of data; MK, HW, HA, TK, SB contributed to acquisition of data; MK, TS, TK, RM, HH were involved in drafting of manuscript; MK, HW, TK, SB, SU, KI contributed to critical revision.
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This study has a pending patent in Japan (2020–094615).
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This retrospective study was approved by the institutional review board in Kyushu University and conducted in accordance with the 1964 Declaration of Helsinki.
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Kawakubo, M., Waki, H., Shirasaka, T. et al. A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection. Int J CARS 18, 1459–1467 (2023). https://doi.org/10.1007/s11548-022-02816-8
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DOI: https://doi.org/10.1007/s11548-022-02816-8