Development and Evaluation of Urolithiasis Detection Technology Based on a Multimethod Algorithm
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Purpose: In this paper, we propose an optimal ureter stone detection model utilizing multiple artificial intelligence technologies. Specifically, the proposed model of urinary tract stone detection merges an artificial intelligence model and an image processing model, resulting in a multimethod approach. Methods: We propose an optimal urinary tract stone detection algorithm based on artificial intelligence technology. This method was intended to increase the accuracy of urinary tract stone detection by combining deep learning technology (Fast R-CNN) and image processing technology (Watershed). Results: As a result of deriving the confusion matrix, the sensitivity and specificity of urinary tract stone detection were calculated to be 0.90 and 0.91, and the accuracy for their position was 0.84. This value was higher than 0.8, which is the standard for accuracy. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery. Conclusions: The performance evaluation of the method proposed herein indicated that it can effectively play an auxiliary role in diagnostic decision-making with a clinically acceptable range of safety. In particular, in the case of ambush stones or urinary stones accompanying ureter polyps, the value that could be obtained through combination therapy based on diagnostic assistance could be evaluated.
Abstract
Purpose: In this paper, we propose an optimal ureter stone detection model utilizing multiple artificial intelligence technologies. Specifically, the proposed model of urinary tract stone detection merges an artificial intelligence model and an image processing model, resulting in a multimethod approach. Methods: We propose an optimal urinary tract stone detection algorithm based on artificial intelligence technology. This method was intended to increase the accuracy of urinary tract stone detection by combining deep learning technology (Fast R-CNN) and image processing technology (Watershed). Results: As a result of deriving the confusion matrix, the sensitivity and specificity of urinary tract stone detection were calculated to be 0.90 and 0.91, and the accuracy for their position was 0.84. This value was higher than 0.8, which is the standard for accuracy. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery. Conclusions: The performance evaluation of the method proposed herein indicated that it can effectively play an auxiliary role in diagnostic decision-making with a clinically acceptable range of safety. In particular, in the case of ambush stones or urinary stones accompanying ureter polyps, the value that could be obtained through combination therapy based on diagnostic assistance could be evaluated.
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