Eun Sung-Jong, Yun Myoung Suk, Whangbo Taeg-Keun, Kim Khae-Hawn
Digital Health Industry Team, National IT Industry Promotion Agency, Jincheon, Korea.
Department of Computer Science, Gachon University, Seongnam, Korea.
Int Neurourol J. 2022 Sep;26(3):210-218. doi: 10.5213/inj.2244202.101. Epub 2022 Sep 30.
This paper aims to develop a clinical decision support system (CDSS) that can help detect the stone that is most important to the diagnosis of urolithiasis. Among them, especially for the development of artificial intelligence (AI) models that support a final judgment in CDSS, we would like to study the optimal AI model by comparing and evaluating them.
This paper proposes the optimal ureter stone detection model using various AI technologies. The use of AI technology compares and evaluates methods such as machine learning (support vector machine), deep learning (ResNet-50, Fast R-CNN), and image processing (watershed) to find a more effective method for detecting ureter stones.
The final value of sensitivity, which is calculated using true positive (TP) and false negative and is a measure of the probability of TP results, showed high recognition accuracy, with an average value of 0.93 for ResNet-50. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery.
The general situation in the most effective way to the detection stone can be found. But a variety of variables may be slightly different the difference through the term could tell. Future works, on urological diseases, are diverse and the research will be expanded by customizing AI models specialized for those diseases.
本文旨在开发一种临床决策支持系统（CDSS），以帮助检测对尿路结石诊断最为重要的结石。其中，特别是针对支持CDSS中最终判断的人工智能（AI）模型的开发，我们希望通过比较和评估来研究最佳的AI模型。
本文提出了使用各种AI技术的最佳输尿管结石检测模型。利用AI技术对机器学习（支持向量机）、深度学习（ResNet-50、Fast R-CNN）和图像处理（分水岭算法）等方法进行比较和评估，以找到检测输尿管结石更有效的方法。
使用真阳性（TP）和假阴性计算得出的灵敏度最终值，是衡量TP结果概率的指标，显示出较高的识别准确率，ResNet-50的平均值为0.93。这一发现证实，当使用开发的平台支持实际手术时，能够对结石区域进行准确引导。
可以找到检测结石的最有效方法的总体情况。但各种变量可能会略有不同，通过该术语可以看出差异。未来关于泌尿系统疾病的工作多种多样，并且将通过定制针对这些疾病的AI模型来扩大研究范围。
