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Predicting stone composition via machine-learning models trained on intra-operative endoscopic digital images.

Guanhua ZhuChengbai LiYinsheng GuoLu SunTao JinZiyue WangShiqing LiFeng Zhou
Published in: BMC urology (2024)
This preliminary study suggests that DL is a promising method for identifying urinary stone components from intraoperative endoscopic images. Compared to intraoperative identification of stone components by the human eye, DL can discriminate single and mixed stone components more accurately and quickly. At the same time, based on the training of stone images in vitro, it is closer to the clinical application of stone images in vivo. This technology can be used to identify the composition of stones in real time and to adjust the frequency and energy intensity of the holmium laser in time. The prediction of stone composition can significantly shorten the operation time, improve the efficiency of stone surgery and prevent the risk of postoperative infection.
Keyphrases
  • deep learning
  • editorial comment
  • machine learning
  • convolutional neural network
  • patients undergoing
  • optical coherence tomography
  • endothelial cells
  • minimally invasive
  • mass spectrometry
  • high speed