TY - JOUR
T1 - Deep learning computer vision algorithm for detecting kidney stone composition
AU - Black, Kristian M.
AU - Law, Hei
AU - Aldoukhi, Ali
AU - Deng, Jia
AU - Ghani, Khurshid R.
N1 - Publisher Copyright:
© 2020 The Authors BJU International © 2020 BJU International Published by John Wiley & Sons Ltd
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Objectives: To assess the recall of a deep learning (DL) method to automatically detect kidney stones composition from digital photographs of stones. Materials and Methods: A total of 63 human kidney stones of varied compositions were obtained from a stone laboratory including calcium oxalate monohydrate (COM), uric acid (UA), magnesium ammonium phosphate hexahydrate (MAPH/struvite), calcium hydrogen phosphate dihydrate (CHPD/brushite), and cystine stones. At least two images of the stones, both surface and inner core, were captured on a digital camera for all stones. A deep convolutional neural network (CNN), ResNet-101 (ResNet, Microsoft), was applied as a multi-class classification model, to each image. This model was assessed using leave-one-out cross-validation with the primary outcome being network prediction recall. Results: The composition prediction recall for each composition was as follows: UA 94% (n = 17), COM 90% (n = 21), MAPH/struvite 86% (n = 7), cystine 75% (n = 4), CHPD/brushite 71% (n = 14). The overall weighted recall of the CNNs composition analysis was 85% for the entire cohort. Specificity and precision for each stone type were as follows: UA (97.83%, 94.12%), COM (97.62%, 95%), struvite (91.84%, 71.43%), cystine (98.31%, 75%), and brushite (96.43%, 75%). Conclusion: Deep CNNs can be used to identify kidney stone composition from digital photographs with good recall. Future work is needed to see if DL can be used for detecting stone composition during digital endoscopy. This technology may enable integrated endoscopic and laser systems that automatically provide laser settings based on stone composition recognition with the goal to improve surgical efficiency.
AB - Objectives: To assess the recall of a deep learning (DL) method to automatically detect kidney stones composition from digital photographs of stones. Materials and Methods: A total of 63 human kidney stones of varied compositions were obtained from a stone laboratory including calcium oxalate monohydrate (COM), uric acid (UA), magnesium ammonium phosphate hexahydrate (MAPH/struvite), calcium hydrogen phosphate dihydrate (CHPD/brushite), and cystine stones. At least two images of the stones, both surface and inner core, were captured on a digital camera for all stones. A deep convolutional neural network (CNN), ResNet-101 (ResNet, Microsoft), was applied as a multi-class classification model, to each image. This model was assessed using leave-one-out cross-validation with the primary outcome being network prediction recall. Results: The composition prediction recall for each composition was as follows: UA 94% (n = 17), COM 90% (n = 21), MAPH/struvite 86% (n = 7), cystine 75% (n = 4), CHPD/brushite 71% (n = 14). The overall weighted recall of the CNNs composition analysis was 85% for the entire cohort. Specificity and precision for each stone type were as follows: UA (97.83%, 94.12%), COM (97.62%, 95%), struvite (91.84%, 71.43%), cystine (98.31%, 75%), and brushite (96.43%, 75%). Conclusion: Deep CNNs can be used to identify kidney stone composition from digital photographs with good recall. Future work is needed to see if DL can be used for detecting stone composition during digital endoscopy. This technology may enable integrated endoscopic and laser systems that automatically provide laser settings based on stone composition recognition with the goal to improve surgical efficiency.
KW - #KidneyStones
KW - #UroStone
KW - artificial intelligence
KW - computer vision
KW - deep learning
KW - holmium
KW - laser lithotripsy
KW - ureteroscopy
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U2 - 10.1111/bju.15035
DO - 10.1111/bju.15035
M3 - Article
C2 - 32045113
AN - SCOPUS:85080980432
VL - 125
SP - 920
EP - 924
JO - British Journal of Urology
JF - British Journal of Urology
SN - 1464-4096
IS - 6
ER -