Automated urinary sediment detection for Fabry disease using deep-learning algorithms.

"to detect the area based on which the models made their judgment we have visualized the heatmaps of the class activation for each segmented image 2 6 mann-whitney u tests were used to compare aucs between each group and analyses were performed using the scipy scikit-learn and keras/tensorflow python packages p < 05 was considered statistically significant 2 7 the image data were blinded and analyzed by h u the entire code is open to the public in github ( https://github com/huryu/dl_fabry ) 2 8 the datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request 3 three fabry patients whose diagnosis was definitely confirmed were enrolled in this study as disease-positive cases."

"our models are already submitted in https://github com/huryu/dl_fabry ."

"Declaration of Competing Interest All authors have no conflict of interest to declare."

"Funding This study was supported by the Research & Development grant from the 10.13039/100007786National Center for Child Health and Development (Grant 30-37)."