Application of deep learning convolutional neural network in prediction of stone location, skin to stone distance and composition in renal lithiasis: A single center pilot study

Radiomics is increasingly applied to the diagnosis, management, and outcome prediction of various urological conditions. Urolithiasis is a common benign condition with a high incidence and recurrence rate. The purpose of this scoping review is to evaluate the current evidence of the application of radiomics in urolithiasis, especially its utility in diagnostics and therapeutics. An electronic literature search on radiomics in the setting of urolithiasis was conducted on PubMed, EMBASE, and Scopus from inception to 21 March 2022. A total of 7 studies were included. Radiomics has been successfully applied in the field of urolithiasis to differentiate phleboliths from calculi and classify stone types and composition pre-operatively. More importantly, it has also been utilized to predict outcomes and complications after endourological procedures. Although radiomics in urolithiasis is still in its infancy, it has the potential for large-scale implementation. Its greatest potential lies in the correlation with conventional established diagnostic and therapeutic factors.

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Application of deep learning convolutional neural network in prediction of stone location, skin to stone distance and composition in renal lithiasis: A single center pilot study

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Radiomics signature for automatic hydronephrosis detection in unenhanced Low-Dose CT

Radiomics in Urolithiasis: Systematic Review of Current Applications, Limitations, and Future Directions

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