Efiloglu O. et al., 2025: A novel deep learning approach for predicting stone-free rates post-ESWL on uncontrasted CT.

Ozgur Efiloglu, Muhammed Yildirim, Kadir Yildirim, Harun Bingol, Mustafa Kaan Akalin, Meftun Culpan, Bilal Alatas, Asif Yildirim 
PeerJ Comput Sci. 2025 Aug 11;11:e3111. doi: 10.7717/peerj-cs.3111 FREE FULL TEXT

Abstract

Extracorporeal shock wave lithotripsy (ESWL) is one of the most often employed therapy methods for managing kidney stones. In our work, we sought to assess the efficacy of the artificial intelligence model developed using non-contrast computed tomography (CT) images in predicting stone-free rates for ESWL. The main difference between this study and other studies is that it proposes an artificial intelligence-based model that predicts the success of ESWL treatment using artificial intelligence methods. Data from 910 patients who underwent ESWL between January 2016 and June 2021 were analyzed retrospectively. Since the local binary pattern (LBP) and histogram of oriented gradients (HOG) feature extraction methods gave more successful results than other methods, a new feature map was obtained using the neighborhood component analysis (NCA) dimension reduction method after combining the features obtained using these methods. Then, the reduced feature map was classified into classifiers. In conclusion, we analyzed the effect of ESWL treatment using different artificial intelligence methods and found that the prediction accuracy was 94% on average. Results were obtained from seven different convolutional neural networks (CNNs) and two textural-based models in the study. Since textural-based models achieved the highest success among these models, these models were used as the base in the proposed model. The proposed model achieved better results than nine different models used in the study. When the results obtained from the proposed hybrid model for ESWL prediction are examined, this model will guide experts in the treatment of the disease.

Comment Hans-Göran Tiselius

It is clear in the literature that we slowly are leaving the era of systematic reviews and meta-analyses. For the average reader – urologist or not – it is much more difficult to follow the modern analytical analyses and calculations associated with AI. This is at least the case for this AI-report on prediction of stone-free rates based on analyses of NCCT-images.
How the different artificial-intelligence methods work is unknown to the reviewer and it is beyond my mathematical horizon to disentangle the details of the image analysis. Although the authors initially mention the importance of lower calyx anatomy and geometry such as infundibulo-pelvic angle, calyx length, calyx width and skin-to-stone distance, it is obvious that these variables were not included in the image analysis. Instead were the properties of the stone measured in detail. In that analysis the predicted outcome of SWL was referred to the result of the AI-analysis.
For conclusions it had been of great interest to take in account all those factors that determine the lower calyx geometry.

Hans-Göran Tiselius