Fully Automated Longitudinal Assessment of Renal Stone Burden on Serial CT Imaging Using Deep Learning

Can Artificial Intelligence Accurately Detect Urinary Stones? A Systematic Review

Objectives: To perform a systematic review on artificial intelligence (AI) performances to detect urinary stones. Methods: A PROSPERO-registered (CRD473152) systematic search of Scopus, Web of Science, Embase, and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms ("automatic" OR "machine learning" OR "convolutional neural network" OR "artificial intelligence" OR "detection" AND "stone volume"). Risk-of-bias (RoB) assessment was performed according to the Cochrane RoB tool, the Joanna Briggs Institute Checklist for nonrandomized studies, and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: Twelve studies were selected for the final review, including three multicenter and nine single-center retrospective studies. Eleven studies completed at least 50% of the CLAIM checkpoints and only one presented a high RoB. All included studies aimed to detect kidney (5/12, 42%), ureter (2/12, 16%), or urinary (5/12, 42%) stones on noncontrast computed tomography (NCCT), but 42% intended to automate measurement. Stone distinction from vascular calcification interested two studies. All studies used AI machine learning network training and internal validation, but a single one provided an external validation. Trained networks achieved stone detection, with sensitivity, specificity, and accuracy rates ranging from 58.7% to 100%, 68.5% to 100%, and 63% to 99.95%, respectively. Detection Dice score ranged from 83% to 97%. A high correlation between manual and automated stone volume (r = 0.95) was noted. Differentiate distal ureteral stones and phleboliths seemed feasible. Conclusions: AI processes can achieve automated urinary stone detection from NCCT. Further studies should provide urinary stone detection coupled with phlebolith distinction and an external validation, and include anatomical abnormalities and urologic foreign bodies (ureteral stent and nephrostomy tubes) cases.

Relationship between stone volume, average CT value and operation time and efficiency before ureteral soft lens laser lithotripsy

BACKGROUND

Soft ureteroscopic holmium laser lithotripsy is becoming increasingly popular as the preferred method for treating mid-to-lower ureteral stones. Studies have indicated that the size, composition, hardness, and fragility of the stones can impact the treatment's effectiveness.

OBJECTIVE

To explore the relationship between stone volume, average CT value and operation time and efficiency before ureteral soft lens laser lithotripsy.

METHODS

Our study on 126 patients undergoing ureteroscopic holmium laser lithotripsy for ureteral calculi from May 2020 to January 2022 categorized them into groups based on stone volume and CT value. We compared surgical outcomes and analyzed correlations between stone characteristics, operation parameters, and stone clearance rate to identify independent risk factors influencing treatment efficacy.

RESULTS

Group A demonstrated significantly shorter operation durations and lower blood loss compared to Group B, along with higher single stone clearance rates and fewer postoperative complications. Similarly, Group C exhibited shorter operation times, reduced blood loss, higher stone clearance rates, and lower complication rates than Group D. Preoperative stone volume and CT value correlated positively with operation time and stone clearance rate, with both factors identified as independent risk factors affecting ureteral stone clearance following holmium laser lithotripsy.

CONCLUSION

The stone volume and average CT value before ureteral soft lens laser lithotripsy show a positive correlation with operation time and efficiency, indicating that larger stone volumes and higher CT values lead to slower lithotripsy speeds and reduced operation efficiency. Furthermore, preoperative stone volume and average CT value are identified as independent risk factors for residual stones.

Imaging-based deep learning in kidney diseases: recent progress and future prospects

Kidney diseases result from various causes, which can generally be divided into neoplastic and non-neoplastic diseases. Deep learning based on medical imaging is an established methodology for further data mining and an evolving field of expertise, which provides the possibility for precise management of kidney diseases. Recently, imaging-based deep learning has been widely applied to many clinical scenarios of kidney diseases including organ segmentation, lesion detection, differential diagnosis, surgical planning, and prognosis prediction, which can provide support for disease diagnosis and management. In this review, we will introduce the basic methodology of imaging-based deep learning and its recent clinical applications in neoplastic and non-neoplastic kidney diseases. Additionally, we further discuss its current challenges and future prospects and conclude that achieving data balance, addressing heterogeneity, and managing data size remain challenges for imaging-based deep learning. Meanwhile, the interpretability of algorithms, ethical risks, and barriers of bias assessment are also issues that require consideration in future development. We hope to provide urologists, nephrologists, and radiologists with clear ideas about imaging-based deep learning and reveal its great potential in clinical practice.Critical relevance statement The wide clinical applications of imaging-based deep learning in kidney diseases can help doctors to diagnose, treat, and manage patients with neoplastic or non-neoplastic renal diseases.Key points• Imaging-based deep learning is widely applied to neoplastic and non-neoplastic renal diseases.• Imaging-based deep learning improves the accuracy of the delineation, diagnosis, and evaluation of kidney diseases.• The small dataset, various lesion sizes, and so on are still challenges for deep learning.