Clinical validation of an AI-assisted system for real-time kidney stone detection during flexible ureteroscopic surgery
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View abstract on PubMed
Summary
This summary is machine-generated.An artificial intelligence system for flexible ureteroscopy (AiFURS) accurately detects, classifies, and measures kidney stones in real-time. This AI tool enhances surgical precision and predicts reoperation risk, improving patient outcomes in endourology.
Area Of Science
- Urology
- Medical Artificial Intelligence
- Endourology
Background
- Flexible ureteroscopy (FURS) is a standard minimally invasive treatment for kidney stones.
- Accurate intraoperative assessment of stone burden and type is crucial for successful FURS outcomes.
- Current methods for stone measurement and classification can be subjective and time-consuming.
Purpose Of The Study
- To develop and clinically validate an artificial intelligence system for real-time stone detection, classification, and measurement during FURS (AiFURS).
- To assess the diagnostic accuracy of AiFURS compared to expert surgeons.
- To identify predictors of reoperation after FURS.
Main Methods
- Trained an AI model (AiFURS) on 6,170 annotated ureteroscopy video frames (11,870 stones).
- Validated AiFURS performance using ex vivo, in vivo, and external datasets.
- Compared AiFURS stone type prediction accuracy against expert urologists.
- Utilized logistic regression to identify predictors of reoperation.
Main Results
- AiFURS demonstrated high accuracy in predicting stone counts (r > 0.9) and size (r = 0.81 for stones >2mm).
- In vivo and external validation showed high diagnostic accuracy (92.2-95.3% and 86.8-92.2%) for patient-level stone type prediction, outperforming surgeons.
- The proportion of residual fragments >2mm was identified as an independent predictor of reoperation.
Conclusions
- AiFURS provides real-time, accurate stone assessment during FURS.
- The AI system enhances surgical precision and potentially reduces complications.
- AiFURS can improve patient outcomes by optimizing stone treatment and predicting reoperation risk.
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