The technological future of percutaneous nephrolithotomy: a Young Academic Urologists Endourology and Urolithiasis Working Group update.
Curr Opin Urol
; 33(2): 90-94, 2023 03 01.
Article
en En
| MEDLINE
| ID: mdl-36622261
ABSTRACT
PURPOSE OF REVIEW With advancements in surgical technology along with procedural techniques, this article throws light on the latest developments and applications of artificial intelligence (AI), extended reality, 3D (three-dimensional) printing and robotics in percutaneous nephrolithotomy (PCNL). RECENT FINDINGS:
This review highlights the applications of AI in PCNL over the past 2 years. Mostly studies have been reported on development of machine learning (ML) based predicting models and identification of stone composition using deep learning convolutional neural network (DL-CNN). But owing to the complexity of the models and lack of generalizability, it is still not incorporated in the routine clinical practice. Extended reality based simulation and training models have enabled trainees to enhance their skills and shorten the learning curve. Similar advantages have been reported with the use of 3D printed models when used to train young and novice endourologists to improve their skills in percutaneous access (PCA). Applications of robotics in PCNL look promising but are still in nascent stages.SUMMARY:
Future research on PCNL should focus more on generalizability and adaptability of technological advancements in terms of training and improvement of patient outcomes.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Cálculos Renales
/
Urolitiasis
/
Nefrolitotomía Percutánea
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Curr Opin Urol
Asunto de la revista:
UROLOGIA
Año:
2023
Tipo del documento:
Article
País de afiliación:
India