☆ 3.8 Proceedings Paper

Machine Learning Based Prediction Models for Spontaneous Ureteral Stone Passage

2022 5TH INTERNATIONAL CONFERENCE ON MULTIMEDIA, SIGNAL PROCESSING AND COMMUNICATION TECHNOLOGIES (IMPACT) (2022)

Related references

Note: Only part of the references are listed.

Article Multidisciplinary Sciences

Development of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis

Jee Soo Park et al.

Summary: A prediction model of spontaneous ureteral stone passage (SSP) was developed using machine learning and logistic regression, with stone opacity, location, and whether it was the first ureteral stone episode identified as significant predictors. The performance of the models in identifying SSP for 5-10 mm ureteral stones was good.

PLOS ONE (2021)

Add to Collection

Review Urology & Nephrology

The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades

B. M. Zeeshan Hameed et al.

Summary: The review highlights the application of artificial intelligence in kidney stone disease, which shows promise in personalized patient care and decision making. Despite its potential benefits, AI is still a research tool and faces challenges in data infrastructure, wider applicability, complexity, and cost. Future research should focus on quality of life and cost of treatment to develop evidence-based AI algorithms that can guide urologists in managing stone disease.

CURRENT UROLOGY REPORTS (2021)

Add to Collection

Article Urology & Nephrology

Association of spontaneous expulsion with C-reactive protein and other clinico-demographic factors in patients with lower ureteric stone

Amit Jain et al.

UROLITHIASIS (2020)

Add to Collection

Article Urology & Nephrology

A neural network-based algorithm for predicting the spontaneous passage of ureteral stones

Mehmet Solakhan et al.

UROLITHIASIS (2020)

Add to Collection

Article Urology & Nephrology

Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy

Seung Woo Yang et al.

BMC UROLOGY (2020)

Add to Collection

Article Urology & Nephrology

MP33-17 USING A CLINICAL REGISTRY AND MACHINE LEARNING TO PREDICT URETERAL STENT PLACEMENT FOLLOWING URETEROSCOPY

Michael Inadomi* et al.

JOURNAL OF UROLOGY (2019)

Add to Collection

Article Engineering, Biomedical

An artificial intelligence-based clinical decision support system for large kidney stone treatment

Tayyebe Shabaniyan et al.

AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE (2019)

Add to Collection

Review Urology & Nephrology

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Jian Chen et al.

BJU INTERNATIONAL (2019)

Add to Collection

Article Biology

Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study

Njoud Abdullah Almansour et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2019)

Add to Collection

Article Multidisciplinary Sciences

Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

Mohamed Elhoseny et al.

SCIENTIFIC REPORTS (2019)

Add to Collection

Article Urology & Nephrology

Artificial Neural Network System to Predict the Postoperative Outcome of Percutaneous Nephrolithotomy

Alireza Aminsharifi et al.

JOURNAL OF ENDOUROLOGY (2017)

Add to Collection

Article Urology & Nephrology

Factors predicting the spontaneous passage of a ureteric calculus of ≤10 mm

Abul-Fotouh Ahmed et al.

ARAB JOURNAL OF UROLOGY (2015)

Add to Collection

Article Urology & Nephrology

Efficacy of tamsulosin in the medical management of juxtavesical ureteral stones

M Dellabella et al.

JOURNAL OF UROLOGY (2003)

Add to Collection

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Add to Collection

© Peeref 2019-2024. All rights reserved.