Prediction of spontaneous distal ureteral stone passage using artificial intelligence.
Artificial intelligence
Spontaneous stone passage
Urolithiasis
Journal
International urology and nephrology
ISSN: 1573-2584
Titre abrégé: Int Urol Nephrol
Pays: Netherlands
ID NLM: 0262521
Informations de publication
Date de publication:
10 Feb 2024
10 Feb 2024
Historique:
received:
06
12
2023
accepted:
06
01
2024
medline:
10
2
2024
pubmed:
10
2
2024
entrez:
10
2
2024
Statut:
aheadofprint
Résumé
Identifying factors predicting the spontaneous passage of distal ureteral stones and evaluating the effectiveness of artificial intelligence in prediction. The files of patients presenting with distal ureteral stones were retrospectively evaluated. Those who experienced spontaneous passage were assigned to Group P, while those who did not were assigned to Group N. Demographic and clinical data of both groups were compared. Then, logistic regression analysis was performed to determine the factors predicting spontaneous stone passage. Based on these factors, a logistic regression model was prepared, and artificial intelligence algorithms trained on the dataset were compared with this model to evaluate the effectiveness of artificial intelligence in predicting spontaneous stone passage. When comparing stone characteristics and NCCT findings, it was found that the stone size was significantly smaller in Group P (4.9 ± 1.7 mm vs. 6.8 ± 1.4 mm), while the ureteral diameter was significantly higher in Group P (3.3 ± 0.9 mm vs. 3.1 ± 1.1 mm) (p < 0.05). Parameters such as stone HU, stone radiopacity, renal pelvis AP diameter, and perirenal stranding were similar between the groups. In multivariate analysis, stone size and alpha-blocker usage were significant factors in predicting spontaneous stone passage. The ROC analysis for the logistic regression model constructed from the significant variables revealed an area under the curve (AUC) of 0.835, with sensitivity of 80.1% and specificity of 68.4%. AI algorithms predicted the spontaneous stone passage up to 92% sensitivity and up to 86% specifity. AI algorithms are high-powered alternatives that can be used in the prediction of spontaneous distal ureteral stone passage.
Identifiants
pubmed: 38340263
doi: 10.1007/s11255-024-03955-4
pii: 10.1007/s11255-024-03955-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature B.V.
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