Testicular salvage: using machine learning algorithm to develop a predictive model in testicular torsion.
Purpose: To compare the models developed with a classical statistics method and a machine learning model to predict the possibility of orchiectomy using preoperative parameters in patients who were admitted with testicular torsion.Materials and Methods: Patients who underwent scrotal exploration due...
| Publicado en: | Pediatric Surgery International Vol. 38; no. 10; pp. 1481 - 1487 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159003194&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159003194 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01790358 O07 jtl: Pediatric Surgery International issn: 01790358 maglogo: N pubinfo: dt: Oct2022 vid: 38 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159003194 158289546 159003194 NLM35915183 10.1007/s00383-022-05185-0 NLM35915183 159003194 ppf: 1481 ppct: 6 formats: tig: atl: Testicular salvage: using machine learning algorithm to develop a predictive model in testicular torsion. aug: au: Ekşi, Mithat Yavuzsan, Abdullah Hizir Evren, İsmail Ayten, Ali Fakir, Ali Emre Akkaş, Fatih Bursali, Kerem Akdağ, Azad Sahin, Selcuk Taşçi, Ali İhsan affil: Department of Urology, University of Health Sciences, Istanbul Bakirkoy Dr. Sadi Konuk Training and Research Hospital, Zuhuratbaba Mh. Tevfik Saglam Cd. No:11 Bakirkoy, Istanbul, Turkey sug: subj: Spermatic Cord Torsion Spermatic Cord Torsion Surgery Testis Algorithms Male Retrospective Design Testis Surgery Scales Male ab: Purpose: To compare the models developed with a classical statistics method and a machine learning model to predict the possibility of orchiectomy using preoperative parameters in patients who were admitted with testicular torsion.Materials and Methods: Patients who underwent scrotal exploration due to testicular torsion between the years 2000 and 2020 were retrospectively reviewed. Demographic data, features of admission time, and other preoperative clinical findings were recorded. Cox Regression Analysis as a classical statistics method and Random Forest as a Machine Learning algorithm was used to create a prediction model.Results: Among patients, 215 (71.6%) were performed orchidopexy and 85 (28.3%) were performed orchiectomy. The multivariate analysis revealed that monocyte count, symptom duration, and the number of previous Doppler ultrasonography were predictive of orchiectomy. Classical Cox regression analysis had an area under the curve (AUC) 0.937 with a sensitivity and specificity of 88 and 87%. The AUC for the Random Forest model was 0.95 with a sensitivity and specificity of 92 and 89%.Conclusion: The ML model outperformed the conventional statistical regression model in the prediction of orchiectomy. The ML methods are cheap, and their powers increase with increasing data input; we believe that their clinical use will increase over time. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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