دورية أكاديمية
Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives.
| العنوان: | Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives. |
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| المؤلفون: | Bektaş M; Department of Gastrointestinal Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands. m.bektas@amsterdamumc.nl., Reiber BMM; Department of Gastrointestinal Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands., Pereira JC; Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, the Netherlands., Burchell GL; Medical Library Department, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands., van der Peet DL; Department of Gastrointestinal Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands. |
| المصدر: | Obesity surgery [Obes Surg] 2022 Aug; Vol. 32 (8), pp. 2772-2783. Date of Electronic Publication: 2022 Jun 17. |
| نوع المنشور: | Journal Article; Review; Systematic Review |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer Science + Business Media Country of Publication: United States NLM ID: 9106714 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1708-0428 (Electronic) Linking ISSN: 09608923 NLM ISO Abbreviation: Obes Surg Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2006- : New York : Springer Science + Business Media Original Publication: Oxford, OX, UK : Rapid Communications of Oxford, [1991- |
| مواضيع طبية MeSH: | Bariatric Surgery* , Obesity, Morbid*/surgery, Algorithms ; Artificial Intelligence ; Humans ; Weight Loss |
| مستخلص: | Background: Machine learning (ML) has been successful in several fields of healthcare, however the use of ML within bariatric surgery seems to be limited. In this systematic review, an overview of ML applications within bariatric surgery is provided. Methods: The databases PubMed, EMBASE, Cochrane, and Web of Science were searched for articles describing ML in bariatric surgery. The Cochrane risk of bias tool and the PROBAST tool were used to evaluate the methodological quality of included studies. Results: The majority of applied ML algorithms predicted postoperative complications and weight loss with accuracies up to 98%. Conclusions: In conclusion, ML algorithms have shown promising capabilities in the prediction of surgical outcomes after bariatric surgery. Nevertheless, the clinical introduction of ML is dependent upon the external validation of ML. (© 2022. The Author(s).) |
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| فهرسة مساهمة: | Keywords: Artificial intelligence; Bariatric surgery; Deep learning; Machine learning |
| تواريخ الأحداث: | Date Created: 20220617 Date Completed: 20220713 Latest Revision: 20220804 |
| رمز التحديث: | 20240628 |
| مُعرف محوري في PubMed: | PMC9273535 |
| DOI: | 10.1007/s11695-022-06146-1 |
| PMID: | 35713855 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1708-0428 |
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| DOI: | 10.1007/s11695-022-06146-1 |