ArticleObesity surgery2024
The Role of Preoperative Abdominal Ultrasound in the Preparation of Patients Undergoing Primary Metabolic and Bariatric Surgery: A Machine Learning Algorithm on 4418 Patients' Records.
Article in Obesity surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence and machine learning in bariatric surgery: a comprehensive systematic review.Langenbeck's archives of surgery · 2026Pooled it
- Based on bibliometric visual analysis, the current status and development trends of research on complications after cholecystectomy.Frontiers in surgery · 2025Pooled it
- Multimodal Evaluation of Body Composition and Muscle Strength in Women Before and After Bariatric Surgery: A Clinical Observational Study.Medicina (Kaunas, Lithuania) · 2026Observational
- Development and External Validation of a Machine Learning-Based Risk Score for Stent Outcomes in Post-Bariatric Leak Management: The "Alexandria-Bari-Stent" Tool.Obesity surgery · 2025Article
- Clinical Value of Routine Preoperative Ultrasonography in Bariatric Surgery Candidates: A Retrospective Analysis of 1119 Cases.Tomography (Ann Arbor, Mich.) · 2025Article
- Application value of abdominal ultrasonography in the diagnosis of pediatric patients aged 3-12 years with acute appendicitis.World journal of gastrointestinal surgery · 2025Article
- Are We Over-Blaming Metabolic and Bariatric Surgery for Gallbladder Diseases It Did Not Cause? The Impact of Preoperative Ultrasound Practices.Obesity surgery · 2025Article
- "Impenetrable" Is a Subjective Term and Bariatric Procedures Can Provide a Safer Treatment Option for Patients with Obesity and Complex Abdominal Wall Hernias-"Laparocele".Obesity surgery · 2024Article
- Reply: Preoperative Anesthesia Evaluations May Affect the Decision for Bariatric Surgery.Obesity surgery · 2024Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe utility of preoperative abdominal ultrasonography (US) in evaluating patients with obesity before metabolic bariatric surgery (MBS) remains ambiguously defined.
methodRetrospective analysis whereby patients were classified into four groups based on ultrasound results. Group 1 had normal findings. Group 2 had non-significant findings that did not affect the planned procedure. Group 3 required additional or follow-up surgeries without changing the surgical plan. Group 4, impacting the procedure, needed further investigations and was subdivided into 4A, delaying surgery for more assessments, and 4B, altering or canceling the procedure due to critical findings. Machine learning techniques were utilized to identify variables.
resultsFour thousand four hundred eighteen patients' records were analyzed. Group 1 was 45.7%. Group 2, 35.7%; Group 3, 17.0%; Group 4, 1.5%, Group 4A, 0.8%; and Group 4B, 0.7%, where surgeries were either canceled (0.3%) or postponed (0.4%). The hyperparameter tuning process identified a Decision Tree classifier with a maximum tree depth of 7 as the most effective model. The model demonstrated high effectiveness in identifying patients who would benefit from preoperative ultrasound before MBS, with training and testing accuracies of 0.983 and 0.985. It also showed high precision (0.954), recall (0.962), F1 score (0.958), and an AUC of 0.976.
conclusionOur study found that preoperative ultrasound demonstrated clinical utility for a subset of patients undergoing metabolic bariatric surgery. Specifically, 15.9% of the cohort benefited from the identification of chronic calculous cholecystitis, leading to concomitant cholecystectomy. Additionally, surgery was postponed in 1.4% of the cases due to other findings. While these findings indicate a potential benefit in certain cases, further research, including a cost-benefit analysis, is necessary to fully evaluate routine preoperative ultrasound's overall utility and economic impact in this patient population.
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