Materials Map

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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in Cooperation with on an Cooperation-Score of 37%

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Publications (1/1 displayed)

  • 2024The Role of Preoperative Abdominal Ultrasound in the Preparation of Patients Undergoing Primary Metabolic and Bariatric Surgery: A Machine Learning Algorithm on 4418 Patients’ Records5citations

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Shafei, Mohamed El
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Aboelsoud, Moustafa R.
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Hany, Mohamed
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Agayby, Ann Samy Shafiq
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Ibrahim, Mohamed
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Torensma, Bart
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2024

Co-Authors (by relevance)

  • Shafei, Mohamed El
  • Aboelsoud, Moustafa R.
  • Hany, Mohamed
  • Agayby, Ann Samy Shafiq
  • Ibrahim, Mohamed
  • Torensma, Bart
  • Elmongui, Ehab
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article

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

  • Shafei, Mohamed El
  • Abouelnasr, Anwar Ashraf
  • Aboelsoud, Moustafa R.
  • Hany, Mohamed
  • Agayby, Ann Samy Shafiq
  • Ibrahim, Mohamed
  • Torensma, Bart
  • Elmongui, Ehab
Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>The utility of preoperative abdominal ultrasonography (US) in evaluating patients with obesity before metabolic bariatric surgery (MBS) remains ambiguously defined.</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>Retrospective 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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Four 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.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Our 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.</jats:p></jats:sec><jats:sec><jats:title>Graphical Abstract</jats:title></jats:sec>

Topics
  • impedance spectroscopy
  • size-exclusion chromatography
  • machine learning