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The development of machine learning in bariatric surgery.

Bassey Enodien1, Stephanie Taha-Mehlitz2, Baraa Saad3

  • 1Department of Surgery, GZO-Hospital, Wetzikon, Switzerland.

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|March 13, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) shows promise in bariatric surgery for predicting patient outcomes and improving data analysis. However, current applications are limited, necessitating further research to validate its benefits and address challenges in weight loss surgery.

Keywords:
ML algorithmsbariatric sugerymachine learningsystematic scoping reviewweight loss surgery

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Area of Science:

  • Medical Informatics
  • Surgical Innovation
  • Data Science in Healthcare

Background:

  • Machine learning (ML) automates analytical model building, offering significant potential for big data evaluation, leading to faster and more accurate outcomes.
  • The medical domain has seen increased adoption of ML, with bariatric surgery (weight loss surgery) being a key area of interest for its application.
  • This review systematically scopes the development and application of ML within bariatric surgery.

Purpose of the Study:

  • To explore the development and current state of machine learning applications in bariatric surgery.
  • To identify the primary roles of ML algorithms within the context of weight loss surgery.
  • To understand the data sources and types of ML models predominantly used in this field.

Main Methods:

  • A systematic scoping review methodology was employed, adhering to the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR) guidelines.
  • A comprehensive literature search was conducted across databases such as PubMed, Cochrane, and IEEE, along with Google Scholar, for studies published from 2016 to the present.
  • The PRESS checklist was utilized to ensure consistency and quality in the review process.

Main Results:

  • Seventeen articles met the inclusion criteria, with sixteen focusing on ML algorithms for prediction and one on diagnostic capacity in bariatric surgery.
  • The majority of included studies (15/17) were journal publications, predominantly from the United States (6/17).
  • Neural networks, particularly convolutional neural networks, were the most common ML algorithms, with most data (13/17) sourced from hospital databases.

Conclusions:

  • Machine learning offers substantial benefits for bariatric surgery, including enhanced prediction of patient outcomes and streamlined data analysis for surgeons.
  • Despite its potential, the current application of ML in bariatric surgery remains limited.
  • Further large-scale, multicenter studies are essential to validate ML findings, explore its limitations, and ensure robust internal and external validation in weight loss surgery.