Improving pre-bariatric surgery diagnosis of hiatal hernia using machine learning models

Assaf, Dan; Rayman, Shlomi; Segev, Lior; Neuman, Yair; Zippel, Douglas; Goitein, David

doi:10.1080/13645706.2021.1901120

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…Furthermore, ML prediction models were utilized to predict preoperative hiatal hernia diagnosis. Assaf and colleagues utilized three optional ML models to improve preoperative contrast swallow study (SS) prediction, thus finding that the implementation of ML algorithms to include patient data increases the sensitivity of preoperative SS and may lower the need for hiatal exploration in a large number of patients undergoing BS [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Current Applications of Artificial Intelligence in Bariatric Surgery

et al. 2022

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The application of artificial intelligence technologies is growing in several fields of healthcare settings. The aim of this article is to review the current applications of artificial intelligence in bariatric surgery. We performed a review of the literature on Scopus, PubMed and Cochrane databases, screening all relevant studies published until September 2021, and finally including 36 articles. The use of machine learning algorithms in bariatric surgery is explored in all steps of the clinical pathway, from presurgical risk-assessment and intraoperative management to complications and outcomes prediction. The models showed remarkable results helping physicians in the decision-making process, thus improving the quality of care, and contributing to precision medicine. Several legal and ethical hurdles should be overcome before these methods can be used in common practice.

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Section: Discussionmentioning

confidence: 99%

Current Applications of Artificial Intelligence in Bariatric Surgery

et al. 2022

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“…Machine learning models in radiological applications have been successfully used in the diagnosis and management of several medical fields related to the brain, breast, lung, and thyroid (41)(42)(43)(44). In bariatric surgery, Assaf et al utilized ML algorithms to increase the sensitivity of preoperative contrast swallow studies when evaluating patients for the presence of hiatal hernias (11). This ability can enhance conventional medical diagnosis and could reduce the number of patients needing hiatal exploration during bariatric surgery (11).…”

Section: Radiologymentioning

confidence: 99%

“…In bariatric surgery, Assaf et al utilized ML algorithms to increase the sensitivity of preoperative contrast swallow studies when evaluating patients for the presence of hiatal hernias (11). This ability can enhance conventional medical diagnosis and could reduce the number of patients needing hiatal exploration during bariatric surgery (11). Zhang et al, used functional magnetic resonance imaging (fMRI) with baseline whole-brain resting-state functional connectivity (RSFC) to develop a multivariate prediction framework "K-nearest neighbor (KNN)" (19).…”

Section: Radiologymentioning

confidence: 99%

“…Assaf et al argued that ML model applications improved pre-bariatric surgery diagnosis of hiatal hernia using contrast swallow studies and improved diagnostic sensitivity by 1.5 times baseline(11).Chiong et al developed a support vector machine (SVM) model and that predicted the level of body fat percentage among patients with higher accuracy than other model compared (12). Nudel et al, developed an artificial neural network (ANN) capable of predicting postoperative gastrointestinal leakage better than traditional regression models (13).…”

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confidence: 99%

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The development of machine learning in bariatric surgery

Enodien

Taha‐Mehlitz²,

Saad³

et al. 2023

Front. Surg.

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BackgroundMachine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery.MethodsThe study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process.ResultsA total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation.ConclusionsThis study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery.

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“…However, these components should be sufficiently tested in terms of various standards. Today, surgeons use robots to improve the surgical process [52,53].…”

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confidence: 99%

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

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Improving pre-bariatric surgery diagnosis of hiatal hernia using machine learning models

Cited by 7 publications

References 22 publications

Current Applications of Artificial Intelligence in Bariatric Surgery

Current Applications of Artificial Intelligence in Bariatric Surgery

The development of machine learning in bariatric surgery

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

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