Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery

Vukićević, Arso M.; Stojadinović, Miroslav M.; Radović, Miloš; Djordjević, Milena; Cirkovic, Bojana Andjelkovic; Pejović, Tomislav; Jovičić, Gordana; Filipović, Nenad

doi:10.1016/j.compbiomed.2016.05.016

Cited by 14 publications

(5 citation statements)

References 44 publications

(55 reference statements)

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“…The ANN revealed the most informative signs of possible choledocholithiasis were the level of bilirubin, alanine aminotransferase, the diameter of the common bile duct, the number of stones in the gall bladder, the size of the smallest stone, history of biliary colic, history of acute cholecystitis or acute pancreatitis. The authors concluded that ANN is a reliable and user-friendly system that can be successfully used to predict choledocholithiasis [ 36 ].…”

Section: Resultsmentioning

confidence: 99%

WSES project on decision support systems based on artificial neural networks in emergency surgery

et al. 2021

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The article is a scoping review of the literature on the use of decision support systems based on artificial neural networks in emergency surgery. The authors present modern literature data on the effectiveness of artificial neural networks for predicting, diagnosing and treating abdominal emergency conditions: acute appendicitis, acute pancreatitis, acute cholecystitis, perforated gastric or duodenal ulcer, acute intestinal obstruction, and strangulated hernia. The intelligent systems developed at present allow a surgeon in an emergency setting, not only to check his own diagnostic and prognostic assumptions, but also to use artificial intelligence in complex urgent clinical cases. The authors summarize the main limitations for the implementation of artificial neural networks in surgery and medicine in general. These limitations are the lack of transparency in the decision-making process; insufficient quality educational medical data; lack of qualified personnel; high cost of projects; and the complexity of secure storage of medical information data. The development and implementation of decision support systems based on artificial neural networks is a promising direction for improving the forecasting, diagnosis and treatment of emergency surgical diseases and their complications.

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Section: Resultsmentioning

confidence: 99%

WSES project on decision support systems based on artificial neural networks in emergency surgery

et al. 2021

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“…Intraoperative conversions and complexities have been predicted by ML algorithms with accuracies between 83% and 89%. Two studies applied ML algorithms to predict gallstones and related diseases, in which ML models have shown AUCs from 0.85 to 0.94, along with accuracies up to 97% [59,60] . In addition, Shi et al applied ML algorithms to predict the postoperative quality of life in patients with gallstones [61] .…”

Section: Biliary Surgerymentioning

confidence: 99%

Artificial intelligence in hepatFIGopancreaticobiliary surgery: a systematic review

Bektaş¹,

Zonderhuis²,

Marquering³

et al. 2022

Art Int Surg

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Aim: The aim of this systematic review was to provide an overview of Machine Learning applications within hepatopancreaticobiliary surgery. The secondary aim was to evaluate the predictive performances of applied Machine Learning models. Methods: A systematic search was conducted in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only eligible for inclusion when they described Machine Learning in hepatopancreaticobiliary surgery. The Cochrane and PROBAST risk of bias tools were used to evaluate the quality of studies and included Machine Learning models. Results: Out of 1821 articles, 52 studies have met the inclusion criteria. The majority of Machine Learning models were developed to predict the course of disease, and postoperative complications. The course of disease has been predicted with accuracies up to 99%, and postoperative complications with accuracies up to 89%. Most studies had a retrospective study design, in which external validation was absent for Machine Learning models. Conclusion: Machine learning models have shown promising accuracies in the prediction of short-term and long-term surgical outcomes after hepatopancreaticobiliary surgery. External validation of Machine Learning models is required to facilitate the clinical introduction of Machine Learning.

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“…Machine learning techniques are extensively applied in medicine for predicting numerous diseases, especially for forecasting various types of disease at early phases after analyzing its characteristics 18‐22 . These algorithms are widely applicable in skin diseases, skin cancer, breast cancer, kidney diseases, diabetes, thyroid disease, other cancer, and many more.…”

Section: Introductionmentioning

confidence: 99%

Predicting sun protection measures against skin diseases using machine learning approaches

Sultana

2021

J of Cosmetic Dermatology

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Background The substantial growth rate of skin cancer has necessitated adequate protection from solar radiation. Consequently, analyzing sun protection practices is an imperative research area in dermatology and pharmacology. Aims This paper aims to analyze public sun‐protection manners in the Arabian Peninsula regions. Methods A simple random survey was conducted to assess public sun protection manners. Artificial neural network (ANN) and support vector machine (SVM) were selected from several machine learning algorithms to create the models for predicting public sun protection measures based on the prediction accuracy. Model performances were evaluated based on several performance indicators depending on the confusion matrices and receiver operating characteristic curves. Results 51% of the respondents have a low level, and 49% have a high level of sun protection practices. The results showed that the SVM performed considerably amended than the ANN for predicting the response. The relative importance of the predictors for the best predictive SVM model was also analyzed. The predictors are ranked as: the number of times having sunburnt >gender > use seat belt while driving/riding a vehicle >considers the UV index for personal sun exposure >income based on the expenses >sports/exercise activities >consciousness of the chance for having sunburnt on extended exposure to the sun >age > having any skin problem >nationality > skin type. Conclusion These identified significant predictors might be considered for developing an effective policy to increase public consciousness using proper protection from solar radiation's detrimental effect to rule out skin diseases.

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Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery

Cited by 14 publications

References 44 publications

WSES project on decision support systems based on artificial neural networks in emergency surgery

WSES project on decision support systems based on artificial neural networks in emergency surgery

Artificial intelligence in hepatFIGopancreaticobiliary surgery: a systematic review

Predicting sun protection measures against skin diseases using machine learning approaches

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