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Recognition of esophagitis in endoscopic images using transfer learning

World Journal of Gastrointestinal Endoscopy 2022年第5期14卷 311-319页

作者： Elena Caires Silveira Caio Fellipe Santos Corrêa Leonardo Madureira Silva Bruna Almeida Santos soraya mattos pretti Fabrício Freire de Melo Multidisciplinary Institute of Health Federal University of BahiaVitória da Conquista 45029-094BahiaBrazil

BACKGROUND Esophagitis is an inflammatory and damaging process of the esophageal mucosa,which is confirmed by endoscopic visualization and may,in extreme cases,result in stenosis,fistulization and esophageal *** use of deep learning(a field of artificial intelligence)techniques can be considered to determine the presence of esophageal lesions compatible with *** To develop,using transfer learning,a deep neural network model to recognize the presence of esophagitis in endoscopic *** Endoscopic images of 1932 patients with a diagnosis of esophagitis and 1663 patients without any pathological diagnosis provenient from the KSAVIR and HyperKSAVIR datasets were splitted in training(80%)and test(20%)and used to develop and evaluate a binary deep learning classifier built using the DenseNet-201 architecture,a densely connected convolutional network,with weights pretrained on the ImageNet image set and fine-tuned during *** classifier model performance was evaluated in the test set according to accuracy,sensitivity,specificity and area under the receiver operating characteristic curve(AUC).RESULTS The model was trained using Adam optimizer with a learning rate of 0.0001 and applying binary cross entropy loss *** the test set(n=719),the classifier achieved 93.32%accuracy,93.18%sensitivity,93.46%specificity and a 0.96 *** for spatial predictive relevance in esophagitis endoscopic images from the test set were also *** face of the obtained results,the use of dense convolutional neural networks with pretrained and fine-tuned weights proves to be a good strategy for predictive modeling for esophagitis recognition in endoscopic *** addition,adopting the classification approach combined with the subsequent plotting of heat maps associated with the classificatory decision gives greater explainability to the *** It is opportune to raise new studies involving transfer learning for the analysis of en

关键词： Esophagitis Endoscopy Artificial intelligence Deep learning Transfer learning

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Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach

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World Journal of Critical Care Medicine 2022年第5期11卷 317-329页

作者： Elena Caires Silveira soraya mattos pretti Bruna Almeida Santos Caio Fellipe Santos Correa Leonardo Madureira Silva Fabricio Freire de Melo Multidisciplinary Institute of Health Federal University of BahiaVitória da Conquista 45-029094Brazil

BACKGROUND Intensive care unit(ICU)patients demand continuous monitoring of several clinical and laboratory parameters that directly influence their medical progress and the staff’s *** data are vital in the assistance of these patients,being already used by several scoring *** this context,machine learning approaches have been used for medical predictions based on clinical data,which includes patient *** To develop a binary classifier for the outcome of death in ICU patients based on clinical and laboratory parameters,a set formed by 1087 instances and 50 variables from ICU patients admitted to the emergency department was obtained in the“WiDS(Women in Data Science)Datathon 2020:ICU Mortality Prediction”*** For categorical variables,frequencies and risk ratios were *** variables were computed as means and standard deviations and Mann-Whitney U tests were *** then divided the data into a training(80%)and test(20%)*** training set was used to train a predictive model based on the Random Forest algorithm and the test set was used to evaluate the predictive effectiveness of the *** A statistically significant association was identified between need for intubation,as well predominant systemic cardiovascular involvement,and hospital death.A number of the numerical variables analyzed(for instance Glasgow Coma Score punctuations,mean arterial pressure,temperature,pH,and lactate,creatinine,albumin and bilirubin values)were also significantly associated with death *** proposed binary Random Forest classifier obtained on the test set(n=218)had an accuracy of 80.28%,sensitivity of 81.82%,specificity of 79.43%,positive predictive value of 73.26%,negative predictive value of 84.85%,F1 score of 0.74,and area under the curve score of *** predictive variables of the greatest importance were the maximum and minimum lactate values,adding up to a predictive importance of 15.54%.CONCLUSIO

关键词： Hospital mortality Machine learning Patient outcome assessment Routinely collected health data Intensive care units Critical care outcomes

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