In this editorial,we comment on the article by Wang and Long,published in a recent issue of the World Journal of Clinical Cases.The article addresses the challenge of predicting intensive care unit-acquired weakness(I...In this editorial,we comment on the article by Wang and Long,published in a recent issue of the World Journal of Clinical Cases.The article addresses the challenge of predicting intensive care unit-acquired weakness(ICUAW),a neuromuscular disorder affecting critically ill patients,by employing a novel processing strategy based on repeated machine learning.The editorial presents a dataset comprising clinical,demographic,and laboratory variables from intensive care unit(ICU)patients and employs a multilayer perceptron neural network model to predict ICUAW.The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW.This editorial contributes to the growing body of literature on predictive modeling in critical care,offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.展开更多
Computerized tomography (CT) scan is the only screening test recommended by doctors to look for lung cancer. Convolutional neural networks (CNNs) have recently proven their ability to successfully classify medical ima...Computerized tomography (CT) scan is the only screening test recommended by doctors to look for lung cancer. Convolutional neural networks (CNNs) have recently proven their ability to successfully classify medical images. Due to its strong compactness property, the Discrete Wavelet transform (DWT) has been commonly used in image feature extraction applications. This paper presents a novel technique for the classification of Lung cancer in Computerized Tomography (CT) scans using Wavelets to find discriminative features in the CT images and CNN to classify the extracted features. Experimental results prove that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.5%.展开更多
The artificial neural network method has been applied to the relationship between the atomic parameters and intemction packeters of binary alloy Phases, and the principle of thermodynamics in combination with artifici...The artificial neural network method has been applied to the relationship between the atomic parameters and intemction packeters of binary alloy Phases, and the principle of thermodynamics in combination with artificial neural network method has been used for the computerized phase diagrams of continuous solid solution of bigamy alloy systems. The computerized phase diagrams well agree with the real phase diagmms.展开更多
In their recent study published in the World Journal of Clinical Cases,the article found that minimally invasive laparoscopic surgery under general anesthesia demonstrates superior efficacy and safety compared to trad...In their recent study published in the World Journal of Clinical Cases,the article found that minimally invasive laparoscopic surgery under general anesthesia demonstrates superior efficacy and safety compared to traditional open surgery for early ovarian cancer patients.This editorial discusses the integration of machine learning in laparoscopic surgery,emphasizing its transformative po-tential in improving patient outcomes and surgical precision.Machine learning algorithms analyze extensive datasets to optimize procedural techniques,enhance decision-making,and personalize treatment plans.Advanced imaging modalities like augmented reality and real-time tissue classification,alongside robotic surgical systems and virtual reality simulations driven by machine learning,enhance imaging and training techniques,offering surgeons clearer visualization and precise tissue manipulation.Despite promising advancements,challenges such as data privacy,algorithm bias,and regulatory hurdles need addressing for the responsible deployment of machine learning technologies.Interdisciplinary collaborations and ongoing technological innovations promise further enha-ncement in laparoscopic surgery,fostering a future where personalized medicine and precision surgery redefine patient care.展开更多
文摘In this editorial,we comment on the article by Wang and Long,published in a recent issue of the World Journal of Clinical Cases.The article addresses the challenge of predicting intensive care unit-acquired weakness(ICUAW),a neuromuscular disorder affecting critically ill patients,by employing a novel processing strategy based on repeated machine learning.The editorial presents a dataset comprising clinical,demographic,and laboratory variables from intensive care unit(ICU)patients and employs a multilayer perceptron neural network model to predict ICUAW.The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW.This editorial contributes to the growing body of literature on predictive modeling in critical care,offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.
文摘Computerized tomography (CT) scan is the only screening test recommended by doctors to look for lung cancer. Convolutional neural networks (CNNs) have recently proven their ability to successfully classify medical images. Due to its strong compactness property, the Discrete Wavelet transform (DWT) has been commonly used in image feature extraction applications. This paper presents a novel technique for the classification of Lung cancer in Computerized Tomography (CT) scans using Wavelets to find discriminative features in the CT images and CNN to classify the extracted features. Experimental results prove that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.5%.
文摘The artificial neural network method has been applied to the relationship between the atomic parameters and intemction packeters of binary alloy Phases, and the principle of thermodynamics in combination with artificial neural network method has been used for the computerized phase diagrams of continuous solid solution of bigamy alloy systems. The computerized phase diagrams well agree with the real phase diagmms.
文摘In their recent study published in the World Journal of Clinical Cases,the article found that minimally invasive laparoscopic surgery under general anesthesia demonstrates superior efficacy and safety compared to traditional open surgery for early ovarian cancer patients.This editorial discusses the integration of machine learning in laparoscopic surgery,emphasizing its transformative po-tential in improving patient outcomes and surgical precision.Machine learning algorithms analyze extensive datasets to optimize procedural techniques,enhance decision-making,and personalize treatment plans.Advanced imaging modalities like augmented reality and real-time tissue classification,alongside robotic surgical systems and virtual reality simulations driven by machine learning,enhance imaging and training techniques,offering surgeons clearer visualization and precise tissue manipulation.Despite promising advancements,challenges such as data privacy,algorithm bias,and regulatory hurdles need addressing for the responsible deployment of machine learning technologies.Interdisciplinary collaborations and ongoing technological innovations promise further enha-ncement in laparoscopic surgery,fostering a future where personalized medicine and precision surgery redefine patient care.
