Correction: EFFNet: A skin cancer classification model based on feature fusion and random forests.

[This corrects the article DOI: 10.1371/journal.pone.

Comparing similarities and differences between NAFLD, MAFLD, and MASLD in the general U.S. population.

Background: Recently, the multisociety Delphi consensus renamed non-alcoholic fatty liver disease (NAFLD) terminology [previously renamed metabolic-associated fatty liver disease (MAFLD)] as metabolic dysfunction-associated steatotic liver disease (MASLD). The aim of this study was to compare the similarities and differences between NAFLD, MAFLD, and MASLD and to clarify the impact of this new name change.

Methods: A cross-sectional study of 3,035 general subjects with valid vibration-controlled transient elastography data was conducted based on data from the National Health and Nutrition Examination Survey (NHANES) 2017-2020.

Assessment of the clinical value of five noninvasive predictors of metabolic dysfunction-associated steatotic liver disease in Han Chinese adults.

Background: Fatty Liver Index (FLI), Triglyceride-Glucose Index (TyG), Lipid Accumulation Product (LAP), Zhejiang University Index (ZJU), and Visceral Adiposity Index (VAI) are five classical predictive models for fatty liver disease. Our cross-sectional study aimed to identify the optimal predictors by comparing the predictive value of five models for metabolic dysfunction-associated steatotic liver disease (MASLD) risk.

Methods: Data on 2687 participants were collected from West China Hospital of Sichuan University.

An improved V-Net lung nodule segmentation model based on pixel threshold separation and attention mechanism.

Accurate labeling of lung nodules in computed tomography (CT) images is crucial in early lung cancer diagnosis and before nodule resection surgery. However, the irregular shape of lung nodules in CT images and the complex lung environment make it much more challenging to segment lung nodules accurately. On this basis, we propose an improved V-Net segmentation method based on pixel threshold separation and attention mechanism for lung nodules.

EFFNet: A skin cancer classification model based on feature fusion and random forests.

Computer-aided diagnosis techniques based on deep learning in skin cancer classification have disadvantages such as unbalanced datasets, redundant information in the extracted features and ignored interactions of partial features among different convolutional layers. In order to overcome these disadvantages, we propose a skin cancer classification model named EFFNet, which is based on feature fusion and random forests. Firstly, the model preprocesses the HAM10000 dataset to make each category of training set images balanced by image enhancement technology.

Comparison of the diagnostic performance of twelve noninvasive scores of metabolic dysfunction-associated fatty liver disease.

Background: The absence of distinct symptoms in the majority of individuals with metabolic dysfunction-associated fatty liver disease (MAFLD) poses challenges in identifying those at high risk, so we need simple, efficient and cost-effective noninvasive scores to aid healthcare professionals in patient identification. While most noninvasive scores were developed for the diagnosis of nonalcoholic fatty liver disease (NAFLD), consequently, the objective of this study was to systematically assess the diagnostic ability of 12 noninvasive scores (METS-IR/TyG/TyG-WC/TyG-BMI/TyG-WtHR/VAI/HSI/FLI/ZJU/FSI/K-NAFLD) for MAFLD.

Methods: The study recruited eligible participants from two sources: the National Health and Nutrition Examination Survey (NHANES) 2017-2020.

Development and validation of a new nomogram to screen for MAFLD.

Background And Aim: Metabolic dysfunction-associated fatty liver disease (MAFLD) poses significant health and economic burdens on all nations. Thus, identifying patients at risk early and managing them appropriately is essential. This study's goal was to develop a new predictive model for MAFLD.