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Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers.
Brief Funct Genomics
December 2024
Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa FL 33612, United States.
Objective: The primary objective of this study is to investigate various applications of artificial intelligence (AI) and statistical methodologies for analyzing and managing peritoneal metastases (PM) caused by gastrointestinal cancers.
Methods: Relevant keywords and search criteria were comprehensively researched on PubMed and Google Scholar to identify articles and reviews related to the topic. The AI approaches considered were conventional machine learning (ML) and deep learning (DL) models, and the relevant statistical approaches included biostatistics and logistic models.
Deep learning-based automated diagnosis of temporomandibular joint anterior disc displacement and its clinical application.
Front Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
Predicting epidermal growth factor receptor mutation status of lung adenocarcinoma based on PET/CT images using deep learning.
Front Oncol
December 2024
Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Background: The aim of this study is to develop deep learning models based on F-fluorodeoxyglucose positron emission tomography/computed tomographic (F-FDG PET/CT) images for predicting individual epidermal growth factor receptor () mutation status in lung adenocarcinoma (LUAD).
Methods: We enrolled 430 patients with non-small-cell lung cancer from two institutions in this study. The advanced Inception V3 model to predict EGFR mutations based on PET/CT images and developed CT, PET, and PET + CT models was used.
Transcranial ultrasound stimulation effect in the redundant and synergistic networks consistent across macaques.
Netw Neurosci
December 2024
Precision Imaging, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
Low-intensity transcranial ultrasound stimulation (TUS) is a noninvasive technique that safely alters neural activity, reaching deep brain areas with good spatial accuracy. We investigated the effects of TUS in macaques using a recent metric, the synergy minus redundancy rank gradient, which quantifies different kinds of neural information processing. We analyzed this high-order quantity on the fMRI data after TUS in two targets: the supplementary motor area (SMA-TUS) and the frontal polar cortex (FPC-TUS).
View Article and Find Full Text PDFBackground: Skin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black-box nature of many models limits interpretability, posing challenges for dermatologists.
Methods: To address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization.
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