The rapid development of artificial intelligence poses a huge impact on health and has become a core driving force for the new generation of the scientific and technological revolution in the field of healthcare. Recently, artificial intelligence has been gradually applied in the field of parasitic diseases and parasitology, including disease diagnosis, prognosis prediction, prediction of transmission risk, intelligent identification of vectors and intermediate hosts, and disease prevention and control, which facilitates the progress towards elimination of parasitic diseases. In addition, artificial intelligence provides highly efficient tools and approaches for healthcare workers and researchers. This comment mainly reviews the application of artificial intelligence in the fields of parasitic diseases and parasitology.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.16250/j.32.1915.2024279 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Artificial Intelligence in Detecting and Segmenting Vertical Misfit of Prosthesis in Radiographic Images of Dental Implants: A Cross-Sectional Analysis.
Clin Oral Implants Res
January 2025
Department of Oral and Maxillofacial Radiology, School of Dentistry, Kashan University of Medical Sciences, Kashan, Iran.
Objective: This study evaluated ResNet-50 and U-Net models for detecting and segmenting vertical misfit in dental implant crowns using periapical radiographic images.
Methods: Periapical radiographs of dental implant crowns were classified by two experts based on the presence of vertical misfit (reference group). The misfit area was manually annotated in images exhibiting vertical misfit.
Efficient Carbon-Based Optoelectronic Synapses for Dynamic Visual Recognition.
Adv Sci (Weinh)
January 2025
Haiping Fang, School of Physics, East China University of Science and Technology, Shanghai, 20023, China.
The human visual nervous system excels at recognizing and processing external stimuli, essential for various physiological functions. Biomimetic visual systems leverage biological synapse properties to improve memory encoding and perception. Optoelectronic devices mimicking these synapses can enhance wearable electronics, with layered heterojunction materials being ideal materials for optoelectronic synapses due to their tunable properties and biocompatibility.
View Article and Find Full Text PDFStimuli-Responsive Nano Drug Delivery Systems for the Treatment of Neurological Diseases.
Small
January 2025
Molecular Imaging Center, National Center for Drug Screening, Stake Key Laboratory of Chemical Biology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, P. R. China.
Nanomaterials with unparalleled physical and chemical attributes have become a cornerstone in the field of nanomedicine delivery. These materials can be engineered into various functionalized nanocarriers, which have become the focus of research. Stimulus-responsive nanodrug delivery systems (SRDDS) stand out as a sophisticated class of nanocarriers that can release drugs in response to environmental cues.
View Article and Find Full Text PDFDisentangling Effects of Vegetation Structure and Physiology on Land-Atmosphere Coupling.
Glob Chang Biol
January 2025
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.
Terrestrial vegetation is a key component of the Earth system, regulating the exchange of carbon, water, and energy between land and atmosphere. Vegetation affects soil moisture dynamics by absorbing and transpiring soil water, thus modulating land-atmosphere interactions. Moreover, changes in vegetation structure (e.
View Article and Find Full Text PDFPredicting Survival Rates in Brain Metastases Patients from Non-Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity.
Adv Sci (Weinh)
January 2025
The department of oncology, Xiangya Hospital, Central South University, Changsha, 410008, China.
Non-small cell lung cancer (NSCLC) frequently metastasizes to the brain, significantly worsened prognoses. This study aimed to develop an interpretable model for predicting survival in NSCLC patients with brain metastases (BM) integrating radiomic features and RNA sequencing data. 292 samples are collected and analyzed utilizing T1/T2 MRIs.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!