AIMC Topic: Microscopy
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Development of deep learning-based mobile application for the identification of Coccidia species in pigs using microscopic images.
Veterinary parasitology
Dec 11, 2024
Coccidiosis is a gastrointestinal parasitic disease caused by different species of Eimeria and Isospora, poses a significant threat to pig farming, leading to substantial economic losses attributed to reduced growth rates, poor feed conversion, incre...
Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection.
Computers in biology and medicine
Dec 10, 2024
BACKGROUND AND OBJECTIVES: Oral cancer is a global health challenge. The disease can be successfully treated if detected early, but the survival rate drops significantly for late stage cases. There is a growing interest in a shift from the current st...
Diagnosis of infections using artificial intelligence techniques versus standard microscopy in a reference laboratory.
Journal of clinical microbiology
Dec 10, 2024
Diagnosing malaria using standard techniques is time-consuming. With limited staffing in many laboratories, this may lead to delays in reporting. Innovative technologies are changing the diagnostic landscape and may help alleviate staffing shortages....
AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks.
Microbiology spectrum
Nov 22, 2024
UNLABELLED: Vulvovaginal candidiasis (VVC) is a prevalent fungal ailment affecting women globally. Timely and accurate diagnosis is crucial. Traditional methods, relying on clinical evaluation and manual microscopic examination, have limitations. Art...
A lightweight deep-learning model for parasite egg detection in microscopy images.
Parasites & vectors
Nov 6, 2024
BACKGROUND: Intestinal parasitic infections are still a serious public health problem in developing countries, and the diagnosis of parasitic infections requires the first step of parasite/egg detection of samples. Automated detection can eliminate t...
Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.
PLoS neglected tropical diseases
Nov 5, 2024
BACKGROUND: Urogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to affect 150 million people worldwide, with a high relevance in resource-poor settings of the African con...
AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Protocol for a Scoping Review.
JMIR research protocols
Nov 1, 2024
BACKGROUND: Digital microscopy combined with artificial intelligence (AI) is increasingly being implemented in health care, predominantly in advanced laboratory settings. However, AI-supported digital microscopy could be especially advantageous in pr...
TOMMicroNet: Convolutional Neural Networks for Smartphone-Based Microscopic Detection of Tomato Biotic and Abiotic Plant Health Issues.
Phytopathology
Oct 30, 2024
The image-based detection and classification of plant diseases has become increasingly important to the development of precision agriculture. We consider the case of tomato, a high-value crop supporting the livelihoods of many farmers around the worl...
Rapid On-Site Histology of Lung and Pleural Biopsies Using Higher Harmonic Generation Microscopy and Artificial Intelligence Analysis.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Oct 16, 2024
Lung cancer is one of the most prevalent and lethal cancers. To improve health outcomes while reducing health care burden, it becomes crucial to move toward early detection and cost-effective workflows. Currently, there is no method for the on-site r...
A novel ensemble approach with deep transfer learning for accurate identification of foodborne bacteria from hyperspectral microscopy.
Computational biology and chemistry
Oct 9, 2024
The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it can lead to dangerous cross-contamination. One of the most common methods for classifying bacteria is...
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