AIMC Topic: Microscopy

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Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning.

Nature communications Dec 30, 2024
Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make the spatial resolution of photoacoustic microscopy significantly poorer than that of conventional conf...
Photoacoustic Techniques Microscopy, Confocal Fibroblasts Unsupervised Machine Learning Microscopy Cell Nucleus Infrared Rays Humans Deep Learning Image Processing, Computer-Assisted
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Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models.

Sensors (Basel, Switzerland) Dec 21, 2024
Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to...
Microscopy Leishmania Humans Machine Learning Algorithms Image Processing, Computer-Assisted Support Vector Machine Leishmaniasis, Cutaneous
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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...
Swine Diseases Deep Learning Feces Animals Swine Eimeria Coccidiosis Isospora India Image Processing, Computer-Assisted Microscopy Coccidia Isosporiasis
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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...
Datasets as Topic Optical Imaging Mouth Mucosa Early Detection of Cancer Multimodal Imaging Biopsy Mouth Neoplasms Microscopy Adult Image Interpretation, Computer-Assisted Humans Deep Learning Papanicolaou Test
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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....
Humans Artificial Intelligence Automation, Laboratory Microscopy Malaria, Falciparum Sensitivity and Specificity Plasmodium falciparum Malaria Parasitemia
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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...
Image Processing, Computer-Assisted Candida Artificial Intelligence Humans Candidiasis, Vulvovaginal Microscopy Neural Networks, Computer Female Diagnosis, Computer-Assisted
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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...
Feces Image Processing, Computer-Assisted Microscopy Diagnosis, Computer-Assisted Humans Deep Learning Parasite Egg Count Animals Convolutional Neural Networks Intestinal Diseases, Parasitic
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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...
Schistosoma haematobium Schistosomiasis haematobia Microscopy Urine Artificial Intelligence Animals Humans Image Processing, Computer-Assisted
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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...
Artificial Intelligence Primary Health Care Microscopy Scoping Reviews As Topic Humans
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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...
Neural Networks, Computer Image Processing, Computer-Assisted Smartphone Plant Leaves Fruit Solanum lycopersicum Microscopy Plant Diseases
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