AIMC Topic: Mycoses

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Deep learning detection and classification of fungal and non-fungal calcifications on paranasal sinus CT imaging.

PloS one
This study aimed to develop and evaluate a deep learning algorithm for detecting and classifying intrasinus calcifications on paranasal sinus (PNS) computed tomography (CT) for the diagnosis of fungal sinusitis and differentiation of fungal and non-f...

Artificial Intelligence Driven Diagnosis and Prognosis Comparison of ChatGPT-4o and DeepSeek-R1 in HIV Negative Talaromycosis.

Mycopathologia
This study evaluates and compares the diagnostic and prognostic capabilities of ChatGPT-4o and DeepSeek-R1 in 56 HIV-negative talaromycosis cases. Clinical case fragments were de-identified and submitted to both models, with diagnostic accuracy and p...

Advancing fungal phylogenetics: integrating modern sequencing, dark taxa discovery, and machine learning.

Archives of microbiology
The study of fungal genetics has undergone transformative advancements in recent decades, profoundly reshaping our understanding of fungal diversity, evolution, and pathogenesis. This review synthesizes cutting-edge molecular techniques revolutionizi...

Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy.

BMC medical informatics and decision making
BACKGROUND: The flexible ureteroscopy lithotripsy (F-URL) is an important treatment for upper urinary tract stones. However, urolithiasis, surgical procedures, and catheter placement are risk factors for fungal infections. Our study aimed to construc...

Early differential diagnosis models of Talaromycosis and Tuberculosis in HIV-negative hosts using clinical data and machine learning.

Journal of infection and public health
BACKGROUND: Talaromyces marneffei is an emerging pathogen, and the number of infections in HIV-negative individuals is increasing. In HIV-negative individuals, talaromycosis is usually misdiagnosed as another disease, especially tuberculosis (TB).

Leveraging machine learning to uncover multi-pathogen infection dynamics across co-distributed frog families.

PeerJ
BACKGROUND: Amphibians are experiencing substantial declines attributed to emerging pathogens. Efforts to understand what drives patterns of pathogen prevalence and differential responses among species are challenging because numerous factors related...

The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: To assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist.

iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks.

Artificial intelligence in medicine
Globally, fungal infections have become a major health concern in humans. Fungal diseases generally occur due to the invading fungus appearing on a specific portion of the body and becoming hard for the human immune system to resist. The recent emerg...

Scedosporium boydii finding in an immunocompromised patient and review of the literature.

Revista iberoamericana de micologia
BACKGROUND: Scedosporiasis is an emerging mycosis that has gained importance in recent years due to its worldwide prevalence. It is caused by species of the Scedosporium apiospermum complex. These species can cause opportunistic infections in immunoc...

Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology.

Cytopathology : official journal of the British Society for Clinical Cytology
Artificial Intelligence (AI) is an emerging, transforming and revolutionary technology that has captured attention worldwide. It is translating research into precision oncology treatments. AI can analyse large or big data sets requiring high-speed sp...