Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy

Superficial Fungal Infections and Artificial Intelligence: A Review on Current Advances and Opportunities: REVISION

BACKGROUND

Superficial fungal infections are among the most common infections in world, they mainly affect skin, nails and scalp without further invasion. Superficial fungal diseases are conventionally diagnosed with direct microscopy, fungal culture or histopathology, treated with topical or systemic antifungal agents and prevented in immunocompetent patients by improving personal hygiene. However, conventional diagnostic tests can be time-consuming, also treatment can be insufficient or ineffective and prevention can prove to be demanding. Artificial Intelligence (AI) refers to a digital system having an intelligence akin to a human being. The concept of AI has existed since 1956, but hasn't been practicalised until recently. AI has revolutionised medical research in the recent years, promising to influence almost all specialties of medicine.

OBJECTIVE

An increasing number of articles have been published about the usage of AI in cutaneous mycoses.

METHODS

In this review, the key findings of articles about utilisation of AI in diagnosis, treatment and prevention of superficial fungal infections are summarised. Moreover, the need for more research and development is highlighted.

RESULTS

Fifty-four studies were reviewed. Onychomycosis was the most researched superficial fungal infection. AI can be used diagnosing fungi in macroscopic and microscopic images and classify them to some extent. AI can be a tool and be used as a part of something bigger to diagnose superficial mycoses.

CONCLUSION

AI can be used in all three steps of diagnosing, treating and preventing. AI can be a tool complementary to the clinician's skills and laboratory results.

Label-Free Optical Transmission Tomography for Direct Mycological Examination and Monitoring of Intracellular Dynamics

Live-cell imaging generally requires pretreatment with fluorophores to either monitor cellular functions or the dynamics of intracellular processes and structures. We have recently introduced full-field optical coherence tomography for the label-free live-cell imaging of fungi with potential clinical applications for the diagnosis of invasive fungal mold infections. While both the spatial resolution and technical set up of this technology are more likely designed for the histopathological analysis of tissue biopsies, there is to our knowledge no previous work reporting the use of a light interference-based optical technique for direct mycological examination and monitoring of intracellular processes. We describe the first application of dynamic full-field optical transmission tomography (D-FF-OTT) to achieve both high-resolution and live-cell imaging of fungi. First, D-FF-OTT allowed for the precise examination and identification of several elementary structures within a selection of fungal species commonly known to be responsible for invasive fungal infections such as Candida albicans, Aspergillus fumigatus, or Rhizopus arrhizus. Furthermore, D-FF-OTT revealed the intracellular trafficking of organelles and vesicles related to metabolic processes of living fungi, thus opening new perspectives in fast fungal infection diagnostics.

Vision Transformer Based Detection Of Chronic Pulmonary Aspergillosis Lung Infections In Chest X-Ray Images

Chronic pulmonary aspergillosis (CPA) is a lung infection caused by the fungus Aspergillus. The infection can occur when the immune system is compromised or there is damage to the lung tissue. Due to its rarity, physicians often do not consider CPA as an initial diagnosis and may not perform relevant tests, leading to a lack of targeted treatment in the early stages of the disease and potentially affecting the effectiveness of subsequent treatments. This paper employs a model based on the Vision Transformer (ViT) and utilizes the self-supervised DINO framework to train a classifier for determining whether a given chest X-ray (CXR) image belongs to a patient with CPA. To the best of our knowledge, there is currently no deep-learning based study for the diagnosis of CPA using CXR images. We demonstrate the effectiveness of ViT trained with the DINO framework for classifying CXR images using both in-house and public datasets. For results in three-class classification (normal, CPA, non-CPA-abnormal), we can achieve macro F1 of 0.768. For binary classification (normal vs. CPA), we obtain macro F1 of 0.935, with recall of 0.803 and precision of 0.953 for the CPA class.

Current status and new experimental diagnostic methods of invasive fungal infections after hematopoietic stem cell transplantation

Invasive fungal infections (IFIs) are common and life-threatening complications in post-hematopoietic stem cell transplantation (post-HSCT) recipients, Severe IFIs can lead to systemic infection and organ damage, which results in high mortality in HSCT recipients. With the development of the field of fungal infection diagnosis, more and more advanced non-culture diagnostic tools have been developed, such as glip biosensors, metagenomic next-generation sequencing, Magnetic Nanoparticles and Identified Using SERS via AgNPs+ , and artificial intelligence-assisted diagnosis. The advanced diagnostic approaches contribute to the success of HSCT and improve the overall survival of post-HSCT leukemia patients by supporting therapeutical decisions. This review provides an overview of the characteristics of two high-incidence IFIs in post-HSCT recipients and discusses some of the recently developed IFI detection technologies. Additionally, it explores the potential application of cationic conjugated polymer fluorescence resonance energy transfer (CCP-FRET) technology for IFI detection. The aim is to offer insights into selecting appropriate IFI detection methods and gaining an understanding of novel fungal diagnostic approaches in laboratory settings.

Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal 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 specialized computing solutions. The data are big in terms of volume and multimodal with the amalgamation of images, text and structure. Machine learning has identified antifungal drug targets, and taxonomic and phylogenetic classification of fungi based on sequence analysis is now available. Real-time identification tools and user-friendly mobile applications for identifying fungi have been discovered. Akin to histopathology, AI can be applied to fungal cytology. AI has been fruitful in cytopathology of the thyroid gland, breast, urine and uterine cervical lesions. AI has a huge scope in fungal cytology and would certainly bear fruit with its accuracy, reproducibility and capacity for handling big data. The purpose of this systematic review was to highlight the AI's utility in detecting fungus and its typing with a special focus on future application in fungal cytology. We also touch upon the basics of AI in brief.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Attention-Guided Transfer Learning for Identification of Filamentous Fungi Encountered in the Clinical Laboratory

This study addresses the challenge of accurately identifying filamentous fungi in medical laboratories using transfer learning with convolutional neural networks (CNNs). The study uses microscopic images from touch-tape slides with lactophenol cotton blue staining, the most common method in clinical settings, to classify fungal genera and identify Aspergillus species. The training and test data sets included 4,108 images with representative microscopic morphology for each genus, and a soft attention mechanism was incorporated to enhance classification accuracy. As a result, the study achieved an overall classification accuracy of 94.9% for four frequently encountered genera and 84.5% for Aspergillus species. One of the distinct features is the involvement of medical technologists in developing a model that seamlessly integrates into routine workflows. In addition, the study highlights the potential of merging advanced technology with medical laboratory practices to diagnose filamentous fungi accurately and efficiently. IMPORTANCE This study utilizes transfer learning with CNNs to classify fungal genera and identify Aspergillus species using microscopic images from touch-tape preparation and lactophenol cotton blue staining. The training and test data sets included 4,108 images with representative microscopic morphology for each genus, and a soft attention mechanism was incorporated to enhance classification accuracy. As a result, the study achieved an overall classification accuracy of 94.9% for four frequently encountered genera and 84.5% for Aspergillus species. One of the distinct features is the involvement of medical technologists in developing a model that seamlessly integrates into routine workflows. In addition, the study highlights the potential of merging advanced technology with medical laboratory practices to diagnose filamentous fungi accurately and efficiently.

A Small Sample Recognition Model for Poisonous and Edible Mushrooms based on Graph Convolutional Neural Network

The automatic identification of disease types of edible mushroom crops and poisonous crops is of great significance for improving crop yield and quality. Based on the graph convolutional neural network theory, this paper constructs a graph convolutional network model for the identification of poisonous crops and edible fungi. By constructing 6 graph convolutional networks with different depths, the model uses the training mechanism of graph convolutional networks to analyze the results of disease identification and completes the automatic extraction of the disease characteristics of the poisonous crops by overfitting problem. During the simulation, firstly, the relevant PlantVillage dataset is used to obtain the pretrained model, and the parameters are adjusted to fit the dataset. The network framework is trained and parameterized with prior knowledge learned from large datasets and finally synthesized by training multiple neural network models and using direct averaging and weighting to synthesize their predictions. The experimental results show that the graph convolutional neural network model that integrates multi-scale category relationships and dense links can use dense connection technology to improve the representation ability and generalization ability of the model, and the accuracy rate generally increases by 1%-10%. The average recognition rate is about 91%, which greatly promotes the ability to identify the diseases of poisonous crops.

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995-2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.