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Related Concept Videos

Skin Diseases and Disorders01:23

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Skin is the first line of defense and encounters a variety of microbes. Some pathogenic strains are often the cause of a broad range of infections of the skin and other body systems. These conditions can affect people of all ages and may have different causes, including genetic factors, infections, autoimmune reactions, environmental factors, and lifestyle choices.
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Fungal Group Zygomycota01:29

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Zygomycota, previously classified as a distinct fungal group, are primarily terrestrial, saprophytic molds that play a crucial role as decomposers. Recent phylogenetic studies have revealed that these fungi are now divided into two major clades — Mucoromycota, which includes many symbiotic species, and Zoopagomycota, which primarily consists of parasitic and pathogenic fungi. These groups exhibit distinct ecological roles and reproductive strategies while sharing key structural and...
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Fungal Phylum Microsporidia01:28

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Microsporidia are a group of obligate intracellular fungi that were initially classified as protists but were later reclassified based on phylogenetic, molecular, and structural evidence linking them to the Chytridiomycota. These unicellular, non-motile organisms are highly specialized parasites that infect a wide range of animal hosts, including humans. They have evolved extensive genomic and metabolic reductions, making them highly dependent on their hosts for survival.Morphology and Genomic...
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Isolation, Characterization, and Total DNA Extraction to Identify Endophytic Fungi in Mycoheterotrophic Plants
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Diagnosing onychomycosis: A step forward?

Aditya K Gupta1,2, Deanna C Hall1

  • 1Mediprobe Research Inc., London, ON, Canada.

Journal of Cosmetic Dermatology
|December 17, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can diagnose onychomycosis from images as well as dermatologists. However, AI diagnostic accuracy is limited by image quality and lack of clinical data, requiring expert oversight.

Keywords:
artificial intelligenceconvolutional neural networksdeep learningdiagnosticsonychomycosis

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Onychomycosis diagnosis relies on various methods, with ongoing technological advancements.
  • Artificial intelligence (AI) is increasingly utilized as a diagnostic tool in dermatology.
  • This review specifically examines AI applications for diagnosing onychomycosis.

Purpose of the Study:

  • To review common diagnostic methods for onychomycosis.
  • To explore the role of AI in dermatological diagnostics.
  • To analyze research on AI-based onychomycosis diagnosis.

Main Methods:

  • Convolutional neural networks (CNNs) were implemented in reviewed studies.
  • AI models analyzed datasets of nail images and histological images.
  • Diagnostic decisions were made based on learned information from image datasets.

Main Results:

  • AI demonstrated diagnostic capabilities for onychomycosis comparable to human dermatologists.
  • AI model performance was constrained by the quality of input images.
  • Exclusion of clinical data in AI analysis presents a significant limitation.

Conclusions:

  • AI shows potential to enhance diagnostic efficiency and reduce costs in onychomycosis detection.
  • Optimal use of AI in diagnosis necessitates the precision and expertise of dermatologists.
  • AI serves as a valuable adjunct tool, not a replacement for professional dermatological judgment.