The design and application of an automated microscope developed based on deep learning for fungal detection in dermatology

Abstract

Background Light microscopy to study the infection of fungi in skin specimens is time‐consuming and requires automation. Objective We aimed to design and explore the application of an automated microscope for fungal detection in skin specimens. Methods An automated microscope was designed, and a deep learning model was selected. Skin, nail and hair samples were collected. The sensitivity and the specificity of the automated microscope for fungal detection were calculated by taking the results of human inspectors as the gold standard. Results An automated microscope was built, and an image processing model based on the ResNet‐50 was trained. A total of 292 samples were collected including 236 skin samples, 50 nail samples and six hair samples. The sensitivities of the automated microscope for fungal detection in skin, nails and hair were 99.5%, 95.2% and 60%, respectively, and the specificities were 91.4%, 100% and 100%, respectively. Conclusion The automated microscope we developed is as skilful as human inspectors for fungal detection in skin and nail samples; however, its performance in hair samples needs to be improved.

Facilitated endospore detection for Bacillus spp. through automated algorithm‐based image processing

“…Moreover, this model can result in an automated spore and vegetative cell count and could be included atline or real-time in a bioprocess. Further development of the algorithm could also lead to a deep-learning model feeding an AI as described by Gao et al for fungal spore detection [36].…”

“…Further development of the algorithm could also lead to a deep‐learning model feeding an AI as described by Gao et al. for fungal spore detection [ 36 ].…”

“…Here, a combination of Schaeffer-Fulton staining and bright-field as well as fluorescence microscopy was used for spore detection [33]. Fully automated microscopy spore detection algorithms were reported for fungi by Lei et al [34], Korsnes et al [35] and Gao et al [36] based on microscopy spore detection.…”

Artificial Intelligence in microbiomes analysis: A review of applications in dermatology

“…However, due to the expansive areas involving skin and hair, the lesions are not concentrated. As a result, the low fungal content in an individual lesion hinders the recognition of mycelial characteristics by CNN, which can then decrease the diagnostic accuracy in these regions ( Gao et al, 2021 ). One approach to alleviate the deficiency of CNN to extract effective information from small-scale data sets, is to combine the CNN model with the attention mechanism (AM) to build an IL-MCAM framework.…”

The use of deep learning technology for the detection of optic neuropathy

“…DL can process more complex data and improve the algorithm of neural network performance, generalization ability, and cross-server distributed training ability, all of which are superior to the performance of shallow artificial neural networks (10). With the help of well-labeled, large data sets, DL can be used for accurate classification of medical images, and it has shown unique advantages in a variety of medical disciplines, including ultrasound (11), dermatology (12,13), pathology (14), radiology (15,16), and ophthalmology. In the field of ophthalmology, the DL system (DLS) has been developed for the detection of various diseases, such as diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity and cardiovascular diseases.…”