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ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.

PloS one
Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency o...

Assessing outcomes of ear molding therapy by health care providers and convolutional neural network.

Scientific reports
Ear molding therapy is a nonsurgical technique to correct certain congenital auricular deformities. While the advantages of nonsurgical treatments over otoplasty are well-described, few studies have assessed aesthetic outcomes. In this study, we comp...

Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study.

The Lancet. Digital health
BACKGROUND: Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We, therefore, aimed to develop and evaluate a machine learning-based screening te...

A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos.

Scientific reports
Routine dental visit is the most common approach to detect the gingivitis. However, such diagnosis can sometimes be unavailable due to the limited medical resources in certain areas and costly for low-income populations. This study proposes to screen...

Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks.

Nature communications
Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular de...

Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images.

Scientific reports
Cervical cancer is the second most common cancer in women worldwide with a mortality rate of 60%. Cervical cancer begins with no overt signs and has a long latent period, making early detection through regular checkups vitally immportant. In this stu...

Classification of large-scale image database of various skin diseases using deep learning.

International journal of computer assisted radiology and surgery
PURPOSE: The purpose of this study was to develop a deep learning-based computer-aided diagnosis system for skin disease classification using photographic images of patients. The targets are 59 skin diseases, including localized and diffuse diseases ...

Analysis of the performance of machine learning and deep learning methods for sex estimation of infant individuals from the analysis of 2D images of the ilium.

International journal of legal medicine
Reducing the subjectivity of the methods used for biological profile estimation is, at present, a priority research line in forensic anthropology. To achieve this, artificial intelligence (AI) techniques can be a valuable tool yet to be exploited in ...

Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone.

The British journal of ophthalmology
BACKGROUND/AIMS: To validate a deep learning algorithm to diagnose glaucoma from fundus photography obtained with a smartphone.