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Smartphone

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Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms.

Journal of cancer research and clinical oncology
PURPOSE: Non-melanoma skin cancer (NMSC) is the most frequent keratinocyte-origin skin tumor. It is confirmed that dermoscopy of NMSC confers a diagnostic advantage as compared to visual face-to-face assessment. COVID-19 restrictions diagnostics by t...

Feasibility assessment of infectious keratitis depicted on slit-lamp and smartphone photographs using deep learning.

International journal of medical informatics
BACKGROUND: This study aims to investigate how infectious keratitis depicted on slit-lamp and smartphone photographs can be reliably assessed using deep learning.

Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning.

Scientific reports
Craniofacial anomaly including deformational plagiocephaly as a result of deformities in head and facial bones evolution is a serious health problem in newbies. The impact of such condition on the affected infants is profound from both medical and so...

Your mileage may vary: impact of data input method for a deep learning bone age app's predictions.

Skeletal radiology
OBJECTIVE: The purpose of this study was to evaluate agreement in predictions made by a bone age prediction application ("app") among three data input methods.

Fast and Accurate Ophthalmic Medication Bottle Identification Using Deep Learning on a Smartphone Device.

Ophthalmology. Glaucoma
PURPOSE: To assess the accuracy and efficacy of deep learning models, specifically convolutional neural networks (CNNs), to identify glaucoma medication bottles.

LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices.

Sensors (Basel, Switzerland)
The penetration of wearable devices in our daily lives is unstoppable. Although they are very popular, so far, these elements provide a limited range of services that are mostly focused on monitoring tasks such as fitness, activity, or health trackin...

Development of a smartphone-based lateral-flow imaging system using machine-learning classifiers for detection of Salmonella spp.

Journal of microbiological methods
Salmonella spp. are a foodborne pathogen frequently found in raw meat, egg products, and milk. Salmonella is responsible for numerous outbreaks, becoming a frequent major public-health concern. Many studies have recently reported handheld and rapid d...

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.

An Unsupervised Learning-Based Spatial Co-Location Detection System from Low-Power Consumption Sensor.

Sensors (Basel, Switzerland)
Spatial co-location detection is the task of inferring the co-location of two or more objects in the geographic space. Mobile devices, especially a smartphone, are commonly employed to accomplish this task with the human object. Previous work focused...