AIMC Topic: Deep Learning

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Automatic Assessment of Pectus Excavatum Severity From CT Images Using Deep Learning.

IEEE journal of biomedical and health informatics
Pectus excavatum (PE) is the most common abnormality of the thoracic cage, whose severity is evaluated by extracting three indices (Haller, correction and asymmetry) from computed tomography (CT) images. To date, this analysis is performed manually, ...

Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography.

Sensors (Basel, Switzerland)
Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based ...

Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray.

Sensors (Basel, Switzerland)
The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising ...

Deep learning approach to estimate foot pressure distribution in walking with application for a cost-effective insole system.

Journal of neuroengineering and rehabilitation
BACKGROUND: Foot pressure distribution can be used as a quantitative parameter for evaluating anatomical deformity of the foot and for diagnosing and treating pathological gait, falling, and pressure sores in diabetes. The objective of this study was...

Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.

BMC pregnancy and childbirth
BACKGROUND: Recently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the ...

A pilot study of a deep learning approach to detect marginal bone loss around implants.

BMC oral health
BACKGROUND: Recently, there has been considerable innovation in artificial intelligence (AI) for healthcare. Convolutional neural networks (CNNs) show excellent object detection and classification performance. This study assessed the accuracy of an a...

Detection of metallic objects on digital radiographs with convolutional neural networks: A MRI screening tool.

Radiography (London, England : 1995)
INTRODUCTION: Screening for metallic implants and foreign bodies before magnetic resonance imaging (MRI) examinations, are crucial for patient safety. History of health are supplied by the patient, a family member, screening of electronic health reco...

A novel method for micropollutant quantification using deep learning and multi-objective optimization.

Water research
Micropollutants (MPs) released into aquatic ecosystems have adverse effects on public health. Hence, monitoring and managing MPs in aquatic systems are imperative. MPs can be quantified by high-resolution mass spectrometry (HRMS) with stable isotope-...

Deep learning of early brain imaging to predict post-arrest electroencephalography.

Resuscitation
INTRODUCTION: Guidelines recommend use of computerized tomography (CT) and electroencephalography (EEG) in post-arrest prognostication. Strong associations between CT and EEG might obviate the need to acquire both modalities. We quantified these asso...

Deep Learning Application for Effective Classification of Different Types of Psoriasis.

Journal of healthcare engineering
Psoriasis is a chronic inflammatory skin disorder mediated by the immune response that affects a large number of people. According to latest worldwide statistics, 125 million individuals are suffering from psoriasis. Deep learning techniques have dem...