AIMC Topic: Deep Learning

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Application of artificial intelligence in dental implant prognosis: A scoping review.

Journal of dentistry
OBJECTIVES: The purpose of this scoping review was to evaluate the performance of artificial intelligence (AI) in the prognosis of dental implants.

Deep-NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data.

CPT: pharmacometrics & systems pharmacology
Noncompartmental analysis (NCA) is a model-independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well-established and widely utilized, they suffer from low accuracies in the setting of sparse PK samp...

Assessing the Impact of Urban Environments on Mental Health and Perception Using Deep Learning: A Review and Text Mining Analysis.

Journal of urban health : bulletin of the New York Academy of Medicine
Understanding how outdoor environments affect mental health outcomes is vital in today's fast-paced and urbanized society. Recently, advancements in data-gathering technologies and deep learning have facilitated the study of the relationship between ...

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.

Cell reports. Medicine
Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions...

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

Journal of translational medicine
BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI...

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification.

Scientific reports
A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges a...

Enabling large-scale screening of Barrett's esophagus using weakly supervised deep learning in histopathology.

Nature communications
Timely detection of Barrett's esophagus, the pre-malignant condition of esophageal adenocarcinoma, can improve patient survival rates. The Cytosponge-TFF3 test, a non-endoscopic minimally invasive procedure, has been used for diagnosing intestinal me...

Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images.

PloS one
Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskelet...

Deep Learning-Based Spermatogenic Staging in Tissue Sections of Cynomolgus Macaque Testes.

Toxicologic pathology
The indirect assessment of adverse effects on fertility in cynomolgus monkeys requires that tissue sections of the testis be microscopically evaluated with awareness of the stage of spermatogenesis that a particular cross-section of a seminiferous tu...

A Combination Model of Shifting Joint Angle Changes With 3D-Deep Convolutional Neural Network to Recognize Human Activity.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Research in the field of human activity recognition is very interesting due to its potential for various applications such as in the field of medical rehabilitation. The need to advance its development has become increasingly necessary to enable effi...