[HTML][HTML] Multi-modality approaches for medical support systems: A systematic review of the last decade
Healthcare traditionally relies on single-modality approaches, which limit the information
available for medical decisions. However, advancements in technology and the availability …
available for medical decisions. However, advancements in technology and the availability …
Multimodal machine learning in precision health: A sco** review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
Integrated multimodal artificial intelligence framework for healthcare applications
Artificial intelligence (AI) systems hold great promise to improve healthcare over the next
decades. Specifically, AI systems leveraging multiple data sources and input modalities are …
decades. Specifically, AI systems leveraging multiple data sources and input modalities are …
Osteoarthritis endotype discovery via clustering of biochemical marker data
Objectives Osteoarthritis (OA) patient stratification is an important challenge to design
tailored treatments and drive drug development. Biochemical markers reflecting joint tissue …
tailored treatments and drive drug development. Biochemical markers reflecting joint tissue …
Artificial intelligence and deep learning for rheumatologists
Deep learning has emerged as the leading method in machine learning, spawning a rapidly
growing field of academic research and commercial applications across medicine. Deep …
growing field of academic research and commercial applications across medicine. Deep …
[HTML][HTML] Machine learning in knee osteoarthritis: A review
Objective The purpose of present review paper is to introduce the reader to key directions of
Machine Learning techniques on the diagnosis and predictions of knee osteoarthritis …
Machine Learning techniques on the diagnosis and predictions of knee osteoarthritis …
[Retracted] Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches
Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that
can be captured by imaging modalities and translated into imaging features. Observing …
can be captured by imaging modalities and translated into imaging features. Observing …
Automatic grading of individual knee osteoarthritis features in plain radiographs using deep convolutional neural networks
Knee osteoarthritis (OA) is the most common musculoskeletal disease in the world. In
primary healthcare, knee OA is diagnosed using clinical examination and radiographic …
primary healthcare, knee OA is diagnosed using clinical examination and radiographic …
Automatic detection and classification of knee osteoarthritis using deep learning approach
Purpose We developed a tool for locating and grading knee osteoarthritis (OA) from digital X-
ray images and illustrate the possibility of deep learning techniques to predict knee OA as …
ray images and illustrate the possibility of deep learning techniques to predict knee OA as …
[HTML][HTML] Review of multimodal machine learning approaches in healthcare
Abstract Machine learning methods in healthcare have traditionally focused on using data
from a single modality, limiting their ability to effectively replicate the clinical practice of …
from a single modality, limiting their ability to effectively replicate the clinical practice of …