External validation of deep learning algorithms for radiologic diagnosis: a systematic review
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …
diagnosis. Materials and Methods In this systematic review, the PubMed database was …
Machine learning applied to diagnosis of human diseases: A systematic review
Human healthcare is one of the most important topics for society. It tries to find the correct
effective and robust disease detection as soon as possible to patients receipt the …
effective and robust disease detection as soon as possible to patients receipt the …
Deep learning for rare disease: A sco** review
Although individually rare, collectively more than 7,000 rare diseases affect about 10% of
patients. Each of the rare diseases impacts the quality of life for patients and their families …
patients. Each of the rare diseases impacts the quality of life for patients and their families …
Applicable artificial intelligence for brain disease: A survey
Brain diseases threaten hundreds of thousands of people over the world. Medical imaging
techniques such as MRI and CT are employed for various brain disease studies. As artificial …
techniques such as MRI and CT are employed for various brain disease studies. As artificial …
Screening of Moyamoya Disease From Retinal Photographs: Development and Validation of Deep Learning Algorithms
BACKGROUND: Moyamoya disease (MMD) is a rare and complex pathological condition
characterized by an abnormal collateral circulation network in the basal brain. The diagnosis …
characterized by an abnormal collateral circulation network in the basal brain. The diagnosis …
Artificial intelligence in neuroimaging: clinical applications
Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in
image recognition tasks. Over the past decade, AI has proven its feasibility for applications in …
image recognition tasks. Over the past decade, AI has proven its feasibility for applications in …
Can additional patient information improve the diagnostic performance of deep learning for the interpretation of knee osteoarthritis severity
DH Kim, KJ Lee, D Choi, JI Lee, HG Choi… - Journal of Clinical …, 2020 - mdpi.com
The study compares the diagnostic performance of deep learning (DL) with that of the former
radiologist reading of the Kellgren–Lawrence (KL) grade and evaluates whether additional …
radiologist reading of the Kellgren–Lawrence (KL) grade and evaluates whether additional …
Contemporary and emerging magnetic resonance imaging methods for evaluation of moyamoya disease
VT Lehman, PM Cogswell, L Rinaldo, W Brinjikji… - Neurosurgical …, 2019 - thejns.org
Numerous recent technological advances offer the potential to substantially enhance the
MRI evaluation of moyamoya disease (MMD). These include high-resolution volumetric …
MRI evaluation of moyamoya disease (MMD). These include high-resolution volumetric …
Application of deep learning algorithm in the recognition of cryptococcosis and talaromycosis skin lesions
W Wei, X He, X Bao, G Wang, Q Luo, L Chen… - Mycoses, 2023 - Wiley Online Library
Background Cryptococcosis and talaromycosis are known as 'neglected epidemics' due to
their high case fatality rates and low concern. Clinically, the skin lesions of the two fungal …
their high case fatality rates and low concern. Clinically, the skin lesions of the two fungal …
Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs
OBJECTIVE Magnetic resonance imaging (MRI) is the gold-standard tool for diagnosing
lumbar spinal stenosis (LSS), but it is difficult to promptly examine all suspected cases with …
lumbar spinal stenosis (LSS), but it is difficult to promptly examine all suspected cases with …