Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. Electronic address: xuwh@pumch.cn.
Department of Radiology and Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk National University Hospital, 567 Baekje-daero, deokjin-gu, Jeonju-si, Jeollabuk-do, 561-756, Republic of Korea. kwak8140@jbnu.ac.kr.
Department of Radiology and Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk National University Hospital, 567 Baekje-daero, deokjin-gu, Jeonju-si, Jeollabuk-do, 561-756, Republic of Korea.
Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, P.R. China.
Anatomy and Neuroscience (JC), School of Biomedical Sciences, University of Melbourne, Parkville; Department of Surgery (JC), Alfred Hospital, Melbourne, Victoria; Interventional Radiology Service (JM, MB, HA), Department of Radiology, Austin Hospital, Melbourne; School of Medicine (JM, MB, HA), Faculty of Health, Deakin University, Waurn Ponds; Stroke Division (JM, MB, HA), Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, Victoria; Interventional Neuroradiology Service (HA), Department of Radiology, St Vincent's Hospital; Interventional Neuroradiology Unit (RVC, L-AS, HA), Monash Imaging, Monash Health; and Faculty of Medicine (RVC, HA), Nursing and Health Sciences, Monash University, Melbourne, Australia.
Anatomy and Neuroscience (JC), School of Biomedical Sciences, University of Melbourne, Parkville; Department of Surgery (JC), Alfred Hospital, Melbourne, Victoria; Interventional Radiology Service (JM, MB, HA), Department of Radiology, Austin Hospital, Melbourne; School of Medicine (JM, MB, HA), Faculty of Health, Deakin University, Waurn Ponds; Stroke Division (JM, MB, HA), Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, Victoria; Interventional Neuroradiology Service (HA), Department of Radiology, St Vincent's Hospital; Interventional Neuroradiology Unit (RVC, L-AS, HA), Monash Imaging, Monash Health; and Faculty of Medicine (RVC, HA), Nursing and Health Sciences, Monash University, Melbourne, Australia.
Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent...
Quantifying the severity of head shape deformity and establishing a threshold for operative intervention remains challenging in patients with metopic craniosynostosis (MCS). This study combines three-...
Head computed tomography scans from subjects with MCS and normal controls (5 to 15 months of age) were used for objective three-dimensional shape analysis using ShapeWorks software and in a survey for...
One hundred twenty-four computed tomography scans were used to develop the model; 50 (24% MCS, 76% controls) were rated by 36 craniofacial surgeons, with an average of 20.8 ratings per skull. The inte...
This study describes a novel metric to quantify the head shape deformity associated with MCS and contextualizes the results using clinical assessments of head shapes by craniofacial experts. This metr...
To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these ...
We studied radiological disease progression in 76 patients with IPF, including overall 190 computed tomography (CT) examinations of the chest. An algorithm identified candidates for imaging patterns m...
Progression marker patterns were identified and exhibited high stability in a repeatability experiment with 20 random sub-cohorts of the overall cohort. The 4 top-ranked progression markers were consi...
Unsupervised learning can identify radiological disease progression markers that predict outcome. Local tracking of pattern transitions reveals pathways of radiological disease progression from health...
• Unsupervised learning can identify radiological disease progression markers that predict outcome in patients with idiopathic pulmonary fibrosis. • Local tracking of pattern transitions reveals pathw...
Treatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images woul...
New CT emphysema subtypes were identified by unsupervised machine learning on only the texture and location of emphysematous regions on CT scans from 2853 participants in the Subpopulations and Interm...
The algorithm discovered six reproducible (interlearner intraclass correlation coefficient, 0.91-1.00) CT emphysema subtypes. The most common subtype in SPIROMICS, the combined bronchitis-apical subty...
Large-scale unsupervised machine learning on CT scans defined six reproducible, familiar CT emphysema subtypes that suggest paths to specific diagnosis and personalised therapies in COPD and pre-COPD....
Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example, the presence of...
The purpose of this study was to introduce a new machine learning approach for differentiation of a pachychoroid from a healthy choroid based on enhanced depth-optical coherence tomography (EDI-OCT) i...
Integrative taxonomy, combining data from multiple axes of biologically relevant variation, is a major goal of systematics. Ideally, such taxonomies will derive from similarly integrative species-deli...
The 2016 American Society of Echocardiography guidelines have been widely used to assess left ventricular diastolic function. However, limitations are present in the current classification system. The...
Baseline demographics, heart failure hospitalization, and all-cause mortality data were obtained for all adult patients who underwent transthoracic echocardiography at Mayo Clinic Rochester in 2015. P...
Among 24,414 patients (mean age, 63.6 ± 16.2 years), all-cause mortality occurred in 4,612 patients (18.9%) during a median follow-up period of 3.1 years. The algorithm determined three clusters with ...
Unsupervised machine learning identified physiologically and prognostically distinct clusters on the basis of nine diastolic function Doppler variables. The clusters can be potentially applied in echo...
The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of var...
Significant regional variations in the HIV epidemic hurt effective common interventions in sub-Saharan Africa. It is crucial to analyze HIV positivity distributions within clusters and assess the homo...
We used an agglomerative hierarchical, unsupervised machine learning, approach for clustering to analyse data for 146,733 male and 155,622 female respondents from 13 sub-Saharan African countries with...
Two principal components were obtained, with the first describing 62.3% and 70.1% and the second explaining 18.3% and 20.6% variance of the total socio-behavioural variation in females and males, resp...
The findings provide a potential use of unsupervised machine learning approaches for substantially identifying clustered countries based on the underlying socio-behavioural characteristics....
