AIMC Topic: Diagnosis, Differential

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Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network.

Scientific reports
We present a computer-aided diagnosis system (CADx) for the automatic categorization of solid, part-solid and non-solid nodules in pulmonary computerized tomography images using a Convolutional Neural Network (CNN). Provided with only a two-dimension...

Crowdsourced validation of a machine-learning classification system for autism and ADHD.

Translational psychiatry
Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) together affect >10% of the children in the United States, but considerable behavioral overlaps between the two disorders can often complicate differential diagnosis. ...

Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This paper presents a deep-learning-based CADx for the differential diagnosis of embryonal (ERMS) and alveolar (ARMS) subtypes of rhabdomysarcoma (RMS) solely by analyzing multiparametric MR images. We formulated an automated pipeline that creates a ...

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purp...

Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND AND AIM: This study aims to examine the distinguishability of age-related cognitive decline (ARCD) from dementias based on some neurocognitive tests using machine learning.

Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry.

NeuroImage. Clinical
This paper presents a brain T1-weighted structural magnetic resonance imaging (MRI) biomarker that combines several individual MRI biomarkers (cortical thickness measurements, volumetric measurements, hippocampal shape, and hippocampal texture). The ...

Classification of Porcine Cranial Fracture Patterns Using a Fracture Printing Interface.

Journal of forensic sciences
Distinguishing between accidental and abusive head trauma in children can be difficult, as there is a lack of baseline data for pediatric cranial fracture patterns. A porcine head model has recently been developed and utilized in a series of studies ...

Procalcitonin as a potential predicting factor for prognosis in bacterial meningitis.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
We investigated the potential role of serum procalcitonin in differentiating bacterial meningitis from viral meningitis, and in predicting the prognosis in patients with bacterial meningitis. This was a retrospective study of 80 patients with bacteri...

Network or regression-based methods for disease discrimination: a comparison study.

BMC medical research methodology
BACKGROUND: In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based met...

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PloS one
The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme lea...