AIMC Topic: Diagnosis, Differential
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Machine learning-based decision tree classifier for the diagnosis of progressive supranuclear palsy and corticobasal degeneration.
Neuropathology and applied neurobiology
Apr 7, 2021
AIMS: This study aimed to clarify the different topographical distribution of tau pathology between progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) and establish a machine learning-based decision tree classifier.
Application of artificial intelligence using a convolutional neural network for detecting cholesteatoma in endoscopic enhanced images.
Auris, nasus, larynx
Apr 4, 2021
OBJECTIVE: We examined whether artificial intelligence (AI) used with the novel digital image enhancement system modalities (CLARA+CHROMA, SPECTRA A, and SPECTRA B) could distinguish the cholesteatoma matrix, cholesteatoma debris, and normal middle e...
Development and Validation of a Radiomics Model for Differentiating Bone Islands and Osteoblastic Bone Metastases at Abdominal CT.
Radiology
Mar 30, 2021
Background It is important to diagnose sclerotic bone lesions in order to determine treatment strategy. Purpose To evaluate the diagnostic performance of a CT radiomics-based machine learning model for differentiating bone islands and osteoblastic bo...
Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration.
Scientific reports
Mar 30, 2021
Polypoidal choroidal vasculopathy (PCV) and neovascular age-related macular degeneration (nAMD) share some similarity in clinical imaging manifestations. However, their disease entity and treatment strategy as well as visual outcomes are very differe...
Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning.
Radiology
Mar 23, 2021
Background Conventional radiologic modalities perform poorly in the radiated rectum and are often unable to differentiate residual cancer from treatment scarring. Purpose To report the development and initial patient study of an imaging system compri...
Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images.
AJNR. American journal of neuroradiology
Mar 18, 2021
BACKGROUND AND PURPOSE: Differentiating glioblastoma from solitary brain metastasis preoperatively using conventional MR images is challenging. Deep learning models have shown promise in performing classification tasks. The diagnostic performance of ...
Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.
Journal of magnetic resonance imaging : JMRI
Mar 11, 2021
BACKGROUND: Differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is useful to guide treatment strategies.
Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorders by multiparametric quantitative MRI using convolutional neural network.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Mar 11, 2021
Multiple sclerosis and neuromyelitis optica spectrum disorders are both neuroinflammatory diseases and have overlapping clinical manifestations. We developed a convolutional neural network model that differentiates between the two based on magnetic r...
Artificial intelligence: finding the intersection of predictive modeling and clinical utility.
Gastrointestinal endoscopy
Mar 7, 2021
Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study.
BMJ open
Mar 5, 2021
OBJECTIVES: Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning ...
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