AIMC Topic: Diagnosis, Differential

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Second branchial cleft anomalies in children: a literature review.

Pediatric surgery international
Branchial cleft anomalies are the second most common head and neck congenital lesions in children. It may sometimes be a part of branchio-oto-renal (BOR) syndrome, so in patients with branchial cleft anomalies associated with a complaint of auricular...

Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

European journal of radiology
PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to differentiate a glioblastoma multiforme (GBM) from a primary cerebral nervous system lymphoma (PCNSL).

Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients.

Acta psychiatrica Scandinavica
OBJECTIVE: This study determined the clinical utility of an fMRI classification algorithm predicting medication-class of response in patients with challenging mood diagnoses.

On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images.

European journal of radiology
PURPOSE: High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult as both appear hyperintense in T-W/FLAIR images. Most studies involving differentiation between vasog...

Discovering hidden knowledge through auditing clinical diagnostic knowledge bases.

Journal of biomedical informatics
OBJECTIVE: Evaluate potential for data mining auditing techniques to identify hidden concepts in diagnostic knowledge bases (KB). Improving completeness enhances KB applications such as differential diagnosis and patient case simulation.

Machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification.

Clinical radiology
AIM: To develop a machine learning-based model for the binary classification of chest radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting prioritisation.

Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.

International journal of radiation oncology, biology, physics
PURPOSE: Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics is an emerging field that promises to improve on conventional i...