AI Medical Compendium Topic:
Diagnosis, Differential

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Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls.

International journal of molecular sciences
Colorectal cancer (CRC) is one of the most daunting diseases due to its increasing worldwide prevalence, which requires imperative development of minimally or non-invasive screening tests. Urinary polyamines have been reported as potential markers to...

Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm.

The Journal of investigative dermatology
We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant melanoma, melanocytic nevu...

The association between reconstructed phase space and Artificial Neural Networks for vectorcardiographic recognition of myocardial infarction.

Journal of electrocardiology
Myocardial infarction is one of the leading causes of death worldwide. As it is life threatening, it requires an immediate and precise treatment. Due to this, a growing number of research and innovations in the field of biomedical signal processing i...

HHV-8-associated haemophagocytic lymphohistiocytosis in a patient with advanced AIDS.

BMJ case reports
We present a patient with advanced AIDS admitted with recurrent shock of unclear aetiology, fevers, altered mental status and refractory cytopenias. His case posed a diagnostic challenge because evaluation of septic shock in the setting of advanced A...

Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid.

Diagnostic cytopathology
BACKGROUND: To distinguish follicular adenoma (FA) and follicular carcinoma (FC) of thyroid in fine needle aspiration cytology (FNAC) is a challenging problem.

Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.

Clinical radiology
AIM: To identify the extent to which transfer learning from deep convolutional neural networks (CNNs), pre-trained on non-medical images, can be used for automated fracture detection on plain radiographs.

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

Scientific reports
Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic image...

Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.

Gut
BACKGROUND: In general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the 'resect and discard' paradigm for diminutive colorectal polyps workable. Computer analysis of video could poten...