Machine learning reveals pathways to neuroendocrine tumor (NET) diagnosis. Patients with NET and age-/gender-matched non-NET controls were retrospectively selected from MarketScan claims. Predictors (e.g., procedures, symptoms, conditions for which...
Annals of clinical and translational neurology
May 19, 2021
Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data ...
Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrati...
Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volum...
The prediction of poor ovarian response (POR) for stratified interference is a critical clinical issue that has received an increasing amount of recent concern. Anthropogenic diagnostic modes remain too simple for the handling of actual clinical comp...
OBJECTIVE: To investigate the impact of changes in body composition during primary treatment on survival outcomes in patients with epithelial ovarian cancer (EOC).
BACKGROUND: There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, ...
Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology
May 10, 2021
BACKGROUND AND STUDY AIM: The study aim was to improve and validate the accuracy of the fibrosis-4 (FIB-4) and aspartate aminotransferase-to-platelet ratio index (APRI) scores for use in a potential machine-learning (ML) method that accurately predic...
AIM: Artificial intelligence (AI)-based breast cancer grading may help to overcome perceived limitations of human assessment. Here, the potential value of AI grade was evaluated at the molecular level and in predicting patient outcome.