COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients: COVID-19 prognostic modeling using CT radiomics and machine learning

• 17/01/1446 - 10:14

The High Rate of Discordance Between Clinical Symptoms and MRI Findings in Patients with Pelvic Floor Dysfunction

• 17/01/1446 - 09:10

Deep learning-based automatic detection of tuberculosis disease in chest X-ray images

• 17/01/1446 - 09:00

Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis

• 17/01/1446 - 08:57

A systematic review and meta-analysis on the association between lymphocyte subsets and the severity of COVID-19

• 17/01/1446 - 08:38

Predicting factors for relapse in patients with granulomatosis with polyangiitis: results from a long-term cohort

• 17/01/1446 - 08:33

H Ghanaati

• 16/01/1446 - 10:33

Neonatal Outcomes of Fetuses with Isolated or Multiple Soft Markers in Ultrasound Screening

• 16/01/1446 - 10:13

Technical Accuracy of Percutaneous Needle Biopsy of Subcentimeter Pulmonary Nodules under CT Fluoroscopy Guidance and Associated Procedural Complication

• 16/01/1446 - 09:50

Diagnostic Accuracy of Transvaginal Sonography for Deeply Infiltrating Endometriosis and Pouch of Douglas Obliteration in the Presence or Absence of Ovarian Endometrioma

• 15/01/1446 - 12:45