Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on F-florbetapir and F-fluorodeox...
During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results i...
Accepting surgical complications, especially those related to the learning curve, as unavoidable events in colorectal procedures, is like accepting to fly onboard an aircraft with a 10% to 20% chance of not arriving at final destination. Under this c...
Clinical cancer research : an official journal of the American Association for Cancer Research
May 4, 2021
PURPOSE: Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning-based fully-automated tool called the DeepPET-OPSCC biomarker for predicting ove...
Efficient methods developed with deep learning in the last ten years have provided objectivity and high accuracy in the diagnosis of skin diseases. They also support accurate, cost-effective and timely treatment. In addition, they provide diagnoses w...
European journal of obstetrics, gynecology, and reproductive biology
May 4, 2021
OBJECTIVE: To describe perioperative adverse events, fertility and obstetric outcome, following a robot assisted laparoscopic approach for treating Cesarean scar pregnancies (CSP).
OBJECTIVE: One barrier hindering high frequency brain signals (HFBS, >80 Hz) from wide clinical applications is that the brain generates both pathological and physiological HFBS. This study was to find specific biomarkers for localizing epileptogenic...
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
May 4, 2021
BACKGROUND AND PURPOSE: Delineating organs at risk (OARs) on computed tomography (CT) images is an essential step in radiation therapy; however, it is notoriously time-consuming and prone to inter-observer variation. Herein, we report a deep learning...
Background The interpretation of radiographs suffers from an ever-increasing workload in emergency and radiology departments, while missed fractures represent up to 80% of diagnostic errors in the emergency department. Purpose To assess the performan...
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