Applying data mining and machine learning (ML) techniques to clinical data might identify predictive biomarkers for diabetic nephropathy (DN), a common complication of type 2 diabetes mellitus (T2DM). A retrospective analysis of the Action to Control...
Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be...
BACKGROUND: Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates financial and psychological consequences of applying inefficient therapy. To achieve this goal we p...
BACKGROUND: We designed a deep learning model for assessing F-FDG PET/CT for early prediction of local and distant failures for patients with locally advanced cervical cancer.
Photodiagnosis and photodynamic therapy
May 25, 2019
In this report, we collected the Raman spectrum of cervical adenocarcinoma and cervical squamous cell carcinoma tissues by a micro-Raman spectroscopy system. We analysed, compared and summarized the characteristics and differences of the normalized m...
The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtain...
With the advent of convolutional neural networks (CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervi...
OBJECTIVES: This study estimated the diagnostic performance of a deep learning system for detection of Sjögren's syndrome (SjS) on CT, and compared it with the performance of radiologists.
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