OBJECTIVE: To determine if minimally invasive right colectomy with intra-corporeal anastomosis improves postoperative recovery compared to extra-corporeal anastomosis.
In cytological examination, suspicious cells are evaluated regarding malignancy and cancer type. To assist this, we previously proposed an automated method based on supervised learning that classifies cells in lung cytological images as benign or mal...
The study aimed to analyze potential prognostic factors in patients treated with robotic radiosurgery for brain metastases irrespective of primary tumor location and create a simple prognostic score that can be used without a full diagnostic workup. ...
BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized by ST-segment elevation in the right precordial leads, and risk of Sudden Cardiac Death (SCD). Furthe...
RATIONALE AND OBJECTIVES: To compare the performance of pneumothorax deep learning detection models trained with radiologist versus natural language processing (NLP) labels on the NIH ChestX-ray14 dataset.
BACKGROUND: Accurate response evaluation is necessary to select complete responders (CRs) for a watch-and-wait approach. Deep learning may aid in this process, but so far has never been evaluated for this purpose. The aim was to evaluate the accuracy...
BACKGROUND: The safety and feasibility of robot-assisted minimally invasive esophagectomy (RAMIE) remain unclear. The aim of this study was to compare the short-term outcomes of RAMIE with extended lymphadenectomy and conventional minimally invasive ...
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Oct 12, 2021
PURPOSE: To create a model for prediction of postoperative visual acuity (VA) after vitrectomy for macular hole (MH) treatment using preoperative optical coherence tomography (OCT) images, using deep learning (DL)-based artificial intelligence.
PURPOSE: Current diagnostic and treatment modalities for pancreatic cysts (PCs) are invasive and are associated with patient morbidity. The purpose of this study is to develop and evaluate machine learning algorithms to delineate mucinous from non-mu...
BACKGROUND: At present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI), many radiomics research methods have been applied to the identification of pancreatic SCNs and MCNs.
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