Operative neurosurgery (Hagerstown, Md.)
Oct 15, 2020
BACKGROUND: Robotics in neurosurgery has demonstrated widening indications and rapid growth in recent years. Robotic precision and reproducibility are especially pertinent to the field of functional neurosurgery. Deep brain stimulation (DBS) requires...
Hinyokika kiyo. Acta urologica Japonica
Oct 1, 2020
Herein we present simple methods to prevent postoperative inguinal hernia (IH) after extraperitoneal and transperitoneal robot-assisted radical prostatectomy (RARP). Among 275 patients who underwent RARP between January 2014 and December 2016 at our ...
OBJECTIVES: To evaluate the utility of machine learning (ML) for the management of Medicare beneficiaries at risk of severe respiratory infections in community and postacute settings by (1) identifying individuals in a community setting at risk of in...
OBJECTIVES: As a life-threatening condition, sepsis is one of the major public health issues worldwide. Early prediction can improve sepsis outcomes with appropriate interventions. With the PhysioNet/Computing in Cardiology Challenge 2019, we aimed t...
IMPORTANCE: Screening for diabetic retinopathy is recommended for children with type 1 diabetes (T1D) and type 2 diabetes (T2D), yet screening rates remain low. Point-of-care diabetic retinopathy screening using autonomous artificial intelligence (AI...
IMPORTANCE: Recent studies have demonstrated the successful application of artificial intelligence (AI) for automated retinal disease diagnostics but have not addressed a fundamental challenge for deep learning systems: the current need for large, cr...
Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft
Oct 1, 2020
BACKGROUND: Empirical models have been an integral part of everyday clinical practice in ophthalmology since the introduction of the Sanders-Retzlaff-Kraff (SRK) formula. Recent developments in the field of statistical learning (artificial intelligen...
IMPORTANCE: A computer algorithm that performs at or above the level of radiologists in mammography screening assessment could improve the effectiveness of breast cancer screening.
The journal of trauma and acute care surgery
Oct 1, 2020
BACKGROUND: Trauma patients admitted to critical care are at high risk of mortality because of their injuries. Our aim was to develop a machine learning-based model to predict mortality using Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework. We...
In this work, we assess how pre-training strategy affects deep learning performance for the task of distinguishing false-recall from malignancy and normal (benign) findings in digital mammography images. A cohort of 1303 breast cancer screening patie...
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