AIMC Topic: Retrospective Studies

Clear Filters Showing 7991 to 8000 of 9989 articles

Cascade recurring deep networks for audible range prediction.

BMC medical informatics and decision making
BACKGROUND: Hearing Aids amplify sounds at certain frequencies to help patients, who have hearing loss, to improve the quality of life. Variables affecting hearing improvement include the characteristics of the patients' hearing loss, the characteris...

Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

Journal of dairy science
The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from Se...

Laparoscopic versus robotic surgery for hepatocellular carcinoma: the first 46 consecutive cases.

The Journal of surgical research
BACKGROUND: Hepatocellular carcinoma has a growing incidence worldwide, and represents a leading cause of death in patients with cirrhosis. Nowadays, minimally invasive approaches are spreading in every field of surgery and in liver surgery as well.

Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This paper presents a deep-learning-based CADx for the differential diagnosis of embryonal (ERMS) and alveolar (ARMS) subtypes of rhabdomysarcoma (RMS) solely by analyzing multiparametric MR images. We formulated an automated pipeline that creates a ...

Cardiovascular events in patients with mild autonomous cortisol secretion: analysis with artificial neural networks.

European journal of endocrinology
BACKGROUND: The independent role of mild autonomous cortisol secretion (ACS) in influencing the cardiovascular event (CVE) occurrence is a topic of interest. We investigated the role of mild ACS in the CVE occurrence in patients with adrenal incident...

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Radiology
Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified HIPAA-compliant datasets were used in this study that were exempted from revi...

Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning.

PloS one
OBJECTIVE: To demonstrate the incremental benefit of using free text data in addition to vital sign and demographic data to identify patients with suspected infection in the emergency department.