AIMC Topic: Humans

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Computer-Aided Detection AI Reduces Interreader Variability in Grading Hip Abnormalities With MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Accurate interpretation of hip MRI is time-intensive and difficult, prone to inter- and intrareviewer variability, and lacks a universally accepted grading scale to evaluate morphological abnormalities.

Discriminating stress from rest based on resting-state connectivity of the human brain: A supervised machine learning study.

Human brain mapping
Acute stress induces large-scale neural reorganization with relevance to stress-related psychopathology. Here, we applied a novel supervised machine learning method, combining the strengths of a priori theoretical insights with a data-driven approach...

Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models.

International journal of medical informatics
BACKGROUND: Febrile neutropenia (FN) has been associated with high mortality among adults with cancer. Current systems for early detection of inpatient FN mortality are based on scoring indexes that require intensive physicians' subjective evaluation...

Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study.

Annals of oncology : official journal of the European Society for Medical Oncology
BACKGROUND: Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough.

Microaneurysms detection in color fundus images using machine learning based on directional local contrast.

Biomedical engineering online
BACKGROUND: As one of the major complications of diabetes, diabetic retinopathy (DR) is a leading cause of visual impairment and blindness due to delayed diagnosis and intervention. Microaneurysms appear as the earliest symptom of DR. Accurate and re...

Machine learning applied to retinal image processing for glaucoma detection: review and perspective.

Biomedical engineering online
INTRODUCTION: This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Fu...

A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan.

BMC health services research
BACKGROUND: Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with 'look-alike and sound-alike' (LASA) being the lead error. Existing solutions to preven...

DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images.

Translational vision science & technology
PURPOSE: To remove blood vessel shadows from optical coherence tomography (OCT) images of the optic nerve head (ONH).