AIMC Topic: Humans

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Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists.

AJR. American journal of roentgenology
The objective of our study was to compare the performance of radiologicradiomic machine learning (ML) models and expert-level radiologists for differentiation of benign and malignant solid renal masses using contrast-enhanced CT examinations. This ...

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning.

Analytica chimica acta
Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absen...

Enhancing the security of patients' portals and websites by detecting malicious web crawlers using machine learning techniques.

International journal of medical informatics
INTRODUCTION: There is increasing demand for access to medical information via patients' portals. However, one of the challenges towards widespread utilisation of such service is maintaining the security of those portals. Recent reports show an alarm...

Phasic dopamine release identification using convolutional neural network.

Computers in biology and medicine
Dopamine has a major behavioral impact related to drug dependence, learning and memory functions, as well as pathologies such as schizophrenia and Parkinson's disease. Phasic release of dopamine can be measured in vivo with fast-scan cyclic voltammet...

Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Epileptic seizures occur as a result of a process that develops over time and space in epileptic networks. In this study, we aim at developing a generalizable method for patient-specific seizure prediction by evaluating the spatio-temporal correlatio...

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.

The Lancet. Digital health
BACKGROUND: Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging.

Prediction of complication related death after radical cystectomy for bladder cancer with machine learning methodology.

Scandinavian journal of urology
To create a pre-operatively usable tool to identify patients at high risk of early death (within 90 days post-operatively) after radical cystectomy and to assess potential risk factors for post-operative and surgery related mortality. Material consi...