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Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine.

EBioMedicine
BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) metho...

Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI's potential in breast screening practice.

Expert review of medical devices
INTRODUCTION: Various factors are driving interest in the application of artificial intelligence (AI) for breast cancer (BC) detection, but it is unclear whether the evidence warrants large-scale use in population-based screening.

Classification of Patients with Coronary Microvascular Dysfunction.

IEEE/ACM transactions on computational biology and bioinformatics
While coronary microvascular dysfunction (CMD) is a major cause of ischemia, it is very challenging to diagnose due to lack of CMD-specific screening measures. CMD has been identified as one of the five priority areas of investigation in a 2014 Natio...

Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis.

Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
BACKGROUND: The aim of this study was to evaluate the hypothesis that a deep convolutional neural network (DCNN) model could facilitate automated Brasfield scoring of chest radiographs (CXRs) for patients with cystic fibrosis (CF), performing similar...

Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multisequence 3D imaging.

Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

The Journal of arthroplasty
BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create...

Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.

Psychological assessment
Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision while minimizing laborious tasks that require human intervention. There is mounting evidence that the ...

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.

The Lancet. Digital health
BACKGROUND: Radical measures are required to identify and reduce blindness due to diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of an artificial intelligence (AI) model using deep learning in a po...

Robot-assisted line bisection in patients with Complex Regional Pain Syndrome.

PloS one
Complex Regional Pain Syndrome (CRPS) is characterized by pain, motor and inflammatory symptoms usually affecting one limb. Cognitive difficulties have been reported to affect patients' ability to represent, perceive and use their affected limb. It i...

Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis.

Clinical rheumatology
OBJECTIVE: To develop a predictive mathematical model for the early identification of ankylosing spondylitis (AS) based on the medical and pharmacy claims history of patients with and without AS.