AIMC Topic: Prognosis

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Deep Learning for Radiotherapy Outcome Prediction Using Dose Data - A Review.

Clinical oncology (Royal College of Radiologists (Great Britain))
Artificial intelligence, and in particular deep learning using convolutional neural networks, has been used extensively for image classification and segmentation, including on medical images for diagnosis and prognosis prediction. Use in radiotherapy...

Integrated Multiomics Analysis Identifies a Novel Biomarker Associated with Prognosis in Intracerebral Hemorrhage.

Oxidative medicine and cellular longevity
Existing treatments for intracerebral hemorrhage (ICH) are unable to satisfactorily prevent development of secondary brain injury after ICH and multiple pathological mechanisms are involved in the development of the injury. In this study, we aimed to...

Machine Learning Models Prognosticate Functional Outcomes Better than Clinical Scores in Spontaneous Intracerebral Haemorrhage.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: This study aims to develop and compare the use of deep neural networks (DNN) and support vector machines (SVM) to clinical prognostic scores for prognosticating 30-day mortality and 90-day poor functional outcome (PFO) in spontaneous intra...

Artificial intelligence and cardiology: Current status and perspective.

Journal of cardiology
The development of artificial intelligence (AI) began in the mid-20th century but has been rapidly accelerating in the past decade. Reflecting the development of digital health over the past few years, this trend is also seen in medicine. The field o...

A radiogenomics application for prognostic profiling of endometrial cancer.

Communications biology
Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecul...

Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: The prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histologic criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and seve...

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.

Scientific reports
In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of r...