AI Medical Compendium Topic

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Machine learning to predict cardiovascular risk.

International journal of clinical practice
AIMS: To analyse the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk scales.

Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning.

Acta ophthalmologica
PURPOSE: To assess the use of deep learning (DL) for computer-assisted glaucoma identification, and the impact of training using images selected by an active learning strategy, which minimizes labelling cost. Additionally, this study focuses on the e...

Implementation of a cloud-based referral platform in ophthalmology: making telemedicine services a reality in eye care.

The British journal of ophthalmology
BACKGROUND: Hospital Eye Services (HES) in the UK face an increasing number of optometric referrals driven by progress in retinal imaging. The National Health Service (NHS) published a 10-year strategy (NHS Long-Term Plan) to transform services to me...

Metabolic cost adaptations during training with a soft exosuit assisting the hip joint.

Scientific reports
Different adaptation rates have been reported in studies involving ankle exoskeletons designed to reduce the metabolic cost of their wearers. This work aimed to investigate energetic adaptations occurring over multiple training sessions, while walkin...

Artificial neural network optimizes self-examination of osteoporosis risk in women.

The Journal of international medical research
OBJECTIVE: This study aimed to investigate the application of an artificial neural network (ANN) in optimizing the Osteoporosis Self-Assessment Tool for Asians (OSTA) score.

Model of a Support Vector Machine to Assess the Functional Cure for Surgery of Intermittent Exotropia.

Scientific reports
In this paper the optimum timing for the postoperative functional cure of basic intermittent exotropia is explored based on support vector machine (SVM). One hundred and thirty-two patients were recruited in this prospective cross-sectional study wit...

Breast cancer outcome prediction with tumour tissue images and machine learning.

Breast cancer research and treatment
PURPOSE: Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.

Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning.

Epilepsy & behavior : E&B
OBJECTIVE: The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy.