AI Medical Compendium Topic

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Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling.

PloS one
PURPOSE: To accelerate coronary MRI acquisitions with arbitrary undersampling patterns by using a novel reconstruction algorithm that applies coil self-consistency using subject-specific neural networks.

A comparison of fMRI and behavioral models for predicting inter-temporal choices.

NeuroImage
In an inter-temporal choice (IteCh) task, subjects are offered a smaller amount of money immediately or a larger amount at a later time point. Here, we are using trial-by-trial fMRI data from 363 recording sessions and machine learning in an attempt ...

Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1)...

A Deep Learning Model for Segmentation of Geographic Atrophy to Study Its Long-Term Natural History.

Ophthalmology
PURPOSE: To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA.

Comparison of medium-term survival outcomes between robot-assisted thoracoscopic surgery and video-assisted thoracoscopic surgery in treating primary lung cancer.

General thoracic and cardiovascular surgery
OBJECTIVES: Robot-assisted thoracoscopic surgery (RATS) for primary lung cancer has been spreading rapidly in Japan. While RATS has various technical advantages over video-assisted thoracoscopic surgery (VATS), the quality of surgery from an oncologi...

Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach.

Diabetes care
OBJECTIVE: To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and pra...

Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Deep learning convolutional neural networks (CNNs) show great potential for melanoma diagnosis. Melanoma thickness at diagnosis amongĀ others depends on melanoma localisation and subtype (e.g. advanced thickness in acrolentiginous or nodul...

Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

Diabetes/metabolism research and reviews
AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whethe...