AIMC Topic: Humans

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Finding a Suitable Class Distribution for Building Histological Images Datasets Used in Deep Model Training-The Case of Cancer Detection.

Journal of digital imaging
The class distribution of a training dataset is an important factor which influences the performance of a deep learning-based system. Understanding the optimal class distribution is therefore crucial when building a new training set which may be cost...

KeratoScreen: Early Keratoconus Classification With Zernike Polynomial Using Deep Learning.

Cornea
PURPOSE: We aimed to investigate the usefulness of Zernike coefficients (ZCs) for distinguishing subclinical keratoconus (KC) from normal corneas and to evaluate the goodness of detection of the entire corneal topography and tomography characteristic...

Robot assisted radical cystectomy with Florence Robotic Intracorporeal Neobladder (FloRIN): Functional and urodynamic features compared with a contemporary series of open Vescica Ileale Padovana (VIP).

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: FloRIN reconfiguration technique was introduced in 2016 according to the IDEAL-Collaboration Guidelines, with the attempt to conjugate the advantages of both intracorporeal neobladder and robotic assistance. Herein we report functional ...

From Dose Reduction to Contrast Maximization: Can Deep Learning Amplify the Impact of Contrast Media on Brain Magnetic Resonance Image Quality? A Reader Study.

Investigative radiology
OBJECTIVES: The aim of this study was to evaluate a deep learning method designed to increase the contrast-to-noise ratio in contrast-enhanced gradient echo T1-weighted brain magnetic resonance imaging (MRI) acquisitions. The processed images are qua...

Artificial intelligence in gastrointestinal and hepatic imaging: past, present and future scopes.

Clinical imaging
The use of technology in medicine has grown exponentially because of the technological advancements allowing the digitization of medical data and optimization of their processing to extract multiple features of significant clinical relevance. Radiolo...

A multimodal deep learning model for cardiac resynchronisation therapy response prediction.

Medical image analysis
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the 'nnU-Net' segmentation model ...

Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework.

Clinical nuclear medicine
PURPOSE: The generalizability and trustworthiness of deep learning (DL)-based algorithms depend on the size and heterogeneity of training datasets. However, because of patient privacy concerns and ethical and legal issues, sharing medical images betw...

Assigning diagnosis codes using medication history.

Artificial intelligence in medicine
Diagnosis assignment is the process of assigning disease codes to patients. Automatic diagnosis assignment has the potential to validate code assignments, correct erroneous codes, and register completion. Previous methods build on text-based techniqu...

Automatic Evaluation of Motor Rehabilitation Exercises Based on Deep Mixture Density Neural Networks.

Journal of biomedical informatics
An automatic assessment system for physical telerehabilitation could reduce the time and cost of treatments. But such assessment involves stochastic uncertainties, nonlinearities, and complexities of human movement. Probabilistic models and deep stru...