AIMC Topic: Reproducibility of Results

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Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors.

Artificial intelligence in medicine
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative infor...

Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

Radiation oncology (London, England)
BACKGROUND: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subje...

A novel artificial intelligence system for the assessment of bowel preparation (with video).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: The quality of bowel preparation is an important factor that can affect the effectiveness of a colonoscopy. Several tools, such as the Boston Bowel Preparation Scale (BBPS) and Ottawa Bowel Preparation Scale, have been developed ...

Building an Otoscopic screening prototype tool using deep learning.

Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale
BACKGROUND: Otologic diseases are often difficult to diagnose accurately for primary care providers. Deep learning methods have been applied with great success in many areas of medicine, often outperforming well trained human observers. The aim of th...

Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Informed estimates claim that 80% to 99% of alarms set off in hospital units are false or clinically insignificant, representing a cacophony of sounds that do not present a real danger to patients. These false alarms can lead to an alert ...

A dual-stream deep convolutional network for reducing metal streak artifacts in CT images.

Physics in medicine and biology
Machine learning and deep learning are rapidly finding applications in the medical imaging field. In this paper, we address the long-standing problem of metal artifacts in computed tomography (CT) images by training a dual-stream deep convolutional n...

ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.

EBioMedicine
BACKGROUND: The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". Ho...

DESIGN AND DEVELOPMENT OF HUMAN COMPUTER INTERFACE USING ELECTROOCULOGRAM WITH DEEP LEARNING.

Artificial intelligence in medicine
Today's life assistive devices were playing significant role in our life to communicate with others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) playing vital part. By using this method we can able to overcome the con...

Celiac disease diagnosis from videocapsule endoscopy images with residual learning and deep feature extraction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Videocapsule endoscopy (VCE) is a relatively new technique for evaluating the presence of villous atrophy in celiac disease patients. The diagnostic analysis of video frames is currently time-consuming and tedious. Recently,...

Towards near real-time assessment of surgical skills: A comparison of feature extraction techniques.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Surgical skill assessment aims to objectively evaluate and provide constructive feedback for trainee surgeons. Conventional methods require direct observation with assessment from surgical experts which are both unscalable a...