AIMC Topic: Humans

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A pilot study of a deep learning approach to detect marginal bone loss around implants.

BMC oral health
BACKGROUND: Recently, there has been considerable innovation in artificial intelligence (AI) for healthcare. Convolutional neural networks (CNNs) show excellent object detection and classification performance. This study assessed the accuracy of an a...

Comparing two artificial intelligence software packages for normative brain volumetry in memory clinic imaging.

Neuroradiology
PURPOSE: To compare two artificial intelligence software packages performing normative brain volumetry and explore whether they could differently impact dementia diagnostics in a clinical context.

Detection of metallic objects on digital radiographs with convolutional neural networks: A MRI screening tool.

Radiography (London, England : 1995)
INTRODUCTION: Screening for metallic implants and foreign bodies before magnetic resonance imaging (MRI) examinations, are crucial for patient safety. History of health are supplied by the patient, a family member, screening of electronic health reco...

Preoperative planning method based on a MOPSO algorithm for robot-assisted cholecystectomy.

International journal of computer assisted radiology and surgery
PURPOSE: Surgical robots have multiple manipulators with complex mechanisms and need to work in a narrow space in the patient's body. Therefore, for robot-assisted minimally invasive surgery (RMIS), it is very important to develop a reasonable preope...

Application of deterministic networking for reducing network delay in urological telesurgery: A retrospective study.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Deterministic Networking (DetNet) is a new technology that can effectively control network delay and may promote the revolution of telemedicine. This study verified the feasibility and advantage of deterministic networking in telesurgery.

Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values.

European journal of radiology
PURPOSE: To develop a machine-learning-based radiomics signature of ADC for discriminating between benign and malignant testicular masses and compare its classification performance with that of minimum and mean ADC.

Deep learning of early brain imaging to predict post-arrest electroencephalography.

Resuscitation
INTRODUCTION: Guidelines recommend use of computerized tomography (CT) and electroencephalography (EEG) in post-arrest prognostication. Strong associations between CT and EEG might obviate the need to acquire both modalities. We quantified these asso...

Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning.

Cells
Publicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discover...

Ranking patients on the kidney transplant waiting list based on fuzzy inference system.

BMC nephrology
BACKGROUND: Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed ...