AIMC Topic: Humans

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Multi disease-prediction framework using hybrid deep learning: an optimal prediction model.

Computer methods in biomechanics and biomedical engineering
Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regard...

Robot-assisted Radical Cystectomy Versus Open Radical Cystectomy in Bladder Cancer Patients: A Multicentre Comparative Effectiveness Study.

European urology
BACKGROUND: Radical cystectomy with pelvic lymph node dissection (PLND) and urinary diversion in patients with bladder cancer is known for its high risk of complications. Although open radical cystectomy (ORC) is regarded as the standard treatment, r...

Relationship Development with Humanoid Social Robots: Applying Interpersonal Theories to Human-Robot Interaction.

Cyberpsychology, behavior and social networking
(HSRs) are human-made technologies that can take physical or digital form, resemble people in form or behavior to some degree, and are designed to interact with people. A common assumption is that social robots can and should mimic humans, such that...

Investigating the potential of deep learning for patient-specific quality assurance of salivary gland contours using EORTC-1219-DAHANCA-29 clinical trial data.

Acta oncologica (Stockholm, Sweden)
INTRODUCTION: Manual quality assurance (QA) of radiotherapy contours for clinical trials is time and labor intensive and subject to inter-observer variability. Therefore, we investigated whether deep-learning (DL) can provide an automated solution to...

Ultrasound volume projection image quality selection by ranking from convolutional RankNet.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3...

A Machine Learning Approach Yields a Multiparameter Prognostic Marker in Liver Cancer.

Cancer immunology research
A number of staging systems have been developed to predict clinical outcomes in hepatocellular carcinoma (HCC). However, no general consensus has been reached regarding the optimal model. New approaches such as machine learning (ML) strategies are po...

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer.

JNCI cancer spectrum
BACKGROUND: New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method...

Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses.

Advances in therapy
INTRODUCTION: Administrative claims data provide an important source for real-world evidence (RWE) generation, but incomplete reporting, such as for body mass index (BMI), limits the sample sizes that can be analyzed to address certain research quest...

Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning.

Journal of digital imaging
Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility...

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Nature neuroscience
Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance to brain development and neuropsychiatric disease. However, the detection of individual somatic mobile element insertions present...