AIMC Topic: Humans

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Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populational Study from Quebec, Canada.

American journal of clinical dermatology
BACKGROUND: Psoriasis is a major global health burden affecting ~ 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consumption, smoking, exercise) and characteristics as drive...

Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence.

Journal of chemical information and modeling
Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. T...

Precise detection of awareness in disorders of consciousness using deep learning framework.

NeuroImage
Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-stat...

Deep Learning-Based Multi-Class Segmentation of the Paranasal Sinuses of Sinusitis Patients Based on Computed Tomographic Images.

Sensors (Basel, Switzerland)
Accurate paranasal sinus segmentation is essential for reducing surgical complications through surgical guidance systems. This study introduces a multiclass Convolutional Neural Network (CNN) segmentation model by comparing four 3D U-Net variations-n...

An automated ICU agitation monitoring system for video streaming using deep learning classification.

BMC medical informatics and decision making
OBJECTIVE: To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning.

Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images.

BMC medical imaging
Significant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, and the amount of cancer-related informati...

Comparison of the S8 navigation system and the TINAVI orthopaedic robot in the treatment of upper cervical instability.

Scientific reports
The objective is to compare the clinical efficacy and safety of the S8 navigation system and the Tinavi orthopaedic surgery robot in the treatment of upper cervical instability. The research methods adopted are as follows. The clinical data of patien...

Fast Human Motion reconstruction from sparse inertial measurement units considering the human shape.

Nature communications
Inertial Measurement Unit-based methods have great potential in capturing motion in large-scale and complex environments with many people. Sparse Inertial Measurement Unit-based methods have more research value due to their simplicity and flexibility...

hinotori vs. da Vinci: propensity score-matched analysis of surgical outcomes of robot-assisted radical prostatectomy.

Journal of robotic surgery
hinotori™ is a recently developed surgical robot system. The present study aims to compare intraoperative and postoperative outcomes of robot-assisted radical prostatectomy (RARP) by the hinotori™ system compared with those of the longer-established ...

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretabilit...