AIMC Topic: Humans

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Development and validation of a deep learning-based laparoscopic system for improving video quality.

International journal of computer assisted radiology and surgery
PURPOSE: A clear surgical field of view is a prerequisite for successful laparoscopic surgery. Surgical smoke, image blur, and lens fogging can affect the clarity of laparoscopic imaging. We aimed to develop a real-time assistance system (namely LVQI...

Machine learning for detecting centre-level irregularities in randomized controlled trials: A pilot study.

Contemporary clinical trials
Centralized statistical monitoring is sometimes employed as an alternative to onsite monitoring for randomized control trials. Current central monitoring methods have limitations, in that they are relatively resource intensive and do not necessarily ...

Deep-Learning-Based Character Recognition from Handwriting Motion Data Captured Using IMU and Force Sensors.

Sensors (Basel, Switzerland)
Digitizing handwriting is mostly performed using either image-based methods, such as optical character recognition, or utilizing two or more devices, such as a special stylus and a smart pad. The high-cost nature of this approach necessitates a cheap...

Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation.

International journal of environmental research and public health
Emotional responses are significant for understanding public perceptions of urban green space (UGS) and can be used to inform proposals for optimal urban design strategies to enhance public emotional health in the times of COVID-19. However, most emp...

Deep learning-based behavioral profiling of rodent stroke recovery.

BMC biology
BACKGROUND: Stroke research heavily relies on rodent behavior when assessing underlying disease mechanisms and treatment efficacy. Although functional motor recovery is considered the primary targeted outcome, tests in rodents are still poorly reprod...

Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning.

BMC medical imaging
BACKGROUND: Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. Th...

Efficacy and safety of robotic spine surgery: systematic review and meta-analysis.

Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology
BACKGROUND: Robotic surgery (RS) may offer benefits compared with freehand/conventional surgery (FS) in the treatment of patients with spinal disease. The aim of this study was to evaluate the efficacy and safety of RS versus FS in spinal fusion.

Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.

Journal of medical systems
There has been an explosive growth in research over the last decade exploring machine learning techniques for analyzing chest X-ray (CXR) images for screening cardiopulmonary abnormalities. In particular, we have observed a strong interest in screeni...

Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images.

Computational and mathematical methods in medicine
Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in co...

Differentiation of eosinophilic and non-eosinophilic chronic rhinosinusitis on preoperative computed tomography using deep learning.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
OBJECTIVES: This study aimed to develop deep learning (DL) models for differentiating between eosinophilic chronic rhinosinusitis (ECRS) and non-ECRS (NECRS) on preoperative CT.