AIMC Topic: Humans

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Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques.

Computer methods and programs in biomedicine
BACKGROUND: Embryo morphology is a predictive marker for implantation success and ultimately live births. Viability evaluation and quality grading are commonly used to select the embryo with the highest implantation potential. However, the traditiona...

A Deep-Learning-Assisted On-Mask Sensor Network for Adaptive Respiratory Monitoring.

Advanced materials (Deerfield Beach, Fla.)
Wearable respiratory monitoring is a fast, non-invasive, and convenient approach to provide early recognition of human health abnormalities like restrictive and obstructive lung diseases. Here, a computational fluid dynamics assisted on-mask sensor n...

Teleoperation of High-Speed Robot Hand with High-Speed Finger Position Recognition and High-Accuracy Grasp Type Estimation.

Sensors (Basel, Switzerland)
This paper focuses on the teleoperation of a robot hand on the basis of finger position recognition and grasp type estimation. For the finger position recognition, we propose a new method that fuses machine learning and high-speed image-processing te...

Overview of ICD-11 architecture and structure.

BMC medical informatics and decision making
BACKGROUND: The International Classification of Diseases (ICD) has progressed from a short list of causes of death to become the predominant classification of human diseases, syndromes, and conditions around the world. The World Health Organization h...

Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model.

BMC medical informatics and decision making
BACKGROUND: One of the most prevalent complications of Partial Nephrectomy (PN) is Acute Kidney Injury (AKI), which could have a negative impact on subsequent renal function and occurs in up to 24.3% of patients undergoing PN. The aim of this study w...

Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression.

BMC psychiatry
BACKGROUND: The debate of whether machine learning models offer advantages over standard statistical methods when making predictions is ongoing. We discuss the use of a meta-learner model combining both approaches as an alternative.

Glaucoma diagnosis using multi-feature analysis and a deep learning technique.

Scientific reports
In this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 ...

Development of a system for the automated identification of herbarium specimens with high accuracy.

Scientific reports
Herbarium specimens are dried plants mounted onto paper. They are used by a limited number of researchers, such as plant taxonomists, as a source of information on morphology and distribution. Recently, digitised herbarium specimens have begun to be ...

Use of Robots in Critical Care: Systematic Review.

Journal of medical Internet research
BACKGROUND: The recent focus on the critical setting, especially with the COVID-19 pandemic, has highlighted the need for minimizing contact-based care and increasing robotic use. Robotics is a rising field in the context of health care, and we sough...

The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in ...