AIMC Topic: Humans

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Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification.

Human brain mapping
The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing eff...

HGM-cNet: Integrating hippocampal gray matter probability map into a cascaded deep learning framework improves hippocampus segmentation.

European journal of radiology
A robust cascaded deep learning framework with integrated hippocampal gray matter (HGM) probability map was developed to improve the hippocampus segmentation (called HGM-cNet) due to its significance in various neuropsychiatric disorders such as Alzh...

Facial reconstruction using 3-D computerized method: A scoping review of Methods, current Status, and future developments.

Legal medicine (Tokyo, Japan)
Facial reconstruction (otherwise known as facial approximation) is an alternative method that has been widely accepted in forensic anthropological and archaeological circumstances. This method is considered useful for creating the virtual face of a p...

Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI.

European radiology
OBJECTIVES: To evaluate a deep learning model for automated and interpretable classification of central canal stenosis, neural foraminal stenosis, and facet arthropathy from lumbar spine MRI.

Developing a machine learning model to detect diagnostic uncertainty in clinical documentation.

Journal of hospital medicine
BACKGROUND AND OBJECTIVE: Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to ...

ChatGPT: Is this version good for healthcare and research?

Diabetes & metabolic syndrome
BACKGROUND AND AIMS: There have been advancements in artificial intelligence (AI) and deep learning in the past decade. Recently, OpenAI Inc. has launched a new chatbot, called ChatGPT that interacts in a conversational way and its dialogue format ma...

Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine.

EBioMedicine
Large Language Models (LLMs) are a key component of generative artificial intelligence (AI) applications for creating new content including text, imagery, audio, code, and videos in response to textual instructions. Without human oversight, guidance ...

Electromagnetic Wave Absorption in the Human Head: A Virtual Sensor Based on a Deep-Learning Model.

Sensors (Basel, Switzerland)
Determining the amount of electromagnetic wave energy absorbed by the human body is an important issue in the analysis of wireless systems. Typically, numerical methods based on Maxwell's equations and numerical models of the body are used for this p...

Initial Experience of Robot-Assisted Transabdominal Preperitoneal (TAPP) Inguinal Hernia Repair by a Single Surgeon in South Korea.

Medicina (Kaunas, Lithuania)
: Inguinal hernia is a common surgical disease. Traditional open herniorrhaphy has been replaced by laparoscopic herniorrhaphy. Nowadays, many attempts at robotic herniorrhaphy have been reported in western countries, but there have been no reports i...

Deep Learning-Based Road Traffic Noise Annoyance Assessment.

International journal of environmental research and public health
With the development of urban road traffic, road noise pollution is becoming a public concern. Controlling and reducing the harm caused by traffic noise pollution have been the hot spots of traffic noise management research. The subjective annoyance ...