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Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study.

BMC psychiatry
OBJECTIVE: Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify and intervene in clinical practice. The objective of this study was to explore the association betwe...

Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury.

BMC psychiatry
BACKGROUND: Nonsuicidal self-injury is a common health problem in adolescents and associated with future suicidal behavior. Predicting who will benefit from treatment is an urgent and a critical first step towards personalized treatment approaches. M...

MHAGuideNet: a 3D pre-trained guidance model for Alzheimer's Disease diagnosis using 2D multi-planar sMRI images.

BMC medical imaging
BACKGROUND: Alzheimer's Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as structural atrophy. Effective precision diagnosis is crucial for slowing disease progression and red...

Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms.

BMC infectious diseases
BACKGROUND: A significant proportion of individuals with symptoms of sexually transmitted infection (STI) delay or avoid seeking healthcare, and digital diagnostic tools may prompt them to seek healthcare earlier. Unfortunately, none of the currently...

Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements.

Open heart
BACKGROUND: Cardiac amyloidosis (CA) is an underdiagnosed, progressive and lethal disease. Machine learning applied to common measurements derived from routine echocardiogram studies can inform suspicion of CA.

Haptiknit: Distributed stiffness knitting for wearable haptics.

Science robotics
Haptic devices typically rely on rigid actuators and bulky power supply systems, limiting wearability. Soft materials improve comfort, but careful distribution of stiffness is required to ground actuation forces and enable load transfer to the skin. ...

Exploring the acceptance of virtual reality training systems among construction workers: a combined structural equation modeling and artificial neural network approach.

Frontiers in public health
Virtual Reality Training System (VRTS) has been verified effective in safety training in the construction field. However, in China, it is not widely used as a regular training tool. Among all the reasons, the acceptance level of construction workers ...

Profiling the AI speaker user: Machine learning insights into consumer adoption patterns.

PloS one
The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do s...

It's the AI's fault, not mine: Mind perception increases blame attribution to AI.

PloS one
Can artificial intelligences (AIs) be held accountable for moral transgressions? Current research examines how attributing human mind to AI influences the blame assignment to both the AI and the humans involved in real-world moral transgressions. We ...

Line-field confocal optical coherence tomography coupled with artificial intelligence algorithms as tool to investigate wound healing: A prospective, randomized, single-blinded pilot study.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Ablative fractional photothermolysis serves as an excellent in vivo model for studying wound healing. The advent of non-invasive imaging devices, such as line-field confocal optical coherence tomography (LC-OCT), enhances this model by en...