AIMC Topic: Humans

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Human arm endpoint-impedance in rhythmic human-robot interaction exhibits cyclic variations.

PloS one
Estimating the human endpoint-impedance interacting with a physical environment provides insights into goal-directed human movements during physical interactions. This work examined the endpoint-impedance of the upper limb during a hybrid ball-bounci...

Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images.

PloS one
COVID-19 has potential consequences on the pulmonary and cardiovascular health of millions of infected people worldwide. Chest computed tomographic (CT) imaging has remained the first line of diagnosis for individuals infected with SARS-CoV-2. Howeve...

Deep Unsupervised Representation Learning for Feature-Informed EEG Domain Extraction.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In electroencephalography (EEG) classification paradigms, data from a target subject is often difficult to obtain, leading to difficulties in training a robust deep learning network. Transfer learning and their variations are effective tools in impro...

Deep learning-based automated detection of the dental crown finish line: An accuracy study.

The Journal of prosthetic dentistry
STATEMENT OF PROBLEM: The marginal fit of dental prostheses is a clinically significant issue, and dental computer-aided design software programs use automated methods to expedite the extraction of finish lines. The accuracy of these automated method...

Assessing the Utility of a Machine-Learning Model to Assist With the Assignment of the American Society of Anesthesiology Physical Status Classification in Pediatric Patients.

Anesthesia and analgesia
BACKGROUND: The American Society of Anesthesiologists Physical Status Classification System (ASA-PS) is used to classify patients' health before delivering an anesthetic. Assigning an ASA-PS Classification score to pediatric patients can be challengi...

Deconstructing depression by machine learning: the POKAL-PSY study.

European archives of psychiatry and clinical neuroscience
Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize depression in about 50% of cases mainly due to somatic comorbidities. Given the significant economic,...

Select or adjust? How information from early treatment stages boosts the prediction of non-response in internet-based depression treatment.

Psychological medicine
BACKGROUND: Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these ...

Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls.

Seminars in ophthalmology
Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (...

Artificial intelligence in the practice of forensic medicine: a scoping review.

International journal of legal medicine
Forensic medicine is a thriving application field for artificial intelligence (AI). Indeed, AI applications intended to forensic pathologists or forensic physicians have emerged since the last decade. For example, AI models were developed to help est...

Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study.

Anaesthesia
While videolaryngoscopy has resulted in better overall success rates of tracheal intubation, airway assessment is still an important prerequisite for safe airway management. This study aimed to create an artificial intelligence model to identify diff...