AIMC Topic: Humans

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A novel machine learning-based approach for the detection and analysis of spontaneous synaptic currents.

PloS one
Spontaneous synaptic activity is a hallmark of biological neural networks. A thorough description of these synaptic signals is essential for understanding neurotransmitter release and the generation of a postsynaptic response. However, the complexity...

Deep learning-based prediction of treatment prognosis from nasal polyp histology slides.

International forum of allergy & rhinology
BACKGROUND: Histopathology of nasal polyps contains rich prognostic information, which is difficult to extract objectively. In the present study, we aimed to develop a prognostic indicator of patient outcomes by analyzing scanned conventional hematox...

Deep learning in automatic detection of dysphonia: Comparing acoustic features and developing a generalizable framework.

International journal of language & communication disorders
BACKGROUND: Auditory-perceptual assessment of voice is a subjective procedure. Artificial intelligence with deep learning (DL) may improve the consistency and accessibility of this task. It is unclear how a DL model performs on different acoustic fea...

Efficacy of Robots-Assisted Therapy in Patients With Stroke: A Meta-analysis Update.

The Journal of cardiovascular nursing
BACKGROUND: Robot-assisted therapy (RAT) could address an unmet need to relieve the strain on healthcare providers and intensify treatment in the context of an increasing stroke incidence. A comprehensive meta-analysis could provide firmer data about...

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in dual-energy CT of the abdomen: a phantom and clinical study.

European radiology
OBJECTIVES: To investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to that of other reconstruction algorithms in a phantom experiment and an ...

Improving malicious email detection through novel designated deep-learning architectures utilizing entire email.

Neural networks : the official journal of the International Neural Network Society
In today's email dependent world, cyber criminals often target organizations using a variety of social engineering techniques and specially crafted malicious emails. When successful, such attacks can result in significant harm to physical and digital...

Support vector machine based methodology for classification of thermal images pertaining to breast cancer.

Journal of thermal biology
Breast cancer has been and continues to be a cause of major health concern for women. It is more prevalent in old age, but its incidence has increased in recent years in groups below 50 years old, as in India. According to the Indian Council of Medic...

Gaze-assisted automatic captioning of fetal ultrasound videos using three-way multi-modal deep neural networks.

Medical image analysis
In this work, we present a novel gaze-assisted natural language processing (NLP)-based video captioning model to describe routine second-trimester fetal ultrasound scan videos in a vocabulary of spoken sonography. The primary novelty of our multi-mod...

Leveraging Artificial Intelligence to Enhance Peer Review: Missed Liver Lesions on Computed Tomographic Pulmonary Angiography.

Journal of the American College of Radiology : JACR
PURPOSE: The aim of this study was to use artificial intelligence (AI) to facilitate peer review for detection of missed suspicious liver lesions (SLLs) on CT pulmonary angiographic (CTPA) examinations.

Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers.

La Radiologia medica
OBJECTIVE: To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperie...