AIMC Topic: Scalp
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Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.
BioMed research international
Jan 29, 2015
The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagn...
Microbial dysbiosis and its diagnostic potential in androgenetic alopecia: insights from multi-kingdom sequencing and machine learning.
mSystems
Jun 17, 2025
Androgenetic alopecia (AGA), the most common form of hair loss, has been linked to dysbiosis of the scalp microbiome. In this study, we collected microbiome samples from the frontal baldness and occipital regions of patients with varying stages of AG...
Realistic Subject-Specific Simulation of Resting State Scalp EEG Based on Physiological Model.
Brain topography
May 13, 2025
Electroencephalography (EEG) recordings are widely used in neuroscience to identify healthy individual brain rhythms and to detect alterations associated with various brain diseases. However, understanding the cellular origins of scalp EEG signals an...
An Automatic AI-Based Algorithm That Grades the Scalp Surface Exfoliating Process From Video Imaging. Application to Dandruff Severity and Its Validation on Subjects of Different Ages and Ethnicities.
Journal of cosmetic dermatology
Apr 1, 2025
OBJECTIVES: To evaluate the technical assets of a new imaging device that, wifi linked to a AI based algorithm, automatically grades in vivo the exfoliating process of the skin, taking dandruff as model.
Artificial Intelligence-Based Personalization of Treatment Regimen for Hair Loss: A 6-Month Clinical Trial.
Journal of drugs in dermatology : JDD
Mar 1, 2025
Hair loss affects up to 50% of women and 80% of men. The high costs and wait times for professional consultations lead many to seek one-size-fits-all solutions that are frequently ineffective. This study tested an artificial intelligence (AI) model f...
Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders.
Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
Apr 1, 2024
BACKGROUND: Hair and scalp disorders present a significant challenge in dermatology due to their clinical diversity and overlapping symptoms, often leading to misdiagnoses. Traditional diagnostic methods rely heavily on clinical expertise and are lim...
Artificial Intelligence-based Detection of Epileptic Discharges from Pediatric Scalp Electroencephalograms: A Pilot Study.
Acta medica Okayama
Dec 1, 2022
We developed an artificial intelligence (AI) technique to identify epileptic discharges (spikes) in pediatric scalp electroencephalograms (EEGs). We built a convolutional neural network (CNN) model to automatically classify steep potential images int...
Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning.
Sleep
Nov 12, 2020
STUDY OBJECTIVES: Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings.
Clinically Applicable Deep Learning Framework for Measurement of the Extent of Hair Loss in Patients With Alopecia Areata.
JAMA dermatology
Sep 1, 2020
This study aims to develop a deep learning framework to determine the Severity of Alopecia Tool (SALT) score for measurement of hair loss in patients with alopecia areata.
Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challenging problem. Deep learning methods can be an automated way to perform this task. In this work, we pres...
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