AIMC Topic: Humans

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Development of a deep-learning model tailored for HER2 detection in breast cancer to aid pathologists in interpreting HER2-low cases.

Histopathology
AIMS: Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (...

Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.

Journal of biomedical informatics
BACKGROUND: Risk prediction plays a crucial role in planning for prevention, monitoring, and treatment. Electronic Health Records (EHRs) offer an expansive repository of temporal medical data encompassing both risk factors and outcome indicators esse...

Role of different omics data in the diagnosis of schizophrenia disorder: A machine learning study.

Schizophrenia research
Schizophrenia is a serious mental disorder that affects millions of people worldwide. This disorder slowly disintegrates thinking ability and changes behaviours of patients. These patients will show some psychotic symptoms such as hallucinations, del...

Neuropsychological and electrophysiological measurements for diagnosis and prediction of dementia: a review on Machine Learning approach.

Ageing research reviews
INTRODUCTION: Emerging and advanced technologies in the field of Artificial Intelligence (AI) represent promising methods to predict and diagnose neurodegenerative diseases, such as dementia. By using multimodal approaches, Machine Learning (ML) seem...

Identification and diagnosis of schizophrenia based on multichannel EEG and CNN deep learning model.

Schizophrenia research
This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machine learning algorithms, our method autonomously extracts the necessary features for network training...

Disentangling brain atrophy heterogeneity in Alzheimer's disease: A deep self-supervised approach with interpretable latent space.

NeuroImage
Alzheimer's disease (AD) is heterogeneous, but existing methods for capturing this heterogeneity through dimensionality reduction and unsupervised clustering have limitations when it comes to extracting intricate atrophy patterns. In this study, we p...

Can the AI tools ChatGPT and Bard generate energy, macro- and micro-nutrient sufficient meal plans for different dietary patterns?

Nutrition research (New York, N.Y.)
Artificial intelligence chatbots based on large language models have recently emerged as an alternative to traditional online searches and are also entering the nutrition space. In this study, we wanted to investigate whether the artificial intellige...

Automated Method for Intracranial Aneurysm Classification Using Deep Learning.

Sensors (Basel, Switzerland)
Intracranial aneurysm (IA) is now a common term closely associated with subarachnoid hemorrhage. IA is the bulging of a blood vessel caused by a weakening of its wall. This bulge can rupture and, in most cases, cause internal bleeding. In most cases,...

State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset.

Scientific reports
Aiming to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine learning algorithm was traine...