AIMC Topic: Humans

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Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.

Scientific reports
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and a...

A platform combining automatic segmentation and automatic measurement of the maxillary sinus and adjacent structures.

Clinical oral investigations
OBJECTIVES: To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.

Identification of Novel Biomarkers for Ischemic Stroke Through Integrated Bioinformatics Analysis and Machine Learning.

Journal of molecular neuroscience : MN
Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints for effective treatment. Predictive biomarkers demonstrate great potential in the clinical diagnosis of ischemic stroke, significantly enhancing the ...

Detecting noncredible symptomology in ADHD evaluations using machine learning.

Journal of clinical and experimental neuropsychology
INTRODUCTION: Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are becoming increasingly complicated by the number of adults who fabricate or exaggerate symptoms. Novel methods are needed to improve the assessment process re...

Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management.

Journal of clinical research in pediatric endocrinology
OBJECTIVE: The honeymoon phase in type 1 diabetes (T1D) represents a temporary improvement in glycemic control but may complicate insulin management. The aim was to develop and validate a machine learning (ML)-driven method for accurately detecting t...

Risk score stratification of cutaneous melanoma patients based on whole slide images analysis by deep learning.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: There is a need to improve risk stratification of primary cutaneous melanomas to better guide adjuvant therapy. Taking into account that haematoxylin and eosin (HE)-stained tumour tissue contains a huge amount of clinically unexploited mo...

Psychophysiological foundations of human physical activity behavior and motivation: theories, systems, mechanisms, evolution, and genetics.

Physiological reviews
Physical activity is a meaningful part of life that starts before birth and lasts until death. There are many health benefits to be derived from physical activity; hence, regular engagement is recommended on a weekly basis. However, these recommendat...

Machine-learning model based on ultrasomics for non-invasive evaluation of fibrosis in IgA nephropathy.

European radiology
OBJECTIVES: To develop and validate an ultrasomics-based machine-learning (ML) model for non-invasive assessment of interstitial fibrosis and tubular atrophy (IF/TA) in patients with IgA nephropathy (IgAN).

Intestinal Microbiome Modulation of Therapeutic Efficacy of Cancer Immunotherapy.

Gastroenterology clinics of North America
Bacteria are associated with certain cancers and may induce genetic instability and cancer progression. The gut microbiome modulates the response to cancer therapy. Training machine learning models with response associated taxa or bacterial genes pre...

A radiomics model combining machine learning and neural networks for high-accuracy prediction of cervical lymph node metastasis on ultrasound of head and neck squamous cell carcinoma.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: This study aimed to develop an ultrasound image-based radiomics model for diagnosing cervical lymph node (LN) metastasis in patients with head and neck squamous cell carcinoma (HNSCC) that shows higher accuracy than previous models.