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Interpretable deep learning survival predictions in sporadic Creutzfeldt-Jakob disease.

Journal of neurology
BACKGROUND: Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive and fatal prion disease with significant public health implications. Survival is heterogenous, posing challenges for prognostication and care planning. We developed a surv...

Utilizing machine learning approaches to investigate the relationship between cystatin C and serious complications in esophageal cancer patients after esophagectomy.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
BACKGROUND: The purpose of this study is to investigate the relationship between preoperative cystatin C levels and the risk of serious postoperative complications in esophageal cancer (EC) patients, utilizing advanced machine learning (ML) methodolo...

Development of a machine learning model for prediction of intraventricular hemorrhage in premature neonates.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
PURPOSE: Intraventricular hemorrhage (IVH) is a common and severe complication in premature neonates, leading to long-term neurological impairments. Early prediction and identification of risk factors for IVH in premature neonates are crucial for imp...

Swing limb detection using a convolutional neural network and a sequential hypothesis test based on foot pressure data during gait initialization in individuals with Parkinson's disease.

Physiological measurement
. Start hesitation is a key issue for individuals with Parkinson's disease (PD) during gait initiation. Visual cues have proven effective in enhancing gait initiation. When applied to laser-light shoes, swing-limb detection efficiently activates the ...

Public health perspectives on green efficiency through smart cities, artificial intelligence for healthcare and low carbon building materials.

Frontiers in public health
INTRODUCTION: Smart cities, artificial intelligence (AI) in healthcare, and low-carbon building materials are pivotal to public health, environmental sustainability, and green efficiency. Despite their critical importance, understanding public percep...

Impaired interhemispheric synchrony in patients with iridocyclitis and classification using machine learning: an fMRI study.

Frontiers in immunology
BACKGROUND: This study examined the interhemispheric integration function pattern in patients with iridocyclitis utilizing the voxel-mirrored homotopic connectivity (VMHC) technique. Additionally, we investigated the ability of VMHC results to distin...

DFASGCNS: A prognostic model for ovarian cancer prediction based on dual fusion channels and stacked graph convolution.

PloS one
Ovarian cancer is a malignant tumor with different clinicopathological and molecular characteristics. Due to its nonspecific early symptoms, the majority of patients are diagnosed with local or extensive metastasis, severely affecting treatment and p...

Estimation of foveal avascular zone area from a B-scan OCT image using machine learning algorithms.

PloS one
PURPOSE: The objective of this study is to estimate the area of the Foveal Avascular Zone (FAZ) from B-scan OCT images using machine learning algorithms.

Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models.

Ultrasound in medicine & biology
OBJECTIVE: Segmentation of brain sulci in pre-term infants is crucial for monitoring their development. While magnetic resonance imaging has been used for this purpose, cranial ultrasound (cUS) is the primary imaging technique used in clinical practi...

Machine learning and multi-omics characterization of SLC2A1 as a prognostic factor in hepatocellular carcinoma: SLC2A1 is a prognostic factor in HCC.

Gene
Hepatocellular carcinoma (HCC) is characterized by high incidence, significant mortality, and marked heterogeneity, making accurate molecular subtyping essential for effective treatment. Using multi-omics data from HCC patients, we applied diverse cl...