AIMC Topic: Adult

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Mucoepidermoid carcinoma: Enhancing diagnostic accuracy and treatment strategy through machine learning models and web-based prognostic tool.

Journal of stomatology, oral and maxillofacial surgery
BACKGROUND: Oral cancer, particularly mucoepidermoid carcinoma (MEC), presents diagnostic challenges due to its histological diversity and rarity. This study aimed to develop machine learning (ML) models to predict survival outcomes for MEC patients ...

Evaluation of cerebral blood flow after subarachnoid hemorrhage using near-field coupling and machine learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundBedside continuous monitoring of cerebral blood flow (CBF) has significant implications in guiding individualized management and improving the prognosis of subarachnoid hemorrhage (SAH).ObjectiveThis study established a CBF monitoring syste...

Identifying potential medical aid beneficiaries using machine learning: A Korean Nationwide cohort study.

International journal of medical informatics
OBJECTIVE: To identify potential medical aid beneficiaries using demographic and medical history of individuals and analyzing important features qualitatively.

Diagnosis of major depressive disorder using a novel interpretable GCN model based on resting state fMRI.

Neuroscience
The diagnosis and analysis of major depressive disorder (MDD) faces some intractable challenges such as dataset limitations and clinical variability. Resting-state functional magnetic resonance imaging (Rs-fMRI) can reflect the fluctuation data of br...

Mortality Prediction in Patients With Breast Cancer by Artificial Neural Network Model and Elastic Net Regression.

Journal of research in health sciences
BACKGROUND: Breast cancer (BC) is the most common cancer in women, and it is important to identify models that can accurately predict mortality in patients with this cancer. The aim of the present study was to use the elastic net regression and artif...

Radiomics and deep learning models for glioblastoma treatment outcome prediction based on tumor invasion modeling.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: We investigate the feasibility of using a biophysically guided approach for delineating the Clinical Target Volume (CTV) in Glioblastoma Multiforme (GBM) by evaluating its impact on the treatment outcomes, specifically Overall Survival (OS) ...

Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models.

Sensors (Basel, Switzerland)
The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over tradition...

Construction of a random survival forest model based on a machine learning algorithm to predict early recurrence after hepatectomy for adult hepatocellular carcinoma.

BMC cancer
BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) exhibits a propensity for early recurrence following liver resection, resulting in a bleak prognosis. At present, majority of the predictive models for the early postoperative recurrence of HCC rely...

Value of the combination of intraepithelial tumor-infiltrating lymphocyte density and the heterogeneity of density as a prognostic marker in stage III colorectal cancers.

Histology and histopathology
Tumor-infiltrating lymphocyte (TIL) density is both a prognostic and a predictive factor in colorectal cancer (CRC). Whether the heterogeneity of TIL density across the tumor plays an important role in the clinical outcome of CRC is not well known. A...

Ensemble learning-based radiomics model for discriminating brain metastasis from glioblastoma.

European journal of radiology
OBJECTIVE: Differentiating between brain metastasis (BM) and glioblastoma (GBM) preoperatively is challenging due to their similar imaging features on conventional brain MRI. This study aimed to enhance diagnostic accuracy through a machine learning ...