AIMC Topic: ROC Curve

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Neglected brucellosis in pediatric populations from non-endemic regions: Clinical manifestations and prediction of severe disease in Yunnan Province, China.

PLoS neglected tropical diseases
BACKGROUND: Although Yunnan Province is not an endemic region for brucellosis, the disease remains a diagnostic and therapeutic challenge in children due to its atypical clinical manifestations and potential for severe complications.

Comparison of serum lactate and lactate-derived ratios as prognostic biomarkers in pediatric dengue shock syndrome using supervised machine learning models.

PloS one
BACKGROUND: Dengue shock syndrome (DSS), with critical complications encompassing mechanical ventilation (MV), dengue-associated acute liver failure (PALF), and encephalitis, is associated with high mortality in children. Although serum lactate is a ...

μOR-ligand: target-aware view-based hybrid feature selection for μ-opioid receptor ligand functional classification.

Journal of computer-aided molecular design
Understanding active functional class (agonist vs antagonist) at the human μ-opioid receptor (μOR) is critical for drug discovery and safety assessment. While recent machine learning models such as ExtraTrees (ET) and message-passing neural networks ...

Machine learning identifies exosome related gene signatures for early prediction of non-small cell lung cancer.

Scientific reports
Non-small cell lung cancer (NSCLC) remains a major health challenge worldwide, mainly due to the lack of effective early diagnostic biomarkers. Exosome-related genes have recently emerged as potential diagnostic markers due to their roles in tumor pr...

Multi-institutional validation of AI models for classifying urothelial neoplasms in digital pathology.

Scientific reports
This study proposes a deep learning approach for classifying normal, noninvasive, and invasive urothelial neoplasms via digitized histopathologicalimages. Despite many artificial intelligence (AI) models for cancer diagnosis, few focus on bladder les...

Screening mild cognitive impairment using aspects of personal, social, and functional lifestyle: Machine Learning Approaches.

PloS one
OBJECTIVE: Mild cognitive impairment (MCI) signals cognitive decline beyond normal aging and increases dementia risk. Early identification enables preventative interventions, yet many patients in primary care go undetected. This study examines whethe...

A multi stage deep learning model for accurate segmentation and classification of breast lesions in mammography.

Scientific reports
Mammography is a routine imaging technique used by radiologists to detect breast lesions, such as tumors and lumps. Precise lesion detection is critical for early treatment and diagnosis planning. Lesion detection and segmentation are still problemat...

An exploratory study on predicting HER2-positive expression status of breast cancer using ultrasound radiomics combined with machine learning models.

PloS one
OBJECTIVE: This study aimed to investigate the feasibility and potential value of predictive models for human epidermal growth factor receptor 2 (HER2)-positive status in breast cancer (BC) based on radiomics features from conventional ultrasound ima...

Plasma multi-omics and machine learning reveal predictive biomarkers for type 2 diabetes and retinopathy in Qatar biobank cohort.

Journal of translational medicine
BACKGROUND: Type 2 diabetes (T2D) and its vascular complications, including diabetic retinopathy (DR), are escalating in prevalence globally, with disproportionately high prevalence in Middle Eastern populations, where genetic predispositions and lif...

Differentiation of optic disc edema and pseudopapilledema with deep learning on near-infrared reflectance images.

BMC ophthalmology
BACKGROUND: This study aimed to develop an artificial intelligence-based deep learning (DL) algorithm using near-infrared reflectance (NIR) images to differentiate between optic disc edema and pseudopapilledema, and to evaluate the diagnostic perform...