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Imaging-based machine learning to evaluate the severity of ischemic stroke in the middle cerebral artery territory.

OBJECTIVES: This study aims to develop an imaging-based machine learning model for evaluating the severity of ischemic stroke in the middle cerebral artery (MCA) territory. METHODS: This retrospective study included 173 patients diagnosed with acute ischemic stroke (AIS) in the MCA territory from two centers, with 114 in the training set and 59 in the test set. In the training set, spearman correlation coefficient and multiple linear regression were utilized to analyze the correlation between the CT imaging features of patients prior to treatment and the national institutes of health stroke s

Accuracy of the 0/1-Hour Algorithm for Diagnosing Myocardial Infarction in Patients With Atrial Fibrillation.

BACKGROUND: Patients with atrial fibrillation (AF) often present with symptoms similar to acute coronary syndrome (ACS), including chest pain and elevated levels of high-sensitivity cardiac troponin (hs-cTn). The 0/1-hour algorithm using hs-cTn is a rapid diagnostic tool endorsed by the European Society of Cardiology to rule out myocardial infarction (MI). However, because its effectiveness in patients with AF remains unclear, in this study we assessed the diagnostic accuracy of the 0/1-hour algorithm in patients with and without AF presenting with chest pain in the emergency department. METH

S4D-ECG: A Shallow State-of-the-Art Model for Cardiac Abnormality Classification.

PURPOSE: This study introduces an algorithm specifically designed for processing unprocessed 12-lead electrocardiogram (ECG) data, with the primary aim of detecting cardiac abnormalities. METHODS: The proposed model integrates Diagonal State Space Sequence (S4D) model into its architecture, leveraging its effectiveness in capturing dynamics within time-series data. The S4D model is designed with stacked S4D layers for processing raw input data and a simplified decoder using a dense layer for predicting abnormality types. Experimental optimization determines the optimal number of S4D layers, s

Detection of cognitive impairment using a machine-learning algorithm.

PURPOSE: The Mini-Mental State Examination (MMSE) is one of the most frequently used bedside screening measures of cognition. However, the Korean Dementia Screening Questionnaire (KDSQ) is an easier and more reliable screening method. Instead, other clinical variables and raw data were used for this study without the consideration of a cutoff value. The objective of this study was to develop a machine-learning algorithm for the detection of cognitive impairment (CI) based on the KDSQ and the MMSE. PATIENTS AND METHODS: The original dataset from the Clinical Research Center for Dementia of Sou

An Algorithmic Approach Using Ultrasonography in the Diagnosis of Pediatric Nasal Bone Fracture.

BACKGROUND: Ultrasonography (US) was recently reported as a reliable modality for diagnosing nasal bone fractures. However, whether US is reliable as a screening tool in the pediatric emergency department (ED) remains unknown. This prospective cohort study had a 2-fold aim: to assess the utility of US in the diagnosis of pediatric nasal bone fracture, and to evaluate the validity of our protocol for managing pediatric nasal bone fractures (Fuchu-Kids algorithm). PATIENTS AND METHODS: Among the patients who presented at the ED with facial trauma, those with a suspected nasal bone fracture were

Prognosis of hip osteonecrosis after cell therapy with a calculator and artificial intelligence: ten year collapse-free survival prediction on three thousand and twenty one hips.

PURPOSE: Several reports have identified prognostic factors for hip osteonecrosis treated with cell therapy, but no study investigated the accuracy of artificial intelligence method such as machine learning and artificial neural network (ANN) to predict the efficiency of the treatment. We determined the benefit of cell therapy compared with core decompression or natural evolution, and developed machine-learning algorithms for predicting ten year collapse-free survival in hip osteonecrosis treated with cell therapy. Using the best algorithm, we propose a calculator for "prognosis hip osteonecro

Does TikTok contribute to eating disorders? A comparison of the TikTok algorithms belonging to individuals with eating disorders versus healthy controls.

TikTok employs sophisticated algorithms to deliver users increasingly personalised content over time. We investigated the potential for these algorithms to exacerbate eating disorder symptoms by analysing 1.03 million TikTok videos delivered to 42 individuals with eating disorders (76 % anorexia nervosa) and 49 healthy controls over one month. Within this video corpus, we identified four video categories relevant to eating disorder psychopathology: appearance-oriented videos, dieting videos, exercise videos, and toxic eating disorder (akin to "pro-anorexia") videos. Multi-level models predicte

Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data.

BACKGROUND: With the increase in the amount of DNA methylation and gene expression data, the epigenetic mechanisms of cancers can be extensively investigate. Available methods integrate the DNA methylation and gene expression data into a network by specifying the anti-correlation between them. However, the correlation between methylation and expression is usually unknown and difficult to determine. RESULTS: To address this issue, we present a novel multiple network framework for epigenetic modules, namely, Epigenetic Module based on Differential Networks (EMDN) algorithm, by simultaneously an

A Validated Algorithm to Identify Hepatic Decompensation in the Veterans Health Administration Electronic Health Record System.

PURPOSE: Accurate identification of hepatic decompensation is essential for pharmacoepidemiologic research among patients with chronic liver disease. METHODS: An algorithm using ≥ 1 inpatient or ≥ 2 outpatient International Classification of Diseases, 10th revision (ICD-10) codes for hepatic decompensation was developed in Veterans Health Administration data from October 2015 through July 2019. Medical records were reviewed by hepatologists to confirm cases. The positive predictive value (PPV) of the coding algorithm for confirmed hepatic decompensation was calculated. RESULTS: Hepatic decom

Improving the Drilling Parameter Optimization Method Based on the Fireworks Algorithm.

The rate of penetration (ROP) is a manifestation of drilling efficiency, and optimizing drilling parameters is an important way to improve it. To achieve a low ROP for a Permian formation in a certain oil and gas field, three single wells in this formation were selected for optimization. An improved fireworks optimization algorithm was proposed for drilling parameter optimization. We first established the objective function that predicted the ROPs for the three wells. The objective function employed a multilayer perceptron neural network as the optimization adaptation function. We then optimiz

Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults.

BACKGROUND: The aim of this study was to analyse the risk factors that affect oral health in adults and to evaluate the success of different machine learning algorithms in predicting these risk factors. METHODS: This study included 2000 patients aged 18 years and older who were admitted to the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Gaziantep University, between September and December 2023. In this study, patients completed a 30-item questionnaire designed to assess the factors that affect the decayed, missing, and filled teeth (DMFT). Clinical and radiological e

Exploring unsupervised feature extraction algorithms: tackling high dimensionality in small datasets.

Small datasets are common in many fields due to factors such as limited data collection opportunities or privacy concerns. These datasets often contain high-dimensional features, yet present significant challenges of dimensionality, wherein the sparsity of data in high-dimensional spaces makes it difficult to extract meaningful information and less accurate predictive models are produced. In this regard, feature extraction algorithms are important in addressing these challenges by reducing dimensionality while retaining essential information. These algorithms can be classified into supervised,

A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging.

BACKGROUND: Precise detection and characterization of pulmonary nodules on computed tomography (CT) is crucial for early diagnosis and management. OBJECTIVES: In this study, we propose the use of a deep learning-based algorithm to automatically detect pulmonary nodules in computed tomography (CT) scans. We evaluated the performance of the algorithm against the interpretation of radiologists to analyze the effectiveness of the algorithm. MATERIALS AND METHODS: The study was conducted in collaboration with a tertiary cancer center. We used a collection of public (LUNA) and private (tertiary ca

Mathematical modeling and intelligent methods in systems biology and medicine applied to complex diseases: An overview.

This article presents a comprehensive analysis of the intersection between mathematical models, classic algorithmic simulations as well as with machine learnig, alongside their applications, in biology, medicine and bioinformatics. From the first models on population growth and competition, we review the evolution of formalizations to present modeling of complex diseases such as diabetes, cancer, aging, and neurodegenerative disorders. Mathematical models are introduced and categorized, alongside the algorithms and computational methods from deterministic models on exponential and logistic gro

Применение искусственного интеллекта в перипостетической суставной инфекции.

Перипростетическая суставная инфекция (ПСО) представляет собой one Из наиболее разрушительных осложнений, вызванных общей совместной артропластикой, часто требующих дополнительных операций и антимикробной терапии, и потенциально ведущих к инвалидности. Это значительно увеличивает нагрузку как на пациентов, так и на систему здравоохранения. С учетом значительных страданий, причиняемых ПСО, его профилактика и лечение уже давно являются ключевыми факторами, вызывающими озабоченность. Вместе с тем сохраняются проблемы, связанные с точной оценкой индивидуального риска, предотвращением инфекции, сов

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… Table 3 Comparison of risk prediction algorithm and risk calculator for PJI. … Table 4 Summary of risk prediction algorithms for PJI. …

Oral Health Indicators as Important Predictors of Frailty in Korean Adults Aged 50 Years and Older: A Sex-Stratified Explainable Machine Learning Approach.

BackgroundFrailty is a major public health concern, yet oral health indicators are rarely incorporated into prediction models.ObjectiveTo develop sex-stratified explainable machine-learning models for frailty prediction.MethodsCross-sectional data from 12,302 adults aged ≥50 years (KNHANES 2016-2019) were analyzed. Frailty was defined using a modified Fried phenotype, with robust and prefrail participants combined as non-frail. Five algorithms were developed separately for men and women and evaluated on independent test sets. Top-performing models were interpreted using SHAP.ResultsThe survey-

Machine learning-enabled early risk stratification of β-Iactam-induced electrolyte imbalances.

AIMS: To estimate the incidence of β-lactam/β-lactamase inhibitor - associated electrolyte imbalances and develop an internally validated, interpretable prediction model for early risk identification. PATIENTS AND METHODS: We retrospectively analyzed 240 hospitalized adults treated with piperacillin - tazobactam or ampicillin - sulbactam. Electrolyte imbalance was defined as any post-treatment abnormality in sodium, potassium, chloride, calcium, or phosphate using CTCAE v5.0 and predefined reference ranges. Predictors were evaluated using logistic regression and machine learning - based featu

Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery.

Blood transfusion prediction studies in pediatric cardiac surgery have employed direct regression, two-stage, and multi-class classification paradigms, but the fundamentally different outputs of these paradigms have prevented direct head-to-head comparison. We developed a dual-mean absolute error (MAE) composite metric that converts all predictions to continuous transfusion volume estimates, enabling unified comparison across paradigms. Using data from 3,342 pediatric patients undergoing congenital heart surgery, we evaluated six imputation methods, three class-balancing strategies, and fiftee

From Molecular Pathways to Artificial Intelligence: Advancing the Understanding and Management of Osteosarcopenia.

Osteosarcopenia, defined as the coexistence of low bone mass and sarcopenia, is a multifactorial condition influenced by molecular crosstalk between bone and muscle. Growing evidence suggests that this interaction contributes to disease progression and highlights the need for integrated diagnostic and therapeutic approaches. Artificial intelligence (AI) is emerging as an important tool for the clinical management of osteosarcopenia, with potential applications in risk prediction, early diagnosis, evaluation of adverse outcomes such as fractures and falls, imaging analysis, and personalized tre

Artificial Intelligence in Proton Therapy: What Works, What Does Not, and What Is Next.

Artificial intelligence is increasingly shaping the evolution of proton therapy, with applications spanning imaging, treatment planning, quality assurance, adaptive workflows, and outcome modeling. Unlike conventional task-specific algorithms, modern AI methods, including machine learning and deep learning, enable integration of heterogeneous data and capture complex relationships across the clinical workflow. These capabilities are particularly relevant in proton therapy, where sensitivity to range uncertainty, anatomic variation, and biological heterogeneity presents persistent clinical and