The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learnin...
Background: MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses,...
This review article presents an appraisal of pioneering technologies poised to revolutionize the diagnosis and management of aortic aneurysm disease, with a primary focus on the thoracic aorta while encompassing insights into abdominal manifestations...
Excitation–emission matrix (EEM) fluorescence spectroscopy, when combined with machine learning algorithms, has emerged as a highly promising tool for non-invasive biomedical diagnosis, demonstrating significant potential across various applica...
Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates for diseases. With the smallest non-coding RNAs—piwi-RNAs (piRNAs)—and their transcripts, we sought to identify if these piRNAs could be us...
Pediatric appendicitis remains a challenging condition to diagnose accurately due to its varied clinical presentations and the non-specific nature of symptoms, particularly in younger patients. Traditional diagnostic approaches often result in delaye...
Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However,...
Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feas...
The digestion of protein into peptide fragments reduces the size and complexity of protein molecules. Peptide fragments can be analyzed with higher sensitivity (often > 102 fold) and resolution using MALDI-TOF mass spectrometers, leading to improv...
Frequency Response Analysis (FRA) is a proven method for detecting mechanical faults in transformers, such as winding deformations and short circuits. However, traditional FRA interpretation relies heavily on visual and subjective comparison of frequ...
A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigati...
CRISPR-based diagnostics now detect viral, bacterial, and cancer-associated nucleic acids with sensitivities approaching quantitative PCR; however, their translation to decentralized care rests on computational design and interpretation that current...
The estimation of the Instantaneous Angular Speed (IAS) has in recent years attracted a growing interest in the diagnostics of rotating machines. Measurement of the IAS can be used as a source of information of the machine condition per se, or for pe...
This paper aims to update the review of diagnostic techniques for rotating electrical machines of different type and size. Each of the main sections of the paper is focused on a specific component of the machine (stator and rotor windings, magnets, b...
Background/Objectives: Obsessive–Compulsive Disorder (OCD) is a chronic mental health condition characterized by intrusive thoughts and repetitive behaviors. Traditional diagnostic methods rely on subjective clinical assessments, delaying effec...
Artificial intelligence (AI) is reshaping precision medicine by revealing diagnostic links between ocular biomarkers and systemic musculoskeletal disorders. This review synthesizes clinical evidence on the associations between optical coherence tomog...
In the context of the digital transformation of industrial production, the need for intelligent maintenance and repair systems capable of ensuring reliable operation of machine-tool equipment without operator involvement is growing. This present stud...
Chronic obstructive pulmonary disease (COPD) is highly underdiagnosed, and early detection is urgent to prevent advanced progression. Circulating microRNAs (miRNAs) have been diagnostic candidates for multiple diseases. However, their diagnostic valu...
Machine components are subject to a wide range of damage and failure processes, and their correct identification is essential for reliable operation, effective maintenance, and accurate diagnosis. However, a persistent gap exists between morphology-b...
The paper presents probabilistic aspects of diagnostics of grinding processes with consideration of metrological aspects of evaluation of topography of machined surfaces and selected problems of assessment of machining accuracy. The processes of crea...
Background. Robotic-assisted bronchoscopy with the ION™ Endoluminal System facilitates precise access to peripheral pulmonary lesions. However, procedural duration and diagnostic performance remain influenced by patient and lesion-specific fact...
This bibliometric review investigates the advancements in machine learning techniques for predictive maintenance, focusing on the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for fault detection in wheelset axle bearing...
The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the...
Objective: This paper explores machine learning methods for exercise-induced laryngeal obstruction (EILO) diagnostics. Traditional diagnostic approaches like CLE scoring face subjectivity, limiting precise objective assessments. Machine learning is i...
In this meta-analysis, we aimed to estimate the diagnostic accuracy of machine learning models on digital mammograms and tomosynthesis in breast cancer classification and to assess the factors affecting its diagnostic accuracy. We searched for relate...
Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to gre...
This paper provides comprehensive insights into the cornea and its diseases, with a particular focus on keratoconus. This paper explores the cornea’s function in maintaining ocular health, detailing its anatomy, pathological conditions, and the...
Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status,...
Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its applicatio...
The accurate diagnosis of keratoconus, especially in its early stages of development, allows one to utilise timely and proper treatment strategies for slowing the progression of the disease and provide visual rehabilitation. Various keratometry indic...
The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in...
No prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and ligh...
Background: Inflammation of unknown origin (IUO) represents a persistent clinical challenge, often requiring extensive diagnostic efforts despite nonspecific inflammatory findings such as elevated C-reactive protein (CRP) and erythrocyte sedimentatio...
In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive a...
The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, ma...
Antibody testing in inflammatory bowel disease (IBD) can add to diagnostic accuracy of the main subtypes Crohn’s disease (CD) and ulcerative colitis (UC). Whether modern modeling techniques such as supervised and unsupervised machine learning a...
This paper deals with the topic of composite insulation materials for rotating machines and it is primarily pointed to the synthesis of new three-component insulation system. In connection with this research, the basic components of the insulation sy...
Induction motors are a common component of electromechanical equipment in mining operations, yet they are susceptible to failures resulting from frequent start–stops, overloading, wear and tear, and component failure. It is evident that such fa...
Background: This study demonstrates differences in the distribution of multiple cardiovascular biomarkers between non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA) patients. Diagnostic machine learning predictive models...
Significance: We describe a novel, specimen-free diagnostic platform that can immediately detect both a metabolite (glucose) or an infection (COVID-19) by non-invasively using Raman spectroscopy and machine learning. Aim: Current diagnostic testing f...
Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including...
Diabetic kidney disease (DKD) affects approximately 40% of patients with diabetes mellitus and remains a leading cause of end-stage renal disease worldwide. Early diagnosis and identification of therapeutic targets are critical for improving patient...
8 May 2024
Background: Rheumatic diseases are chronic diseases that affect joints, tendons, ligaments, bones, muscles, and other vital organs. Detection of rheumatic diseases is a complex process that requires careful analysis of heterogeneous content from clin...
Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC&rsquo...
Background: Multiple myeloma (MM) diagnosis requires specialised testing that is not universally available, contributing to diagnostic delays. The aim of this study was to develop and internally validate statistical and machine learning (ML) models t...
Protein damage by glycation, oxidation and nitration is a continuous process in the physiological system caused by reactive metabolites associated with dicarbonyl stress, oxidative stress and nitrative stress, respectively. The term AGEomics is defin...
Background/Objectives: Right heart catheterization (RHC) is the diagnostic gold standard for pulmonary hypertension (PH), defined as mean pulmonary arterial pressure (mPAP) greater than 20 mmHg at rest. It is invasive and carries procedural risk. Mac...
Cardiovascular disease (CVD) is the leading cause of death. CVD symptoms may develop within a short-term after diagnostic catheterizations and lead to life-threatening situations. This study is the first to apply machine learning (ML) methods to pred...
Random Lasing (RL) is based on the interaction of an optical amplifying system and a highly scattering medium that produces the optical feedback necessary for laser emission. RL has demonstrated its potential as a diagnostic tool in biomedicine when...
Background/Objectives: Computerized diagnostic algorithms could achieve early detection of acute kidney injury (AKI) only with available baseline serum creatinine (SCr). To tackle this weakness, we tried to construct a machine learning model for AKI...
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