BACKGROUND: Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling. Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility. Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns. METHODS: We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex. The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics. The model's performance was compared against four expert clinicians using 14 previously unseen MRI scans. RESULTS: Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs. The model achieved a balanced accuracy of 64.8% ± 3.4%, with a weighted top-3 accuracy of 84.7% ± 1.8% and top-5 accuracy of 90.2% ± 2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making. Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0% ± 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. CONCLUSIONS: The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques. Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform.

• MeSH
• Adult MeSH
• Internet MeSH
• Middle Aged MeSH
• Humans MeSH
• Magnetic Resonance Imaging * methods   MeSH
• Neuromuscular Diseases * diagnosis   diagnostic imaging   MeSH
• Machine Learning * MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional diagnostic methods face challenges such as variability in expert opinions and limited integration of advanced imaging techniques. This prospective cohort study investigates a novel multimodal approach, integrating traditional methods with advanced machine learning techniques. We studied 181 patients who underwent fine-needle aspiration (FNA) biopsy, each contributing one nodule, resulting in a total of 181 nodules for our analysis. Data collection included sex, age, and ultrasound imaging, which incorporated elastography. Features extracted from these images included Thyroid Imaging Reporting and Data System (TIRADS) scores, elastography parameters, and radiomic features. The pathological results based on the FNA biopsy, provided by the pathologists, served as our gold standard for nodule classification. Our methodology, termed ELTIRADS, combines these features with interpretable machine learning techniques. Performance evaluation showed that a Support Vector Machine (SVM) classifier using TIRADS, elastography data, and radiomic features achieved high accuracy (0.92), with sensitivity (0.89), specificity (0.94), precision (0.89), and F1 score (0.89). To enhance interpretability, we used hierarchical clustering, shapley additive explanations (SHAP), and partial dependence plots (PDP). This combined approach holds promise for enhancing the accuracy of thyroid nodule malignancy detection, thereby contributing to advancements in personalized and precision medicine in the field of thyroid cancer research.

• MeSH
• Adult MeSH
• Elasticity Imaging Techniques * methods   MeSH
• Middle Aged MeSH
• Humans MeSH
• Thyroid Neoplasms diagnostic imaging   classification   pathology   diagnosis   MeSH
• Prospective Studies MeSH
• Radiomics MeSH
• Aged MeSH
• Thyroid Gland diagnostic imaging   pathology   MeSH
• Machine Learning * MeSH
• Support Vector Machine MeSH
• Biopsy, Fine-Needle MeSH
• Thyroid Nodule * diagnostic imaging   pathology   classification   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. METHODS: This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models. RESULTS: The proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications. CONCLUSIONS: The proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.

• MeSH
• Databases, Factual MeSH
• Humans MeSH
• Neural Networks, Computer MeSH
• Patient-Centered Care * MeSH
• Machine Learning * MeSH
• Translational Science, Biomedical MeSH
• Translational Research, Biomedical MeSH
• Artificial Intelligence * MeSH
• Treatment Outcome MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: Prognostic machine learning research in multiple sclerosis has been mainly focusing on black-box models predicting whether a patients' disability will progress in a fixed number of years. However, as this is a binary yes/no question, it cannot take individual disease severity into account. Therefore, in this work we propose to model the time to disease progression instead. Additionally, we use explainable machine learning techniques to make the model outputs more interpretable. METHODS: A preprocessed subset of 29,201 patients of the international data registry MSBase was used. Disability was assessed in terms of the Expanded Disability Status Scale (EDSS). We predict the time to significant and confirmed disability progression using random survival forests, a machine learning model for survival analysis. Performance is evaluated on a time-dependent area under the receiver operating characteristic and the precision-recall curves. Importantly, predictions are then explained using SHAP and Bellatrex, two explainability toolboxes, and lead to both global (population-wide) as well as local (patient visit-specific) insights. RESULTS: On the task of predicting progression in 2 years, the random survival forest achieves state-of-the-art performance, comparable to previous work employing a random forest. However, here the random survival forest has the added advantage of being able to predict progression over a longer time horizon, with AUROC >60% for the first 10 years after baseline. Explainability techniques further validated the model by extracting clinically valid insights from the predictions made by the model. For example, a clear decline in the per-visit probability of progression is observed in more recent years since 2012, likely reflecting globally increasing use of more effective MS therapies. CONCLUSION: The binary classification models found in the literature can be extended to a time-to-event setting without loss of performance, thus allowing a more comprehensive prediction of patient prognosis. Furthermore, explainability techniques proved to be key to reach a better understanding of the model and increase validation of its behaviour.

• MeSH
• Algorithms MeSH
• Time Factors MeSH
• Adult MeSH
• Humans MeSH
• Prognosis MeSH
• Disease Progression * MeSH
• Registries MeSH
• ROC Curve MeSH
• Multiple Sclerosis * physiopathology   MeSH
• Machine Learning * MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Over recent decades, advancements in omics technologies, such as proteomics, genomics, epigenomics, metabolomics, transcriptomics, and microbiomics, have significantly enhanced our understanding of the molecular mechanisms underlying various physiological and pathological processes. Nonetheless, the analysis and interpretation of vast omics data concerning reproductive diseases are complicated by the cyclic regulation of hormones and multiple other factors, which, in conjunction with a genetic makeup of an individual, lead to diverse biological responses. Reproductomics investigates the interplay between a hormonal regulation of an individual, environmental factors, genetic predisposition (DNA composition and epigenome), health effects, and resulting biological outcomes. It is a rapidly emerging field that utilizes computational tools to analyze and interpret reproductive data, with the aim of improving reproductive health outcomes. It is time to explore the applications of reproductomics in understanding the molecular mechanisms underlying infertility, identification of potential biomarkers for diagnosis and treatment, and in improving assisted reproductive technologies (ARTs). Reproductomics tools include machine learning algorithms for predicting fertility outcomes, gene editing technologies for correcting genetic abnormalities, and single cell sequencing techniques for analyzing gene expression patterns at the individual cell level. However, there are several challenges, limitations and ethical issues involved with the use of reproductomics, such as the applications of gene editing technologies and their potential impact on future generations are discussed. The review comprehensively covers the applications and advancements of reproductomics, highlighting its potential to improve reproductive health outcomes and deepen our understanding of reproductive molecular mechanisms.

• MeSH
• Reproductive Techniques, Assisted trends   MeSH
• Genomics MeSH
• Infertility genetics   therapy   diagnosis   MeSH
• Humans MeSH
• Reproduction genetics   physiology   MeSH
• Machine Learning MeSH
• Computational Biology * MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme-ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75% of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50% of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles.Scientific contributionsThe pipeline introduced in this work allows for the detailed analysis of a large set of protein-ligand complexes, focusing on transport pathways. We are introducing a novel predictor for determining the relevance of binding pockets for tunnel calculation. For the first time in the field, we present a high-throughput energetic analysis of ligand binding and unbinding, showing that approximate methods for these simulations can identify additional mutagenesis hotspots in enzymes compared to purely geometrical methods. The predictor is included in the supplementary material and can also be accessed at https://github.com/Faranehhad/Large-Scale-Pocket-Tunnel-Annotation.git . The tunnel data calculated in this study has been made publicly available as part of the ChannelsDB 2.0 database, accessible at https://channelsdb2.biodata.ceitec.cz/ .

• Publication type
• Journal Article MeSH

BACKGROUND: Plasma donor-derived cell-free DNA (dd-cfDNA) is used to screen for rejection in heart transplants. We launched the Trifecta-Heart study ( ClinicalTrials.gov No. NCT04707872), an investigator-initiated, prospective trial, to examine the correlations between genome-wide molecular changes in endomyocardial biopsies (EMBs) and plasma dd-cfDNA. The present report analyzes the correlation of plasma dd-cfDNA with gene expression in EMBs from 4 vanguard centers and compared these correlations with those in 604 kidney transplant biopsies in the Trifecta-Kidney study ( ClinicalTrials.gov No. NCT04239703). METHODS: We analyzed 137 consecutive dd-cfDNA-EMB pairs from 70 patients. Plasma %dd-cfDNA was measured by the Prospera test (Natera Inc), and gene expression in EMBs was assessed by Molecular Microscope Diagnostic System using machine-learning algorithms to interpret rejection and injury states. RESULTS: Top transcripts correlating with dd-cfDNA were related to genes increased in rejection such as interferon gamma-inducible genes (eg, HLA-DMA ) but also with genes induced by injury and expressed in macrophages (eg, SERPINA1 and HMOX1 ). In gene enrichment analysis, the top dd-cfDNA-correlated genes reflected inflammation and rejection pathways. Dd-cfDNA correlations with rejection genes in EMB were similar to those seen in kidney transplant biopsies, with somewhat stronger correlations for TCMR genes in hearts and ABMR genes in kidneys. However, the correlations with parenchymal injury-induced genes and macrophage genes were much stronger in hearts. CONCLUSIONS: In this first analysis of Trifecta-Heart study, dd-cfDNA correlates significantly with molecular rejection but also with injury and macrophage infiltration, reflecting the proinflammatory properties of injured cardiomyocytes. The relationship supports the utility of dd-cfDNA in clinical management of heart transplant recipients.

• MeSH
• Biomarkers blood   MeSH
• Biopsy MeSH
• Tissue Donors * MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Myocardium * pathology   metabolism   MeSH
• Predictive Value of Tests MeSH
• Prospective Studies MeSH
• Graft Rejection * genetics   immunology   pathology   blood   diagnosis   MeSH
• Aged MeSH
• Gene Expression Profiling MeSH
• Kidney Transplantation adverse effects   MeSH
• Heart Transplantation * adverse effects   MeSH
• Cell-Free Nucleic Acids * blood   genetics   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Comparative Study MeSH

Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of "Radiomics" involves the extraction of high-dimensional features, often referred to as "Radiomic features," from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.

• MeSH
• Adenoma * diagnostic imaging   MeSH
• Deep Learning MeSH
• Humans MeSH
• Pituitary Neoplasms * diagnostic imaging   MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Radiomics MeSH
• Machine Learning MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH
• Systematic Review MeSH

Východiská: Endometriálny karcinóm (EC) je najčastejšou rakovinou ženského reprodukčného traktu vo vyspelých krajinách. Prognóza a päťročná miera prežitia úzko súvisia so štádiom pri diagnostikovaní. Súčasné rutinné diagnostické metódy EC sú buď málo špecifické alebo pre pacientku nepríjemné, invazívne a bolestivé. Aktuálne je zlatým diagnostickým štandardom endometriálna biopsia. Včasná a neinvazívnu diagnostika EC vyžaduje identifikáciu nových markerov ochorenia a skríningový test aplikovateľný do rutinnej laboratórnej diagnostiky. Aplikácia necielenej metabolomiky v kombinácii s nástrojmi umelej inteligencie a bioštatistiky má potenciál kvalitatívne a kvantitatívne prezentovať metabolóm, ale jej zavedenie do rutinnej diagnostiky je z dôvodu finančnej, časovej aj interpretačnej náročnosti v súčasnosti nereálne. Fluorescenčná spektrálna analýza telových tekutín využíva autofluorescenciu určitých metabolitov na definovanie zloženia metabolómu za fyziologických podmienok. Cieľ: Tento prehľadový článok poukazuje na potenciál fluorescenčnej spektroskopie pri včasnej detekcii EC. Dáta získané trojrozmernou fluorescenčnou spektroskopiou definujú kvantitatívne aj kvalitatívne zloženie komplexného fluorescenčného metabolómu a sú vhodné na identifikáciu biochemických metabolických zmien spojených s karcinogenézou endometria. Autofluorescencia biologických tekutín má perspektívu poskytnúť nové molekulové markery EC. Integráciou algoritmov strojového učenia a umelej inteligencie pri dátovej analýze fluorescenčného metabolómu má táto technika veľký potenciál byť implementovaná do rutinnej laboratórnej diagnostiky.

Background: Endometrial carcinoma (EC) is the most common cancer of the female reproductive tract in developed countries. The prognosis and 5-year survival rates are closely tied to the stage diagnosis. Current routine diagnostic methods of EC are either lacking specificity or are uncomfortable, invasive and painful for the patient. As of now, the gold diagnostic standard is endometrial biopsy. Early and non-invasive diagnosis of EC requires the identification of new biomarkers of disease and a screening test applicable to routine laboratory diagnostics. The application of untargeted metabolomics combined with artificial intelligence and biostatistics tools has the potential to qualitatively and quantitatively represent the metabolome, but its introduction into routine diagnostics is currently unrealistic due to the financial, time and interpretation challenges. Fluorescence spectral analysis of body fluids utilizes autofluorescence of certain metabolites to define the composition of the metabolome under physiological conditions. Purpose: This review highlights the potential of fluorescence spectroscopy in the early detection of EC. Data obtained by three-dimensional fluorescence spectroscopy define the quantitative and qualitative composition of the complex fluorescent metabolome and are useful for identifying biochemical metabolic changes associated with endometrial carcinogenesis. Autofluorescence of biological fluids has the prospect of providing new molecular markers of EC. By integrating machine learning and artificial intelligence algorithms in the data analysis of the fluorescent metabolome, this technique has great potential to be implemented in routine laboratory diagnostics.

• MeSH
• Diagnostic Techniques and Procedures MeSH
• Spectrometry, Fluorescence methods   MeSH
• Humans MeSH
• Metabolomics methods   MeSH
• Endometrial Neoplasms * diagnostic imaging   metabolism   MeSH
• Optical Imaging * methods   MeSH
• Body Fluids diagnostic imaging   MeSH
• Tryptophan physiology   metabolism   MeSH
• Uterus diagnostic imaging   MeSH
• Check Tag
• Humans MeSH
• Female MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

Rýchly rozvoj umelej inteligencie patrí medzi najdôležitejšie technologické pokroky súčasného desaťročia a ovplyvňuje takmer všetky aspekty života, medicínu nevynímajúc. Široké uplatnenie umelá inteligencia zaznamenáva aj v neurorádiológii, osobitne v diagnostike CMP. K hlavným účelom jej použitia v tejto sfére patrí urýchlenie vyhodnocovacieho procesu, zvýšenie diagnostickej presnosti a pomoc pri voľbe liečebnej stratégie. Lekári zapojení do iniciálneho manažmentu pacienta s CMP by mali byť oboznámení s technickými princípmi a možnými aplikáciami nástrojov umelej inteligencie v neurozobrazovaní a poznať silné a slabé stránky tejto technológie. V článku sú v skratke predstavené metódy umelej inteligencie využívané pri spracovaní obrazových dát. Hlavným cieľom publikácie je prezentácia jednotlivých automatických analýz nápomocných v interpretácii diagnostických informácií získaných vyšetrením CT, ktoré je pre väčšinu pracovísk modalitou prvej voľby v diagnostike CMP. Patria tu kalkulácia skóre ASPECT a detekcia príznaku hyperdenznej cievy z natívneho vyšetrenia CT, identifikácia uzáveru veľkej cievy a určenie skóre kolaterál z CTA a vytvorenie perfúznych máp z perfúzneho vyšetrenia CT.

Artificial intelligence and its rapid development represent one of the most important technological advances of the current decade. It affects almost all aspects of life, including medicine. Artificial intelligence is widely applied in neuroradiology, particularly in stroke diagnosis. The primary purpose of its application in this area is to accelerate the interpretation process, increase diagnostic accuracy, and help to select the treatment strategy. Clinicians involved in the initial management of a stroke patient should be familiar with the technical principles and possible use of artificial intelligence in neuroimaging, and they should know the strengths and weaknesses of the technology. This article briefly presents methods of artificial intelligence used in visual data processing. The main goal of the publication is to present particular automated analyses used in the interpretation of diagnostic information taken from CT images. CT is the primary choice in stroke diagnostics for most medical departments. The presented analyses are a calculation of the ASPECT score and detection of a hyperdense artery sign from non-contrast CT scans, identification of large vessel occlusion and collateral score evaluation from CTA, and creation of perfusion maps from CT perfusion.

• MeSH
• Stroke * diagnostic imaging   MeSH
• Deep Learning MeSH
• Humans MeSH
• Neuroimaging * methods   MeSH
• Image Processing, Computer-Assisted MeSH
• Machine Learning MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Review MeSH