Tento článek zpracovává téma nových trendů a technologií v urologii, a to konkrétně v oblasti telemedicíny a umělé inteligence. Nejprve stručně pojednává o přínosech telemedicíny a jak mění pohled na vztah mezi lékařem a pacientem. Podrobněji se pak text věnuje především umělé inteligenci, jež se v současnosti dostává do popředí zájmu laické i odborné veřejnosti. Její potenciál v urologii je testován v mnoha studiích, především se zaměřením na uroonkologii, v menší míře pak také v oblasti benigních urologických onemocnění. Článek se snaží identifikovat nejvýznamnější pokroky v této rychle se rozvíjející oblasti, a zároveň předkládá současné limity jejího zapojení do klinické praxe.
This article explores the emerging trends and technologies in urology, focusing on telemedicine and artificial intelligence. It provides a brief overview of the benefits of telemedicine and its impact on the patient-physician interactions. The article subsequently explores in detail the use of artificial intelligence, which is currently gaining considerable interest from both general public and medical professionals. Its potential in urology has been tested in a number of clinical studies, particularly in the field of uro-oncology and, to a lesser extent, in benign urological diseases. The aim of this article is to identify the key advances in this rapidly evolving field, while also highlighting the current limitations of its implementation into clinical practice.
- MeSH
- Deep Learning MeSH
- Humans MeSH
- Robotic Surgical Procedures MeSH
- Machine Learning MeSH
- Telemedicine MeSH
- Artificial Intelligence MeSH
- Urologic Neoplasms diagnosis therapy MeSH
- Urology * trends MeSH
- Check Tag
- Humans MeSH
- Publication type
- Review MeSH
- Keywords
- studie ACCESS,
- MeSH
- Detection Algorithms MeSH
- Diabetes Mellitus MeSH
- Diabetic Retinopathy * diagnosis prevention & control MeSH
- Health Services Accessibility MeSH
- Clinical Trials as Topic MeSH
- Humans MeSH
- Diagnostic Screening Programs * MeSH
- Artificial Intelligence * MeSH
- Insurance, Health MeSH
- Check Tag
- Humans MeSH
- Geographicals
- Czech Republic MeSH
BACKGROUND: Use of artificial intelligence (AI) in rare diseases has grown rapidly in recent years. In this review we have outlined the most common machine-learning and deep-learning methods currently being used to classify and analyse large amounts of data, such as standardized images or specific text in electronic health records. To illustrate how these methods have been adapted or developed for use with rare diseases, we have focused on Fabry disease, an X-linked genetic disorder caused by lysosomal α-galactosidase. A deficiency that can result in multiple organ damage. METHODS: We searched PubMed for articles focusing on AI, rare diseases, and Fabry disease published anytime up to 08 January 2025. Further searches, limited to articles published between 01 January 2021 and 31 December 2023, were also performed using double combinations of keywords related to AI and each organ affected in Fabry disease, and AI and rare diseases. RESULTS: In total, 20 articles on AI and Fabry disease were included. In the rare disease field, AI methods may be applied prospectively to large populations to identify specific patients, or retrospectively to large data sets to diagnose a previously overlooked rare disease. Different AI methods may facilitate Fabry disease diagnosis, help monitor progression in affected organs, and potentially contribute to personalized therapy development. The implementation of AI methods in general healthcare and medical imaging centres may help raise awareness of rare diseases and prompt general practitioners to consider these conditions earlier in the diagnostic pathway, while chatbots and telemedicine may accelerate patient referral to rare disease experts. The use of AI technologies in healthcare may generate specific ethical risks, prompting new AI regulatory frameworks aimed at addressing these issues to be established in Europe and the United States. CONCLUSION: AI-based methods will lead to substantial improvements in the diagnosis and management of rare diseases. The need for a human guarantee of AI is a key issue in pursuing innovation while ensuring that human involvement remains at the centre of patient care during this technological revolution.
- MeSH
- Fabry Disease * diagnosis MeSH
- Humans MeSH
- Artificial Intelligence * MeSH
- Rare Diseases * diagnosis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood1-3. We use spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis4,5, we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudotime across fibrosis stages. We achieve proteome depth of up to 4,300 proteins from one-third of a single cell in formalin-fixed, paraffin-embedded tissue. This dataset reveals a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show α1-antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with artificial intelligence-guided image-based phenotyping across several disease stages, revealing a late-stage hepatocyte phenotype characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10 (also known as TRAIL) amounts. This phenotype may represent a critical disease progression stage. Our study offers new insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue.
- MeSH
- alpha 1-Antitrypsin metabolism MeSH
- Single-Cell Analysis MeSH
- alpha 1-Antitrypsin Deficiency * pathology metabolism genetics MeSH
- Phenotype MeSH
- Hepatocytes metabolism pathology MeSH
- Liver Cirrhosis pathology metabolism MeSH
- Liver pathology metabolism MeSH
- Humans MeSH
- Disease Progression MeSH
- Proteome * analysis metabolism MeSH
- Proteomics * methods MeSH
- Unfolded Protein Response MeSH
- Machine Learning MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Targeting ubiquitin E3 ligases is therapeutically attractive; however, the absence of an active-site pocket impedes computational approaches for identifying inhibitors. In a large, unbiased biochemical screen, we discover inhibitors that bind a cryptic cavity distant from the catalytic cysteine of the homologous to E6-associated protein C terminus domain (HECT) E3 ligase, SMAD ubiquitin regulatory factor 1 (SMURF1). Structural and biochemical analyses and engineered escape mutants revealed that these inhibitors restrict an essential catalytic motion by extending an α helix over a conserved glycine hinge. SMURF1 levels are increased in pulmonary arterial hypertension (PAH), a disease caused by mutation of bone morphogenetic protein receptor-2 (BMPR2). We demonstrated that SMURF1 inhibition prevented BMPR2 ubiquitylation, normalized bone morphogenetic protein (BMP) signaling, restored pulmonary vascular cell homeostasis, and reversed pathology in established experimental PAH. Leveraging this deep mechanistic understanding, we undertook an in silico machine-learning-based screen to identify inhibitors of the prototypic HECT E6AP and confirmed glycine-hinge-dependent allosteric activity in vitro. Inhibiting HECTs and other glycine-hinge proteins opens a new druggable space.
- MeSH
- Allosteric Regulation drug effects MeSH
- Humans MeSH
- Mice MeSH
- Pulmonary Arterial Hypertension drug therapy MeSH
- Bone Morphogenetic Protein Receptors, Type II MeSH
- Signal Transduction drug effects MeSH
- Ubiquitination drug effects MeSH
- Ubiquitin-Protein Ligases * antagonists & inhibitors metabolism chemistry genetics MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Mice MeSH
- Animals 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 AND OBJECTIVES: Frontotemporal lobar degeneration (FTLD) as the second most common dementia encompasses a range of syndromes and often shows overlapping symptoms with other subtypes or neurodegenerative diseases, which poses a significant clinical diagnostic challenge. Recent advancements in artificial intelligence (AI), specifically the application of machine learning (ML) algorithms to neuroimaging, have significantly progressed in addressing this challenge. This study aims to assess the diagnostic and predictive efficacy of neuroimaging feature-based AI algorithms for FTLD. METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines. We searched Pubmed, Scopus, and Web of Science for English-language, peer-reviewed studies using the following three umbrella terms: artificial intelligence, frontotemporal lobar degeneration, and neuroimaging modality. Our survey focused on computer-aided diagnosis for FTLD, employing machine/deep learning with neuroimaging radiomic features. RESULTS: The meta-analysis includes 75 articles with 20,601 subjects, including 8,051 FTLD patients. The results reveal that FTLD can be automatically classified against healthy controls (HC) with pooled sensitivity and specificity of 86% and 89%, respectively. Likewise, FTLD versus Alzheimer's disease (AD) classification exhibits pooled sensitivity and specificity of 84% and 81%, while FTLD versus Parkinson's disease (PD) demonstrates pooled sensitivity and specificity of 84% and 75%, respectively. Classification performance distinguishing FTLD from atypical Parkinsonian syndromes (APS) showed pooled sensitivity and specificity of 84% and 79%, respectively. Multiclass classification sensitivity ranges from 42% to 100%, with lower sensitivity occurring in higher class distinctions (e.g., 5-class and 11-class). DISCUSSION: Our study demonstrates the effectiveness of utilizing neuroimaging features to distinguish FTLD from HC, AD, APS, and PD in binary classification. Utilizing deep learning with multimodal neuroimaging data to differentiate FTLD subtypes and perform multiclassification among FTLD and other neurodegenerative disease holds promise for expediting diagnosis. In sum, the meta-analysis supports translation of machine learning tools in combination with imaging to clinical routine paving the way to precision medicine.
Ovarian lesions are common and often incidentally detected. A critical shortage of expert ultrasound examiners has raised concerns of unnecessary interventions and delayed cancer diagnoses. Deep learning has shown promising results in the detection of ovarian cancer in ultrasound images; however, external validation is lacking. In this international multicenter retrospective study, we developed and validated transformer-based neural network models using a comprehensive dataset of 17,119 ultrasound images from 3,652 patients across 20 centers in eight countries. Using a leave-one-center-out cross-validation scheme, for each center in turn, we trained a model using data from the remaining centers. The models demonstrated robust performance across centers, ultrasound systems, histological diagnoses and patient age groups, significantly outperforming both expert and non-expert examiners on all evaluated metrics, namely F1 score, sensitivity, specificity, accuracy, Cohen's kappa, Matthew's correlation coefficient, diagnostic odds ratio and Youden's J statistic. Furthermore, in a retrospective triage simulation, artificial intelligence (AI)-driven diagnostic support reduced referrals to experts by 63% while significantly surpassing the diagnostic performance of the current practice. These results show that transformer-based models exhibit strong generalization and above human expert-level diagnostic accuracy, with the potential to alleviate the shortage of expert ultrasound examiners and improve patient outcomes.
- MeSH
- Deep Learning MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Ovarian Neoplasms * diagnostic imaging MeSH
- Neural Networks, Computer * MeSH
- Retrospective Studies MeSH
- Aged MeSH
- Sensitivity and Specificity MeSH
- Ultrasonography * methods MeSH
- Artificial Intelligence MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
- Validation Study MeSH
Intracranial calcifications, particularly within the falx cerebri, serve as crucial diagnostic markers ranging from benign accumulations to signs of severe pathologies. The falx cerebri, a dural fold that separates the cerebral hemispheres, presents challenges in visualization due to its low contrast in standard imaging techniques. Recent advancements in artificial intelligence (AI), particularly in machine learning and deep learning, have significantly transformed radiological diagnostics. This study aims to explore the application of AI in the segmentation and detection of falx cerebri calcifications using Cone-Beam Computed Tomography (CBCT) images through a comprehensive literature review and a detailed case report. The case report presents a 59-year-old patient diagnosed with falx cerebri calcifications whose CBCT images were analyzed using a cloud-based AI platform, demonstrating effectiveness in segmenting these calcifications, although challenges persist in distinguishing these from other cranial structures. A specific search strategy was employed to search electronic databases, yielding four studies exploring AI-based segmentation of the falx cerebri. The review detailed various AI models and their accuracy across different imaging modalities in identifying and segmenting falx cerebri calcifications, also highlighting the gap in publications in this area. In conclusion, further research is needed to improve AI-driven methods for accurately identifying and measuring intracranial calcifications. Advancing AI applications in radiology, particularly for detecting falx cerebri calcifications, could significantly enhance diagnostic precision, support disease monitoring, and inform treatment planning.
Idiopatická pLicní fibróza (IPF) je definována jako chronická progresivní fibrotizující intersticiáiní pneumonie neznámé etioLogie, která postihuje starší dospělé a týká se výlučně plic. Doporučené postupy zdůrazňují potřebu mezioborové spolupráce vtělené do diskuse muLtidiscipLinárního týmu nad případy nemocných s IPF a připouští možnost nebioptovat nejen nemocné s typickými rysy IPF, aLe ani nemocné s radioLogickým obrazem (v doporučeném postupu jasně definované) možné obvykLé intersticiáLní pneumonie. V hodnocení radioLogického náLezu se začínají upLatňovat metody strojového učení a hLubokého učení, tedy jedny z nástrojů uměLé inteLigence. Pozitivními výsLedky (dosažením primárního cíle) skončiLa v Letošním roce studie FIBRONEER-IPF testující nerandomiLast a po deseti Letech od posLedního úspěchu, který vedL k registraci nintedanibu pro Léčbu IPF, dává naději nemocným s IPF na novou Léčebnou možnost.
Idiopathic pulmonary fibrosis (IPF) is defined as chronic, progressive fibrosing interstitial pneumonia of unknown etiology that affects older adults and involves exclusively the lungs. The recommended procedures emphasize the need for interdisciplinary cooperation embodied in the discussion of the multidisciplinary team on the cases of patients with IPF and allows the possibility of no biopsy in patients with typical IPF and also in patients with a radiological picture (clearly defined by the guidelines) of possible usual interstitial pneumonia. Machine learning and deep learning methods, i.e. one of the tools of artificial intelligence, are beginning to be applied in the evaluation of radiological findings. The FIBRONEER-IPF study testing nerandomilast ended this year with positive results (achieving the primary goal) and ten years after the last success that led to the registration of nintedanib for the treatment of IPF, it gives hope to patients with IPF for a new treatment option.
