INTRODUCTION: The histopathological classification for antineutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis (ANCA-GN) is a well-established tool to reflect the variety of patterns and severity of lesions that can occur in kidney biopsies. It was demonstrated previously that deep learning (DL) approaches can aid in identifying histopathological classes of kidney diseases; for example, of diabetic kidney disease. These models can potentially be used as decision support tools for kidney pathologists. Although they reach high prediction accuracies, their "black box" structure makes them nontransparent. Explainable (X) artificial intelligence (AI) techniques can be used to make the AI model decisions accessible for human experts. We have developed a DL-based model, which detects and classifies the glomerular lesions according to the Berden classification. METHODS: Kidney biopsy slides of 80 patients with ANCA-GN from 3 European centers, who underwent a diagnostic kidney biopsy between 1991 and 2011, were included. We also investigated the explainability of our model using Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps. These maps were analyzed by pathologists to compare the decision-making criteria of humans and the DL model and assess the impact of different training settings. RESULTS: The DL model shows a prediction accuracy of 93% for classifying lesions. The heatmaps from our trained DL models showed that the most predictive areas in the image correlated well with the areas deemed to be important by the pathologist. CONCLUSION: We present the first DL-based computational pipeline for classifying ANCA-GN kidney biopsies as per the Berden classification. XAI techniques helped us to make the decision-making criteria of the DL accessible for renal pathologists, potentially improving clinical decision-making.

• Publication type
• Journal Article MeSH

PURPOSE OF REVIEW: A critical evaluation of contemporary literature regarding the role of big data, artificial intelligence, and digital technologies in precision cardio-oncology care and survivorship, emphasizing innovative and groundbreaking endeavors. RECENT FINDINGS: Artificial intelligence (AI) algorithm models can automate the risk assessment process and augment current subjective clinical decision tools. AI, particularly machine learning (ML), can identify medically significant patterns in large data sets. Machine learning in cardio-oncology care has great potential in screening, diagnosis, monitoring, and managing cancer therapy-related cardiovascular complications. To this end, large-scale imaging data and clinical information are being leveraged in training efficient AI algorithms that may lead to effective clinical tools for caring for this vulnerable population. Telemedicine may benefit cardio-oncology patients by enhancing healthcare delivery through lowering costs, improving quality, and personalizing care. Similarly, the utilization of wearable biosensors and mobile health technology for remote monitoring holds the potential to improve cardio-oncology outcomes through early intervention and deeper clinical insight. Investigations are ongoing regarding the application of digital health tools such as telemedicine and remote monitoring devices in enhancing the functional status and recovery of cancer patients, particularly those with limited access to centralized services, by increasing physical activity levels and providing access to rehabilitation services. SUMMARY: In recent years, advances in cancer survival have increased the prevalence of patients experiencing cancer therapy-related cardiovascular complications. Traditional cardio-oncology risk categorization largely relies on basic clinical features and physician assessment, necessitating advancements in machine learning to create objective prediction models using diverse data sources. Healthcare disparities may be perpetuated through AI algorithms in digital health technologies. In turn, this may have a detrimental effect on minority populations by limiting resource allocation. Several AI-powered innovative health tools could be leveraged to bridge the digital divide and improve access to equitable care.

• Publication type
• Journal Article MeSH

BACKGROUND: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. METHODS: We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. RESULTS: From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. CONCLUSIONS: Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.

• MeSH
• Laboratories, Clinical MeSH
• Humans MeSH
• Surveys and Questionnaires MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Europe MeSH

G protein-coupled receptors (GPCRs) play a crucial role in cell function by transducing signals from the extracellular environment to the inside of the cell. They mediate the effects of various stimuli, including hormones, neurotransmitters, ions, photons, food tastants and odorants, and are renowned drug targets. Advancements in structural biology techniques, including X-ray crystallography and cryo-electron microscopy (cryo-EM), have driven the elucidation of an increasing number of GPCR structures. These structures reveal novel features that shed light on receptor activation, dimerization and oligomerization, dichotomy between orthosteric and allosteric modulation, and the intricate interactions underlying signal transduction, providing insights into diverse ligand-binding modes and signalling pathways. However, a substantial portion of the GPCR repertoire and their activation states remain structurally unexplored. Future efforts should prioritize capturing the full structural diversity of GPCRs across multiple dimensions. To do so, the integration of structural biology with biophysical and computational techniques will be essential. We describe in this review the progress of nuclear magnetic resonance (NMR) to examine GPCR plasticity and conformational dynamics, of atomic force microscopy (AFM) to explore the spatial-temporal dynamics and kinetic aspects of GPCRs, and the recent breakthroughs in artificial intelligence for protein structure prediction to characterize the structures of the entire GPCRome. In summary, the journey through GPCR structural biology provided in this review illustrates how far we have come in decoding these essential proteins architecture and function. Looking ahead, integrating cutting-edge biophysics and computational tools offers a path to navigating the GPCR structural landscape, ultimately advancing GPCR-based applications. LINKED ARTICLES: This article is part of a themed issue Complexity of GPCR Modulation and Signaling (ERNST). To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.14/issuetoc.

• MeSH
• Protein Conformation MeSH
• Humans MeSH
• Receptors, G-Protein-Coupled * chemistry   metabolism   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imaging methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morphological evaluation, including size, shape, necrosis, and unenhanced attenuation. More advanced post-processing tools to analyse digital images, such as texture analysis and radiomics, are currently being studied. Radiomic features utilise digital image pixels to calculate parameters and relations undetectable by the human eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis but also the prediction of complications and therapy outcomes of phaeochromocytomas in the future. Currently, the potential of radiomics and machine learning does not match expectations and awaits its fulfilment.

• MeSH
• Pheochromocytoma * diagnostic imaging   MeSH
• Humans MeSH
• Adrenal Gland Neoplasms * diagnostic imaging   MeSH
• Paraganglioma * diagnostic imaging   MeSH
• Tomography, X-Ray Computed methods   MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Radiomics MeSH
• Machine Learning MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

• Keywords
• naivní model, transfuzní přípravek,
• MeSH
• Blood Donors MeSH
• Blood Banking organization & administration   MeSH
• Machine Learning supply & distribution   MeSH
• Planning Techniques MeSH
• Artificial Intelligence supply & distribution   MeSH
• Equipment and Supplies, Hospital MeSH

• Conspectus
• Patologie. Klinická medicína
• NML Publication type
• závěrečné práce

• MeSH
• Generative Artificial Intelligence * MeSH
• Communication MeSH
• General Practice MeSH
• Models, Theoretical MeSH
• Medical Informatics Applications MeSH
• Computer Security MeSH

• MeSH
• Hypertension MeSH
• Humans MeSH
• Smoking Cessation * MeSH
• Disability Evaluation MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH
• Publication type
• Examination Questions MeSH

Umělá inteligence (AI) se stále častěji uplatňuje v radiologii, kde nabízí potenciál zlepšit přesnost a efektivitu diagnostiky, zejména při hodnocení běžných zobrazovacích metod, jako jsou rtg snímky hrudníku. Tato studie analyzuje přesnost komerčního softwaru využívajícího strojové učení, respektive metody umělé inteligence, při detekci abnormalit na rtg snímcích hrudníku ve srovnání s nezávislými hodnoceními 3 juniorních radiologů. Výzkum byl proveden ve spolupráci s Nemocnicí Tábor, která poskytla dataset 207 anonymizovaných rtg snímků, z nichž 196 bylo vyhodnoceno jako relevantní. Senzitivita a specificita AI byla porovnána s lidským hodnocením v 5 kategoriích abnormalit: atelektáza (ATE), konsolidace (CON), zvětšení srdečního stínu (CMG), pleurální výpotek (EFF) a plicní léze (LES). Software Carebot AI CXR dosáhl vysoké senzitivity ve všech hodnocených kategoriích (např. ATE: 0,909; CMG: 0,889; EFF: 0,951), přičemž jeho přesnost byla konzistentní napříč všemi nálezy. Naopak specificita AI byla v některých kategoriích nižší (např. EFF: 0,792; CON 0,895), zatímco u radiologů dosahovala ve většině případů hodnot blížících se 1,000 (např. RAD 1 a RAD 2 EFF: 1,000). AI vykazovala konzistentně vyšší senzitivitu než méně zkušení radiologové (např. RAD 1 ATE: 0,087; CMG: 0,327) a v některých případech i než zkušenější hodnotitelé, avšak za cenu mírného snížení specificity. Studie zahrnuje také kazuistiky, včetně falešně pozitivních a falešně negativních nálezů, které přispívají k hlubšímu pochopení přesnosti AI v klinické praxi. Výsledky naznačují, že AI může efektivně doplňovat práci radiologů, zejména u méně zkušených lékařů, a zlepšit senzitivitu diagnostiky na rtg snímcích hrudníku.

Artificial intelligence (AI) has been increasingly applied in radiology, where it offers the potential to improve the accuracy and efficiency of diagnosis, particularly in the evaluation of conventional imaging modalities such as chest X-rays. This study analyzes the performance of commercial software using machine learning and, respectively, artificial intelligence approaches (Carebot AI CXR; Carebot s.r.o.) in detecting abnormalities in chest radiographs compared with independent evaluations by 3 radiologists of different levels of experience. The study was conducted in collaboration with Hospital Tabor, which provided a dataset of 207 anonymised radiographs, out of which 196 were assessed as relevant. The sensitivity and specificity of AI were compared with human assessment in 5 categories of abnormalities: atelectasis (ATE), consolidation (CON), cardiac shadow enlargement (CMG), pleural effusion (EFF) and pulmonary lesions (LES). Carebot AI CXR software achieved high sensitivity in all evaluated categories (e.g., ATE: 0.909, CMG: 0.889, EFF: 0.951), and its performance was consistent across all findings. In contrast, AI specificity was lower in some categories (e.g., EFF: 0.792, CON: 0.895), while radiologists achieved performance values approaching 1.000 in most cases (e.g., RAD 1 and RAD 2 EFF: 1.000). AI demonstrated consistently higher sensitivity than less experienced radiologists (e.g., RAD 1 ATE: 0.087, CMG: 0.327) and in some cases than more experienced assessors, but at a modest decrease in specificity. The study also includes case reports, including false-positive and false-negative findings, which contribute to a deeper understanding of AI performance in clinical practice. The results suggest that AI can effectively complement the work of radiologists, especially for less experienced doctors, and improve the sensitivity of diagnosis on chest radiographs.

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