• Klíčová slova
• studie ACCESS,
• MeSH
• detekční algoritmy MeSH
• diabetes mellitus MeSH
• diabetická retinopatie * diagnóza   prevence a kontrola   MeSH
• dostupnost zdravotnických služeb MeSH
• klinické zkoušky jako téma MeSH
• lidé MeSH
• screeningové diagnostické programy * MeSH
• umělá inteligence * MeSH
• zdravotní pojištění MeSH
• Check Tag
• lidé MeSH
• Geografické názvy
• Česká republika MeSH

BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3D18F-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site. METHOD: A publicly available lesion-annotated dataset of 1014 whole-body FDG-PET/CT images was used to train, validate, and test (70:10:20) eight configurations with 3D U-Net as the backbone architecture. The best-performing model on the test set was further evaluated on 3 different unseen cohorts consisting of osteosarcoma or neuroblastoma (OS cohort) (n = 13), pediatric solid tumors (ST cohort) (n = 14), and adult Pheochromocytoma/Paraganglioma (PHEO cohort) (n = 40). Both lesion-level and patient-level statistical analyses were conducted to validate the performance of the model on different cohorts. RESULTS: The best performing 3D full resolution nnUNet model achieved a lesion-level sensitivity and DISC of 71.70 % and 0.40 for the test set, 97.83 % and 0.73 for ST, 40.15 % and 0.36 for OS, and 78.37 % and 0.50 for the PHEO cohort. For the test set and PHEO cohort, the model has missed small volume and lower uptake lesions (p < 0.01), whereas no statistically significant differences (p > 0.05) were found in the false positive (FP) and false negative lesions volume and uptake for the OS and ST cohort. The predicted total lesion glycolysis is slightly higher than the ground truth because of FP calls, which experts can easily check and reject. CONCLUSION: The developed deep learning-based automated lesion segmentation AI model which utilizes 3D_FullRes configuration of the nnUNet framework showed promising and reliable performance for the whole-body FDG-PET/CT images.

• MeSH
• celotělové zobrazování * metody   MeSH
• deep learning * MeSH
• dítě MeSH
• dospělí MeSH
• fluorodeoxyglukosa F18 * MeSH
• kohortové studie MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• nádory * diagnostické zobrazování   MeSH
• PET/CT * metody   MeSH
• počítačové zpracování obrazu * metody   MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• validační studie MeSH

PURPOSE: This study aimed to compare general ophthalmologists, retina specialists, and Aireen AI screening system with the clinical reference standard of a three-member high-level expert committee for diabetic retinopathy (DR) in the evaluation of fundus images for DR. PATIENTS AND METHODS: The study was designed as a diagnostic, multicenter, cross-sectional, non-randomized diagnostic study. The cohort included in the clinical investigation consisted of 1274 patients with diabetes mellitus (DM) type I or II. Each patient underwent one-field fundus photography using a non-mydriatic camera to assess findings of DR. One hundred and nineteen subjects (9.3%) were excluded from the clinical investigation based on Aireen system assessment. In the clinical investigation, all images were assessed at three independent levels of evaluation: 1) general ophthalmologists (GO) - without subspecialty training in the retina; 2) retina specialists (RS); and 3) system Aireen. In cases where there may be disagreements amongst groups, the image is referred for assessment by the Diabetic Retinopathy Board (DRB). RESULTS: The overall prevalence of any DR was 31.9% (368 cases out of 1154 DM), according to the DRB. Overall concordance between AI system Aireen and GO and RS assessments in the detection of DR from fundus photography occurred in 734 cases (63.6%). The number of disagreements between Aireen system, GO and RS evaluation occurred in 420 (36.4%) cases. Sensitivity for GO was 87.0% (95% CI: 83.6; 90.4), for RS was 82.9% (95% CI: 79.1; 86.7), and for AI system Aireen was 92.1% (95% CI: 89.3; 94.9). Specificity was 76.5% (95% CI: 73.5; 79.5), 81.2% (95% CI: 78.5; 83.9), and 90.7% (95% CI: 88.7; 92.7) for GO, RS and AI system Aireen, respectively. CONCLUSION: This real-world study illustrates the potential use of AI system Aireen in screening for DR. It exhibits higher sensitivity and specificity compared to telemedicine evaluation of one field fundus image.

• Publikační typ
• časopisecké články MeSH

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics have generated genetic variants at an unprecedented scale. However, efficient tools and resources are needed to link disparate data types-to 'map' variants onto protein structures, to better understand how the variation causes disease, and thereby design therapeutics. Here we present the Genomics 2 Proteins portal ( https://g2p.broadinstitute.org/ ): a human proteome-wide resource that maps 20,076,998 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the Genomics 2 Proteins portal allows users to interactively upload protein residue-wise annotations (for example, variants and scores) as well as the protein structure beyond databases to establish the connection between genomics to proteins. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotypes.

• MeSH
• databáze proteinů * MeSH
• genetická variace MeSH
• genetické testování metody   MeSH
• genomika * metody   MeSH
• konformace proteinů MeSH
• lidé MeSH
• proteiny genetika   chemie   MeSH
• proteom genetika   MeSH
• sekvence aminokyselin MeSH
• software MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Worldwide stroke is the second leading cause of death and the third leading cause of death and disability combined. The estimated global economic burden by stroke is over US$891 billion per year. Within three decades (1990-2019), the incidence increased by 70%, deaths by 43%, prevalence by 102%, and DALYs by 143%. Of over 100 million people affected by stroke, about 76% are ischemic stroke (IS) patients recorded worldwide. Contextually, ischemic stroke moves into particular focus of multi-professional groups including researchers, healthcare industry, economists, and policy-makers. Risk factors of ischemic stroke demonstrate sufficient space for cost-effective prevention interventions in primary (suboptimal health) and secondary (clinically manifested collateral disorders contributing to stroke risks) care. These risks are interrelated. For example, sedentary lifestyle and toxic environment both cause mitochondrial stress, systemic low-grade inflammation and accelerated ageing; inflammageing is a low-grade inflammation associated with accelerated ageing and poor stroke outcomes. Stress overload, decreased mitochondrial bioenergetics and hypomagnesaemia are associated with systemic vasospasm and ischemic lesions in heart and brain of all age groups including teenagers. Imbalanced dietary patterns poor in folate but rich in red and processed meat, refined grains, and sugary beverages are associated with hyperhomocysteinaemia, systemic inflammation, small vessel disease, and increased IS risks. Ongoing 3PM research towards vulnerable groups in the population promoted by the European Association for Predictive, Preventive and Personalised Medicine (EPMA) demonstrates promising results for the holistic patient-friendly non-invasive approach utilising tear fluid-based health risk assessment, mitochondria as a vital biosensor and AI-based multi-professional data interpretation as reported here by the EPMA expert group. Collected data demonstrate that IS-relevant risks and corresponding molecular pathways are interrelated. For examples, there is an evident overlap between molecular patterns involved in IS and diabetic retinopathy as an early indicator of IS risk in diabetic patients. Just to exemplify some of them such as the 5-aminolevulinic acid/pathway, which are also characteristic for an altered mitophagy patterns, insomnia, stress regulation and modulation of microbiota-gut-brain crosstalk. Further, ceramides are considered mediators of oxidative stress and inflammation in cardiometabolic disease, negatively affecting mitochondrial respiratory chain function and fission/fusion activity, altered sleep-wake behaviour, vascular stiffness and remodelling. Xanthine/pathway regulation is involved in mitochondrial homeostasis and stress-driven anxiety-like behaviour as well as molecular mechanisms of arterial stiffness. In order to assess individual health risks, an application of machine learning (AI tool) is essential for an accurate data interpretation performed by the multiparametric analysis. Aspects presented in the paper include the needs of young populations and elderly, personalised risk assessment in primary and secondary care, cost-efficacy, application of innovative technologies and screening programmes, advanced education measures for professionals and general population-all are essential pillars for the paradigm change from reactive medical services to 3PM in the overall IS management promoted by the EPMA.

• Publikační typ
• časopisecké články MeSH

Východiska: Rakovina plic je jednou z nejčastějších příčin úmrtí na celosvětové úrovni, přičemž její incidence i mortalita jsou výrazně ovlivněny stárnutím populace a změnami v prevalenci rizikových faktorů. Plicní noduly, často detekované náhodně při zobrazovacích vyšetřeních, představují významnou diagnostickou výzvu, jelikož mohou signalizovat benigní i maligní procesy. Správná diagnostika a management těchto nodulů jsou proto zásadní pro optimalizaci klinických výsledků. Cíl: Tento článek poskytuje komplexní přehled diagnostických a terapeutických postupů u plicních nodulů se zaměřením na hodnocení maligního potenciálu na základě morfologie, velikosti a růstového potenciálu nodulů. Diskutovány jsou také rizikové faktory, které ovlivňují rozhodovací proces, jako je kouření, věk a expozice karcinogenům. Dále jsou detailně rozebrána klíčová doporučení Fleischnerovy společnosti a British Thoracic Society. Článek analyzuje přínosy moderních zobrazovacích metod, vč. využití umělé inteligence (AI) v diagnostice plicních nodulů. AI technologie, zejména techniky hlubokého učení, vykazují vysokou přesnost v detekci a hodnocení maligního rizika, přičemž jejich využití je stále doplňkem odborného klinického posouzení. Závěrem článek zdůrazňuje význam multidisciplinárního přístupu v diagnostice a léčbě plicních nodulů a také zmiňuje implementaci pilotního screeningového programu rakoviny plic v ČR, který je zaměřen na časný záchyt onemocnění. Tento program má potenciál významně snížit mortalitu spojenou s rakovinou plic a zlepšit prognózu pacientů.

Background: Lung cancer is one of the leading causes of death worldwide, with incidence and mortality significantly affected by population ageing and changes in the prevalence of risk factors. Lung nodules, which are often detected incidentally on imaging studies, pose a significant diagnostic challenge as they may indicate both benign and malignant processes. Correct diagnosis and management of these nodules is therefore essential to optimize clinical outcomes. Purpose: This article provides a comprehensive review of diagnostic and therapeutic approaches to pulmonary nodules, focusing on the assessment of malignant potential based on nodule morphology, size and growth potential. Risk factors influencing the decision-making process such as smoking, age and exposure to carcinogens are also discussed. In addition, key recommendations from the Fleischner Society and the British Thoracic Society are discussed in detail. The article analyses the benefits of modern imaging techniques, including the use of artificial intelligence (AI) in the diagnosis of lung nodules. AI technologies, particularly deep learning techniques, have shown high accuracy in detecting and assessing malignancy risk, and their use is increasingly complementary to expert clinical judgement. Finally, the article highlights the importance of a multidisciplinary approach to the diagnosis and management of lung nodules, and also mentions the implementation of a pilot lung cancer screening programme in the Czech Republic aimed at early detection of the disease. This programme has the potential to significantly reduce lung cancer mortality and improve patient prognosis.

• MeSH
• diagnostické zobrazování metody   MeSH
• diferenciální diagnóza MeSH
• karcinogeny MeSH
• kouření škodlivé účinky   MeSH
• lidé MeSH
• mnohočetné plicní uzly * diagnóza   etiologie   terapie   MeSH
• nádory plic diagnóza   terapie   MeSH
• plošný screening metody   MeSH
• rizikové faktory MeSH
• terciární prevence metody   MeSH
• umělá inteligence trendy   MeSH
• věkové faktory MeSH
• Check Tag
• lidé MeSH

Zaměřujeme se na možné využití AI v rámci diagnostiky ložiskových změn plicního parenchymu, které mohou být projevem zhoubného nádoru plic, na základě skiagramu hrudníku. Ačkoliv ve srovnání s jinými metodami, především výpočetní tomografií (CT) hrudníku, tato modalita vykazuje nižší senzitivitu, vzhledem k rutinnímu provádění velmi často představuje první vyšetření, při němž jsou plicní léze zachyceny. Prezentujeme vlastní řešení založené na metodách hlubokého učení, které má za cíl zvýšit záchyt plicních lézí především v časných fázích onemocnění. Následně uvádíme výsledky našich předchozích původních prací, které validují navržený model ve dvou odlišných klinických prostředích – v prostředí spádové nemocnice s nízkou prevalencí nálezů a v prostředí specializovaného onkologického centra. Na základě kvantitativního srovnání se závěry radiologů různých úrovní zkušeností jsme zjistili, že náš model dosahuje vysoké senzitivity, na druhou stranu byla jeho specificita nižší než u oslovených radiologů. V kontextu klinických požadavků a diagnostiky asistované AI hraje zásadní roli zkušenost a klinické uvažování lékaře, proto se v současnosti přikláníme k modelům s vyšší senzitivitou na úkor nižší specificity. V případě suspekce, byť vyhodnocené jako nepravděpodobné, model nález raději předkládá lékaři. Na základě těchto výsledků lze očekávat, že v budoucnu bude AI hrát klíčovou roli v oblasti radiologie jako pomocný nástroj pro hodnotící specialisty. Aby k tomu mohlo dojít, je potřeba vyřešit nejen technické, ale i některé medicínské a regulatorní aspekty. Zásadní je dostupnost kvalitních a spolehlivých informací nejen o přínosech, ale také o limitacích možností strojového učení a AI v medicíně.

In recent years healthcare is undergoing significant changes due to technological innovations, with Artificial Intelligence (AI) being a key trend. Particularly in radiodiagnostics, according to studies, AI has the potential to enhance accuracy and efficiency. We focus on AI’s role in diagnosing pulmonary lesions, which could indicate lung cancer, based on chest X-rays. Despite lower sensitivity in comparison to other methods like chest CT, due to its routine use, X-rays often provide the first detection of lung lesions. We present our deep learning-based solution aimed at improving lung lesion detection, especially during early-stage of illness. We then share results from our previous studies validating this model in two different clinical settings: a general hospital with low prevalence findings and a specialized oncology center. Based on a quantitative comparison with the conclusions of radiologists of different levels of experience, our model achieves high sensitivity, but lower specificity than comparing radiologists. In the context of clinical requirements and AI-assisted diagnostics, the experience and clinical reasoning of the doctor play a crucial role, therefore we currently lean more towards models with higher sensitivity over specificity. Even unlikely suspicions are presented to the doctor. Based on these results, it can be expected that in the future artificial intelligence will play a key role in the field of radiology as a supporting tool for evaluating specialists. To achieve this, it is necessary to solve not only technical but also medical and regulatory aspects. It is crucial to have access to quality and reliable information not only about the benefits but also about the limitations of machine learning and AI in medicine.

• Klíčová slova
• skiagram hrudníku,
• MeSH
• časná detekce nádoru metody   MeSH
• hrudník * diagnostické zobrazování   MeSH
• interpretace obrazu počítačem MeSH
• lidé MeSH
• nádory plic diagnostické zobrazování   MeSH
• radiografie MeSH
• retrospektivní studie MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Geografické názvy
• Česká republika MeSH

• MeSH
• emigranti a imigranti statistika a číselné údaje   MeSH
• halucinogeny terapeutické užití   MeSH
• klinická studie jako téma MeSH
• kolorektální nádory prevence a kontrola   MeSH
• kompenzace a odškodnění zákonodárství a právo   MeSH
• lidé MeSH
• paliativní péče metody   trendy   MeSH
• plošný screening MeSH
• poskytování zdravotní péče * organizace a řízení   MeSH
• srdeční zástava terapie   MeSH
• umělá inteligence trendy   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• novinové články MeSH

• MeSH
• dítě MeSH
• péče o pacienty v kritickém stavu MeSH
• terapie náhlých příhod MeSH
• Check Tag
• dítě MeSH

• Konspekt
• Pediatrie
• NLK Obory
• pediatrie
• urgentní lékařství
• NLK Publikační typ
• kolektivní monografie

Objects: Health Behaviours in School-aged Children (HBSC) is an international survey programme aiming to investigate adolescents' health behaviours, subjective perception of health status, wellbeing, and the related contextual information. Our scoping review aimed to synthesise the evidence from HBSC about the relationship between family environmental contributors and adolescents' health-related outcomes. Methods: We searched previous studies from six electronic databases. Two researchers identified the qualified publications independently by abstract and full-text screening with the assistance of an NLP-based AI instrument, ASReview. Publications were included if they were based on HBSC data and investigated the effects of family environment on adolescents' health outcomes. Researches addressed family-related factors as mediators or moderators were also included. Results: A total of 241 articles were included. Family environmental contributors could be mapped into six categories: (1) Demographic backgrounds (N = 177); (2) General family's psycho-socio functions (N = 44); (3) Parenting behaviours (N = 100); (4) Parental health behaviours (N = 7); (5) Family activities (N = 24); and (6) Siblings (N = 7). Except for 75 papers that assessed family variables as moderators (N = 70) and mediators (N = 7), the others suggested family environment was an independent variable. Only five studies employed the data-driven approach. Conclusion: Our results suggest most research studies focussed on the influences of family demographic backgrounds on adolescents' health. The researches related to parental health behaviours and siblings are most inadequate. Besides, we recommend further research studies to focus on the mediator/moderator roles of the family, for exploring the deep mechanism of the family's impacts. Also, it would be valuable to consider data-driven analysis more in the future, as HBSC has mass variables and data.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH