BACKGROUND: Tumor consistency is considered to be a critical factor for the surgical removal of meningiomas and its preoperative assessment is intensively studied. A significant drawback in the research of predictive methods is the lack of a clear shared definition of tumor consistency, with most authors resorting to subjective binary classification labeling the samples as "soft" and "hard." This classification is highly observer-dependent and its discrete nature fails to capture the fine nuances in tumor consistency. To compensate for these shortcomings, we examined the utility of texture analysis to provide an objective observer-independent continuous measure of meningioma consistency. METHODS: A total of 169 texturometric measurements were conducted using the Brookfield CT3 Texture Analyzer on meningioma samples from five patients immediately after the removal and on the first, second, and seventh postoperative day. The relationship between measured stiffness and time from sample extraction, subjectively assessed consistency grade and histopathological features (amount of collagen and reticulin fibers, presence of psammoma bodies, predominant microscopic morphology) was analyzed. RESULTS: The stiffness measurements exhibited significantly lower variance within a sample than among samples (p = 0.0225) and significant increase with a higher objectively assessed consistency grade (p = 0.0161, p = 0.0055). A significant negative correlation was found between the measured stiffness and the time from sample extraction (p < 0.01). A significant monotonic relationship was revealed between stiffness values and amount of collagen I and reticulin fibers; there were no statistically significant differences between histological phenotypes in regard to presence of psammoma bodies and predominant microscopic morphology. CONCLUSIONS: We conclude that the values yielded by texture analysis are highly representative of an intrinsic consistency-related quality of the sample despite the influence of intra-sample heterogeneity and that our proposed method can be used to conduct quantitative studies on the role of meningioma consistency.
- MeSH
- kolagen MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- meningeální nádory * chirurgie patologie MeSH
- meningeom * diagnostické zobrazování chirurgie patologie MeSH
- retikulin MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Publikační typ
- abstrakt z konference MeSH
Falls are a serious problem in the hospital setting and home environments. However, this problem does not only affect the elderly, but also people who have had surgery, have disabling problems, have associated diagnoses (such as poor eyesight, confusion, etc.) or are dizzy or have walking aids. The aim of research was to find, compare and implement fall detectors especially for the hospital environment. This paper summarizes possible fall detectors. Various technological solutions were selected for testing, including wearable technologies as well as contactless technologies based on PIR detectors and mmWave technologies. The selected fall detectors were tested in living laboratory of HEALTHLab.vsb.cz and then in Hospital AGEL Třinec - Podlesí. The best result of the testing was the use of two Vayyar Home Care devices in one room, thus achieving a detection accuracy of 92.50 % and a sensitivity of 92.50 %.
- MeSH
- laboratoře MeSH
- lidé MeSH
- nemocnice MeSH
- senioři MeSH
- služby domácí péče * MeSH
- úrazy pádem * prevence a kontrola MeSH
- závrať MeSH
- Check Tag
- lidé MeSH
- senioři MeSH
- Publikační typ
- časopisecké články MeSH
V posledních letech se v ČR stává využívání koloběžek stále populárnějším způsobem dopravy, zejména ve velkých městech. S nárůstem popularity však přichází i alarmující nárůst počtu úrazů, které jsou s tímto typem dopravy spojeny. Tento článek prezentuje výsledky retrospektivní studie, která hodnotila klinické a demografické charakteristiky pacientů s těmito úrazy, vč. diagnóz, výsledků a komplikací. Z analýzy bylo zjištěno, že v Ústřední vojenské nemocnici bylo v letech 2010–2022 ošetřeno 21 pacientů s úrazy hlavy a krku spojenými s jízdou na koloběžkách. Hlavní diagnózy zahrnovaly akutní subdurální hematom (5; 24 %), intracerebrální hematom (1; 5 %), kontuze mozku (5; 24 %), frakturu lbi (2; 10 %) a poranění obličejového skeletu (8; 38 %). Ze závažných komplikujících diagnóz nesouvisejících s kraniofaciálním poraněním se vyskytlo postižení axiálního skeletu (2; 10 %) a tenzní pneumotorax (1; 5 %). Hospitalizace byla nutná u 71 % pacientů a neurochirurgická intervence byla provedena u 4 pacientů. Závažným problémem je jízda na koloběžkách v ebrietě a pod vlivem omamných látek, která byla zjištěna v 29 % případů. Tyto výsledky poukazují na potřebu zvýšené opatrnosti a bezpečnosti při používání koloběžek.
In recent years, the use of scooters has become an increasingly popular mode of transport in the Czech Republic, especially in large cities. However, with the increase in popularity comes an alarming increase in the number of accidents associated with this type of transport. This article presents the results of a retrospective study that evaluated the clinical and demographic characteristics of patients with these injuries, including diagnoses, outcomes, and complications. The analysis found that 21 patients with head and neck injuries associated with riding scooters were treated at the Central Military Hospital between 2010 and 2022. Major diagnoses included acute subdural hematoma (5; 24%), intracerebral hematoma (1; 5%), brain contusion (5; 24%), skull fracture (2; 10%) and facial skeletal injury (8; 38%). Of the serious complicating diagnoses not related to craniofacial injury, involvement of axial skeletal injury (2; 10%) and tension pneumothorax (1; 5%) occurred. Hospitalization was required in 71% of patients and neurosurgical intervention was performed in 4 patients. A serious problem is riding scooters while under the influence of alcohol and narcotics, which was found in 29% of cases. These results point to the need for increased caution and safety when using scooters.
- Klíčová slova
- bezpečnost silničního provozu, koloběžka,
- MeSH
- akutní subdurální hematom etiologie MeSH
- dopravní nehody * MeSH
- kontuze mozku etiologie MeSH
- kraniocerebrální traumata * etiologie MeSH
- lidé MeSH
- poranění obličeje etiologie MeSH
- retrospektivní studie MeSH
- traumatické intracerebrální krvácení etiologie MeSH
- úrazy a nehody MeSH
- Check Tag
- lidé MeSH
- MeSH
- disociační poruchy patologie MeSH
- dospělí MeSH
- komunikace MeSH
- lidé středního věku MeSH
- lidé MeSH
- narození mrtvého plodu psychologie MeSH
- perinatální smrt MeSH
- psychický stres etiologie patologie psychologie terapie MeSH
- psychologická první pomoc metody MeSH
- zármutek * MeSH
- ztráta blízké osoby MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- kazuistiky MeSH
- přehledy MeSH
BACKGROUND: Artificial intelligence (AI) has advanced substantially in recent years, transforming many industries and improving the way people live and work. In scientific research, AI can enhance the quality and efficiency of data analysis and publication. However, AI has also opened up the possibility of generating high-quality fraudulent papers that are difficult to detect, raising important questions about the integrity of scientific research and the trustworthiness of published papers. OBJECTIVE: The aim of this study was to investigate the capabilities of current AI language models in generating high-quality fraudulent medical articles. We hypothesized that modern AI models can create highly convincing fraudulent papers that can easily deceive readers and even experienced researchers. METHODS: This proof-of-concept study used ChatGPT (Chat Generative Pre-trained Transformer) powered by the GPT-3 (Generative Pre-trained Transformer 3) language model to generate a fraudulent scientific article related to neurosurgery. GPT-3 is a large language model developed by OpenAI that uses deep learning algorithms to generate human-like text in response to prompts given by users. The model was trained on a massive corpus of text from the internet and is capable of generating high-quality text in a variety of languages and on various topics. The authors posed questions and prompts to the model and refined them iteratively as the model generated the responses. The goal was to create a completely fabricated article including the abstract, introduction, material and methods, discussion, references, charts, etc. Once the article was generated, it was reviewed for accuracy and coherence by experts in the fields of neurosurgery, psychiatry, and statistics and compared to existing similar articles. RESULTS: The study found that the AI language model can create a highly convincing fraudulent article that resembled a genuine scientific paper in terms of word usage, sentence structure, and overall composition. The AI-generated article included standard sections such as introduction, material and methods, results, and discussion, as well a data sheet. It consisted of 1992 words and 17 citations, and the whole process of article creation took approximately 1 hour without any special training of the human user. However, there were some concerns and specific mistakes identified in the generated article, specifically in the references. CONCLUSIONS: The study demonstrates the potential of current AI language models to generate completely fabricated scientific articles. Although the papers look sophisticated and seemingly flawless, expert readers may identify semantic inaccuracies and errors upon closer inspection. We highlight the need for increased vigilance and better detection methods to combat the potential misuse of AI in scientific research. At the same time, it is important to recognize the potential benefits of using AI language models in genuine scientific writing and research, such as manuscript preparation and language editing.
- MeSH
- algoritmy * MeSH
- analýza dat MeSH
- jazyk (prostředek komunikace) MeSH
- lidé MeSH
- sémantika MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
This study aims to develop a fully automated imaging protocol independent system for pituitary adenoma segmentation from magnetic resonance imaging (MRI) scans that can work without user interaction and evaluate its accuracy and utility for clinical applications. We trained two independent artificial neural networks on MRI scans of 394 patients. The scans were acquired according to various imaging protocols over the course of 11 years on 1.5T and 3T MRI systems. The segmentation model assigned a class label to each input pixel (pituitary adenoma, internal carotid artery, normal pituitary gland, background). The slice segmentation model classified slices as clinically relevant (structures of interest in slice) or irrelevant (anterior or posterior to sella turcica). We used MRI data of another 99 patients to evaluate the performance of the model during training. We validated the model on a prospective cohort of 28 patients, Dice coefficients of 0.910, 0.719, and 0.240 for tumour, internal carotid artery, and normal gland labels, respectively, were achieved. The slice selection model achieved 82.5% accuracy, 88.7% sensitivity, 76.7% specificity, and an AUC of 0.904. A human expert rated 71.4% of the segmentation results as accurate, 21.4% as slightly inaccurate, and 7.1% as coarsely inaccurate. Our model achieved good results comparable with recent works of other authors on the largest dataset to date and generalized well for various imaging protocols. We discussed future clinical applications, and their considerations. Models and frameworks for clinical use have yet to be developed and evaluated.
- MeSH
- adenom * diagnostické zobrazování chirurgie MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- nádory hypofýzy * diagnostické zobrazování chirurgie MeSH
- neuronové sítě (počítačové) MeSH
- počítačové zpracování obrazu metody MeSH
- prospektivní studie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
The vast majority of agricultural land undergoes abiotic stress that can significantly reduce agricultural yields. Understanding the mechanisms of plant defenses against stresses and putting this knowledge into practice is, therefore, an integral part of sustainable agriculture. In this review, we focus on current findings in plant resistance to four cardinal abiotic stressors-drought, heat, salinity, and low temperatures. Apart from the description of the newly discovered mechanisms of signaling and resistance to abiotic stress, this review also focuses on the importance of primary and secondary metabolites, including carbohydrates, amino acids, phenolics, and phytohormones. A meta-analysis of transcriptomic studies concerning the model plant Arabidopsis demonstrates the long-observed phenomenon that abiotic stressors induce different signals and effects at the level of gene expression, but genes whose regulation is similar under most stressors can still be traced. The analysis further reveals the transcriptional modulation of Golgi-targeted proteins in response to heat stress. Our analysis also highlights several genes that are similarly regulated under all stress conditions. These genes support the central role of phytohormones in the abiotic stress response, and the importance of some of these in plant resistance has not yet been studied. Finally, this review provides information about the response to abiotic stress in major European crop plants-wheat, sugar beet, maize, potatoes, barley, sunflowers, grapes, rapeseed, tomatoes, and apples.
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
- rentgendiagnostika MeSH
- retrospektivní studie MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Geografické názvy
- Česká republika MeSH
