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
Tento přehledový článek se zaměřuje na základní principy technologií umělé inteligence (AI), možnosti jejich využití v medicíně a na příklady aplikací, které již byly začleněny do klinické praxe. Diskutuje také klíčové výzvy včetně etických otázek, jako je ochrana soukromí pacientů, algoritmická bias a problém transparentnosti modelů AI. Článek zdůrazňuje nutnost integrace AI do medicíny způsobem, který zajistí bezpečnost a důvěryhodnost, a současně vyzdvihuje význam vzdělávání zdravotnických profesionálů v oblasti AI. Umělá inteligence nabízí potenciál ke zlepšení přesnosti diagnostiky, efektivity péče a podpory při klinickém rozhodování, přičemž optimálních výsledků lze dosáhnout spoluprací mezi lékaři a systémy AI.
This review article focuses on the fundamental principles of artificial intelligence (AI) technologies, their utilisation in medicine, and examples of applications that have already been incorporated into clinical practice. It also discusses key challenges, including ethical issues such as patient data privacy, algorithmic bias, and the transparency problem of AI models. The article emphasizes the necessity of integrating AI into medicine in a manner that ensures safety and trustworthiness, while underscoring the importance of educating healthcare professionals about AI. Artificial intelligence offers the potential to enhance diagnostic accuracy, the efficiency of care, and support for clinical decision-making, with optimal outcomes being achieved through collaboration between physicians and AI systems.
- MeSH
- Algorithms MeSH
- Medicine * MeSH
- Humans MeSH
- Nephrology MeSH
- Artificial Intelligence * ethics MeSH
- Large Language Models MeSH
- Computer Security MeSH
- Check Tag
- Humans MeSH
- Publication type
- Review 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
- Whole Body Imaging * methods MeSH
- Deep Learning * MeSH
- Child MeSH
- Adult MeSH
- Fluorodeoxyglucose F18 * MeSH
- Cohort Studies MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Neoplasms * diagnostic imaging MeSH
- Positron Emission Tomography Computed Tomography * methods MeSH
- Image Processing, Computer-Assisted * methods MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Validation Study MeSH
Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major challenge, particularly pertinent for 'actionable' genes such as thyroid hormone transporter MCT8 (encoded by the X-linked SLC16A2 gene), where loss-of-function (LoF) variants cause a rare neurodevelopmental and (treatable) metabolic disorder in males. The combination of deep phenotyping data with functional and computational tests and with outcomes in population cohorts, enabled us to: (i) identify the genetic aetiology of divergent clinical phenotypes of MCT8 deficiency with genotype-phenotype relationships present across survival and 24 out of 32 disease features; (ii) demonstrate a mild phenocopy in ~400,000 individuals with common genetic variants in MCT8; (iii) assess therapeutic effectiveness, which did not differ among LoF-categories; (iv) advance structural insights in normal and mutated MCT8 by delineating seven critical functional domains; (v) create a pathogenicity-severity MCT8 variant classifier that accurately predicted pathogenicity (AUC:0.91) and severity (AUC:0.86) for 8151 variants. Our information-dense mapping provides a generalizable approach to advance multiple dimensions of rare genetic disorders.
- MeSH
- Deep Learning * MeSH
- Child MeSH
- Adult MeSH
- Phenotype * MeSH
- Genetic Variation MeSH
- Genetic Association Studies MeSH
- Genomics methods MeSH
- Thyroid Hormones metabolism genetics MeSH
- Humans MeSH
- X-Linked Intellectual Disability genetics metabolism MeSH
- Adolescent MeSH
- Loss of Function Mutation MeSH
- Child, Preschool MeSH
- Monocarboxylic Acid Transporters * genetics metabolism MeSH
- Severity of Illness Index MeSH
- Muscular Atrophy genetics metabolism pathology MeSH
- Muscle Hypotonia genetics metabolism MeSH
- Symporters * genetics metabolism MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Child, Preschool MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
OBJECTIVES: Artificial Intelligence (AI), particularly deep learning, has significantly impacted healthcare, including dentistry, by improving diagnostics, treatment planning, and prognosis prediction. This systematic mapping review explores the current applications of deep learning in dentistry, offering a comprehensive overview of trends, models, and their clinical significance. MATERIALS AND METHODS: Following a structured methodology, relevant studies published from January 2012 to September 2023 were identified through database searches in PubMed, Scopus, and Embase. Key data, including clinical purpose, deep learning tasks, model architectures, and data modalities, were extracted for qualitative synthesis. RESULTS: From 21,242 screened studies, 1,007 were included. Of these, 63.5% targeted diagnostic tasks, primarily with convolutional neural networks (CNNs). Classification (43.7%) and segmentation (22.9%) were the main methods, and imaging data-such as cone-beam computed tomography and orthopantomograms-were used in 84.4% of cases. Most studies (95.2%) applied fully supervised learning, emphasizing the need for annotated data. Pathology (21.5%), radiology (17.5%), and orthodontics (10.2%) were prominent fields, with 24.9% of studies relating to more than one specialty. CONCLUSION: This review explores the advancements in deep learning in dentistry, particulary for diagnostics, and identifies areas for further improvement. While CNNs have been used successfully, it is essential to explore emerging model architectures, learning approaches, and ways to obtain diverse and reliable data. Furthermore, fostering trust among all stakeholders by advancing explainable AI and addressing ethical considerations is crucial for transitioning AI from research to clinical practice. CLINICAL RELEVANCE: This review offers a comprehensive overview of a decade of deep learning in dentistry, showcasing its significant growth in recent years. By mapping its key applications and identifying research trends, it provides a valuable guide for future studies and highlights emerging opportunities for advancing AI-driven dental care.
- MeSH
- Deep Learning * MeSH
- Humans MeSH
- Dentistry * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
- Systematic Review MeSH
In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training datasets. The protracted generation time of physics-based models, often employed to produce these datasets, limits their broad applicability. We introduce FakET, a method based on neural style transfer, capable of simulating the forward operator of any cryo transmission electron microscope. It can be used to adapt a synthetic training dataset according to reference data producing high-quality simulated micrographs or tilt-series. To assess the quality of our generated data, we used it to train a state-of-the-art localization and classification architecture and compared its performance with a counterpart trained on benchmark data. Remarkably, our technique matches the performance, boosts data generation speed 750×, uses 33× less memory, and scales well to typical transmission electron microscope detector sizes. It leverages GPU acceleration and parallel processing. The source code is available at https://github.com/paloha/faket/.
PURPOSE: Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep-learning strategies on histology samples to predict outcome for osteosarcoma in the neoadjuvant setting. EXPERIMENTAL DESIGN: Our study relies on a training cohort from New York University (NYU; New York, NY) and an external cohort from Charles University (Prague, Czechia). We trained and validated the performance of a supervised approach that integrates neural network predictions of necrosis/tumor content and compared predicted overall survival (OS) using Kaplan-Meier curves. Furthermore, we explored morphology-based supervised and self-supervised approaches to determine whether intrinsic histomorphologic features could serve as a potential marker for OS in the neoadjuvant setting. RESULTS: Excellent correlation between the trained network and pathologists was obtained for the quantification of necrosis content (R2 = 0.899; r = 0.949; P < 0.0001). OS prediction cutoffs were consistent between pathologists and the neural network (22% and 30% of necrosis, respectively). The morphology-based supervised approach predicted OS; P = 0.0028, HR = 2.43 (1.10-5.38). The self-supervised approach corroborated the findings with clusters enriched in necrosis, fibroblastic stroma, and osteoblastic morphology associating with better OS [log-2 hazard ratio (lg2 HR); -2.366; -1.164; -1.175; 95% confidence interval, (-2.996 to -0.514)]. Viable/partially viable tumor and fat necrosis were associated with worse OS [lg2 HR; 1.287; 0.822; 0.828; 95% confidence interval, (0.38-1.974)]. CONCLUSIONS: Neural networks can be used to automatically estimate the necrosis to tumor ratio, a quantitative metric predictive of survival. Furthermore, we identified alternate histomorphologic biomarkers specific to the necrotic and tumor regions, which could serve as predictors.
- MeSH
- Deep Learning MeSH
- Child MeSH
- Adult MeSH
- Kaplan-Meier Estimate MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Bone Neoplasms * mortality pathology MeSH
- Necrosis * MeSH
- Neoadjuvant Therapy * methods MeSH
- Neural Networks, Computer * MeSH
- Osteosarcoma * mortality pathology therapy MeSH
- Prognosis MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
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
BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. METHODS: In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). RESULTS: We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). DISCUSSION: The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.
- MeSH
- Deep Learning * MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Longitudinal Studies MeSH
- Magnetic Resonance Imaging * MeSH
- Brain * diagnostic imaging pathology MeSH
- Neurodegenerative Diseases diagnostic imaging MeSH
- Cross-Sectional Studies MeSH
- Retrospective Studies MeSH
- Multiple Sclerosis * diagnostic imaging pathology MeSH
- Aging * pathology physiology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
- MeSH
- Biological Products * MeSH
- Deep Learning MeSH
- Coagulase MeSH
- Humans MeSH
- Drug Design * MeSH
- Check Tag
- Humans MeSH
