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.
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- deep learning * MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- longitudinální studie MeSH
- magnetická rezonanční tomografie * MeSH
- mozek * diagnostické zobrazování patologie MeSH
- neurodegenerativní nemoci diagnostické zobrazování MeSH
- průřezové studie MeSH
- retrospektivní studie MeSH
- roztroušená skleróza * diagnostické zobrazování patologie MeSH
- stárnutí * patologie fyziologie MeSH
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- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
- MeSH
- biologické přípravky * MeSH
- deep learning MeSH
- koagulasa MeSH
- lidé MeSH
- racionální návrh léčiv * MeSH
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- lidé MeSH
Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for distinguishing between malignant and healthy bone is essential. Bones that have cancer have a different texture than bones in unaffected areas. Diagnosing hematological illnesses relies on correct labeling and categorizing nucleated cells in the bone marrow. However, timely diagnosis and treatment are hampered by pathologists' need to identify specimens, which can be sensitive and time-consuming manually. Humanity's ability to evaluate and identify these more complicated illnesses has significantly been bolstered by the development of artificial intelligence, particularly machine, and deep learning. Conversely, much research and development is needed to enhance cancer cell identification-and lower false alarm rates. We built a deep learning model for morphological analysis to solve this problem. This paper introduces a novel deep convolutional neural network architecture in which hybrid multi-objective and category-based optimization algorithms are used to optimize the hyperparameters adaptively. Using the processed cell pictures as input, the proposed model is then trained with an optimized attention-based multi-scale convolutional neural network to identify the kind of cancer cells in the bone marrow. Extensive experiments are run on publicly available datasets, with the results being measured and evaluated using a wide range of performance indicators. In contrast to deep learning models that have already been trained, the total accuracy of 99.7% was determined to be superior.
Cíl: Automaticky předpovídat stabilitu aterosklerotického plátu v karotidě ze standardních transverzálních ultrazvukových obrazů v B-modu za použití hlubokého učení. Spolehlivý prediktor by snížil potřebu klinických kontrol i farmakologické či chirurgické léčby. Metody: Automaticky byla lokalizována oblast zájmu obsahující karotidu. Adversariální metoda segmentace byla natrénována na kombinaci malého kompletně anotovaného datasetu a většího slabě anotovaného datasetu. Multikriteriální regrese s automatickou adaptací vah byla použita k predikci série klinicky relevantních atributů, vč. nárůstu tloušťky plátu během 3 let. Výsledky: Současnou šíři plátu bylo možno odhadnout s vysokou korelací (ρ = 0,32) a velmi vysokou statistickou signifikancí. Odhadovaný budoucí nárůst šíře plátu byl korelován méně (ρ = 0,22), ale stále statisticky významně (p < 0,01). Korelace mezi automatickým a expertním hodnocením echogenicity, hladkosti a kalcifikací byla ještě nižší. Závěr: Potvrdili jsme závislost mezi vzhledem plátu v ultrazvukovém obraze a pravděpodobností jeho budoucího růstu, ale je příliš slabá, než aby byla využitelná v klinické praxi jako jediný prediktor stability plátu.
Aim: To automatically predict the stability of carotid artery plaque from standard B-mode transversal ultrasound images using deep learning. A reliable predictor would reduce the need for follow-up examination and pharmacological and surgical treatment. Methods: A region of interest containing the carotid artery was automatically localized. An adversarial segmentation method was trained on a combination of a small pixelwise annotated dataset and a larger weakly annotated dataset. A multicriterion regression with automatic weight adaptation was applied to predict a series of clinically relevant attributes, including the plaque width increase over 3 years. Results: The current plaque width could be estimated with a high correlation (ρ = 0.32) and a very high statistical significance. The estimated future increase of the plaque width was correlated less (ρ = 0.22) but statistically significantly (P < 0.01). The correlation between automatic and expert assessments of echogenicity, smoothness and calcification was even smaller. Conclusion: We confirmed a relationship between the plaque appearance in ultrasound and the probability of its future growth, but it is too weak to be used in clinical practice as the sole predictor of the plaque stability.
- MeSH
- algoritmy MeSH
- aterosklerotický plát * diagnostické zobrazování patologie MeSH
- deep learning MeSH
- lidé MeSH
- počítačové metodologie MeSH
- prognóza MeSH
- regresní analýza MeSH
- statistika jako téma MeSH
- ultrasonografie karotid * statistika a číselné údaje MeSH
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- lidé MeSH
- Publikační typ
- klinická studie MeSH
- práce podpořená grantem MeSH
PURPOSE: Recent papers suggested a correlation between the risk of distant metastasis (DM) and dose outside the PTV, though conclusions in different publications conflicted. This study resolves these conflicts and provides a compelling explanation of prognostic factors. MATERIALS AND METHODS: A dataset of 478 NSCLC patients treated with SBRT (IMRT or VMAT) was analyzed. We developed a deep learning model for DM prediction and explainable AI was used to identify the most significant prognostic factors. Subsequently, the prognostic power of the extracted features and clinical details were analyzed using conventional statistical methods. RESULTS: Treatment technique, tumor features, and dosiomic features in a 3 cm wide ring around the PTV (PTV3cm) were identified as the strongest predictors of DM. The Hazard Ratio (HR) for Dmean,PTV3cm was significantly above 1 (p < 0.001). There was no significance of the PTV3cm dose after treatment technique stratification. However, the dose in PTV3cm was found to be a highly significant DM predictor (HR > 1, p = 0.004) when analyzing only VMAT patients with small and spherical tumors (i.e., sphericity > 0.5). CONCLUSIONS: The main reason for conflicting conclusions in previous papers was inconsistent datasets and insufficient consideration of confounding variables. No causal correlation between the risk of DM and dose outside the PTV was found. However, the mean dose to PTV3cm can be a significant predictor of DM in small spherical targets treated with VMAT, which might clinically imply considering larger PTV margins for smaller, more spherical tumors (e.g., if IGTV > 2 cm, then margin ≤ 7 mm, else margin > 7 mm).
- MeSH
- celková dávka radioterapie * MeSH
- deep learning * MeSH
- lidé středního věku MeSH
- lidé MeSH
- metastázy nádorů MeSH
- nádory plic * patologie radioterapie MeSH
- nemalobuněčný karcinom plic * radioterapie patologie MeSH
- plánování radioterapie pomocí počítače metody MeSH
- prognóza MeSH
- radiochirurgie * metody MeSH
- radioterapie s modulovanou intenzitou metody MeSH
- senioři MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min-max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min-max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.
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- Bayesova věta MeSH
- deep learning * MeSH
- diagnóza počítačová metody MeSH
- Formicidae MeSH
- kardiovaskulární nemoci * diagnóza MeSH
- lidé MeSH
- neuronové sítě * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Advancements in deep learning speech representations have facilitated the effective use of extensive unlabeled speech datasets for Parkinson's disease (PD) modeling with minimal annotated data. This study employs the non-fine-tuned wav2vec 1.0 architecture to develop machine learning models for PD speech diagnosis tasks, such as cross-database classification and regression to predict demographic and articulation characteristics. The primary aim is to analyze overlapping components within the embeddings on both classification and regression tasks, investigating whether latent speech representations in PD are shared across models, particularly for related tasks. Firstly, evaluation using three multi-language PD datasets showed that wav2vec accurately detected PD based on speech, outperforming feature extraction using mel-frequency cepstral coefficients in the proposed cross-database classification scenarios. In cross-database scenarios using Italian and English-read texts, wav2vec demonstrated performance comparable to intra-dataset evaluations. We also compared our cross-database findings against those of other related studies. Secondly, wav2vec proved effective in regression, modeling various quantitative speech characteristics related to articulation and aging. Ultimately, subsequent analysis of important features examined the presence of significant overlaps between classification and regression models. The feature importance experiments discovered shared features across trained models, with increased sharing for related tasks, further suggesting that wav2vec contributes to improved generalizability. The study proposes wav2vec embeddings as a next promising step toward a speech-based universal model to assist in the evaluation of PD.
- MeSH
- databáze faktografické * MeSH
- deep learning MeSH
- lidé středního věku MeSH
- lidé MeSH
- Parkinsonova nemoc * patofyziologie MeSH
- řeč * fyziologie MeSH
- senioři MeSH
- strojové učení MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~ 50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts' histopathological assessment standards (> 85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.
Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of "Radiomics" involves the extraction of high-dimensional features, often referred to as "Radiomic features," from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.
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- adenom * diagnostické zobrazování MeSH
- deep learning MeSH
- lidé MeSH
- nádory hypofýzy * diagnostické zobrazování MeSH
- počítačové zpracování obrazu metody MeSH
- radiomika MeSH
- strojové učení MeSH
- umělá inteligence MeSH
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- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- systematický přehled MeSH
Telemedicine is an emerging development in the healthcare domain, where the Internet of Things (IoT) fiber optics technology assists telemedicine applications to improve overall digital healthcare performances for society. Telemedicine applications are bowel disease monitoring based on fiber optics laser endoscopy, gastrointestinal disease fiber optics lights, remote doctor-patient communication, and remote surgeries. However, many existing systems are not effective and their approaches based on deep reinforcement learning have not obtained optimal results. This paper presents the fiber optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications. In the proposed system, we propose the adaptive security deep q-learning network (ASDQN) algorithm methodology to execute all telemedicine applications under their given quality of services (deadline, latency, security, and resources) constraints. For the problem solution, we have exploited different fiber optics endoscopy datasets with images, video, and numeric data for telemedicine applications. The objective is to minimize the overall latency of telemedicine applications (e.g., local, communication, and edge nodes) and maximize the overall rewards during offloading and scheduling on different nodes. The simulation results show that ASDQN outperforms all telemedicine applications with their QoS and objectives compared to existing state action reward state (SARSA) and deep q-learning network (DQN) policy during execution and scheduling on different nodes.
- MeSH
- algoritmy MeSH
- deep learning * MeSH
- internet věcí * MeSH
- lidé MeSH
- technologie optických vláken MeSH
- telemedicína * MeSH
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- lidé MeSH
- Publikační typ
- časopisecké články MeSH
