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2.
Adv Exp Med Biol ; 1213: 149-163, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32030669

RESUMEN

The skin is the largest organ of our body. Skin disease abnormalities which occur within the skin layers are difficult to examine visually and often require biopsies to make a confirmation on a suspected condition. Such invasive methods are not well-accepted by children and women due to the possibility of scarring. Optical coherence tomography (OCT) is a non-invasive technique enabling in vivo examination of sub-surface skin tissue without the need for excision of tissue. However, one of the challenges in OCT imaging is the interpretation and analysis of OCT images. In this review, we discuss the various methodologies in skin layer segmentation and how it could potentially improve the management of skin diseases. We also present a review of works which use advanced machine learning techniques to achieve layers segmentation and detection of skin diseases. Lastly, current challenges in analysis and applications are also discussed.


Asunto(s)
Procesamiento de Imagen Asistida por Computador , Aprendizaje Automático , Enfermedades de la Piel/diagnóstico por imagen , Piel/diagnóstico por imagen , Tomografía de Coherencia Óptica , Humanos
3.
Adv Exp Med Biol ; 1232: 285-290, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-31893422

RESUMEN

In neonatal intensive care units (NICUs), 87.5% of alarms by the monitoring system are false alarms, often caused by the movements of the neonates. Such false alarms are not only stressful for the neonates as well as for their parents and caregivers, but may also lead to longer response times in real critical situations. The aim of this project was to reduce the rates of false alarms by employing machine learning algorithms (MLA), which intelligently analyze data stemming from standard physiological monitoring in combination with cerebral oximetry data (in-house built, OxyPrem). MATERIALS & METHODS: Four popular MLAs were selected to categorize the alarms as false or real: (i) decision tree (DT), (ii) 5-nearest neighbors (5-NN), (iii) naïve Bayes (NB) and (iv) support vector machine (SVM). We acquired and processed monitoring data (median duration (SD): 54.6 (± 6.9) min) of 14 preterm infants (gestational age: 26 6/7 (± 2 5/7) weeks). A hybrid method of filter and wrapper feature selection generated the candidate subset for training these four MLAs. RESULTS: A high specificity of >99% was achieved by all four approaches. DT showed the highest sensitivity (87%). The cerebral oximetry data improved the classification accuracy. DISCUSSION & CONCLUSION: Despite a (as yet) low amount of data for training, the four MLAs achieved an excellent specificity and a promising sensitivity. Presently, the current sensitivity is insufficient since, in the NICU, it is crucial that no real alarms are missed. This will most likely be improved by including more subjects and data in the training of the MLAs, which makes pursuing this approach worthwhile.


Asunto(s)
Unidades de Cuidado Intensivo Neonatal , Cuidado Intensivo Neonatal , Aprendizaje Automático , Monitoreo Fisiológico , Oximetría , Teorema de Bayes , Circulación Cerebrovascular , Humanos , Recién Nacido , Recien Nacido Prematuro , Cuidado Intensivo Neonatal/métodos , Monitoreo Fisiológico/métodos , Oximetría/métodos , Oximetría/normas
4.
Anticancer Res ; 40(1): 271-280, 2020 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-31892576

RESUMEN

BACKGROUND/AIM: To investigate whether a radiomic machine learning (ML) approach employing texture-analysis (TA) features extracted from primary tumor lesions (PTLs) is able to predict tumor grade (TG) and nodal status (NS) in patients with oropharyngeal (OP) and oral cavity (OC) squamous-cell carcinoma (SCC). PATIENTS AND METHODS: Contrast-enhanced CT images of 40 patients with OP and OC SCC were post-processed to extract TA features from PTLs. A feature selection method and different ML algorithms were applied to find the most accurate subset of features to predict TG and NS. RESULTS: For the prediction of TG, the best accuracy (92.9%) was achieved by Naïve Bayes (NB), bagging of NB and K Nearest Neighbor (KNN). For the prediction of NS, J48, NB, bagging of NB and boosting of J48 overcame the accuracy of 90%. CONCLUSION: A radiomic ML approach applied to PTLs is able to predict TG and NS in patients with OC and OP SCC.


Asunto(s)
Carcinoma de Células Escamosas/diagnóstico por imagen , Carcinoma de Células Escamosas/patología , Ganglios Linfáticos/patología , Neoplasias de la Boca/diagnóstico por imagen , Neoplasias de la Boca/patología , Neoplasias Orofaríngeas/diagnóstico por imagen , Neoplasias Orofaríngeas/patología , Anciano , Algoritmos , Humanos , Aprendizaje Automático , Clasificación del Tumor , Tomografía Computarizada por Rayos X
5.
Nervenarzt ; 91(1): 18-25, 2020 Jan.
Artículo en Alemán | MEDLINE | ID: mdl-31919551

RESUMEN

Imaging methods have become the main approach for identifying dysfunctional neuronal networks in schizophrenia. This review article presents recent results of disorders of neuronal networks at structural and functional levels and summarizes the current developments. Large multicenter analyses have further established patterns of regional brain alterations, while novel methods in magnetic resonance (MR) morphometry have contributed to differentiating early from delayed brain structural changes. The use of machine learning approaches has not only enabled the establishment of classification models using biological data for future differential diagnostic use, it has also facilitated multivariate models for outcome prediction following therapeutic interventions. Novel methods, such as BrainAGE, a surrogate marker of accelerated brain aging processes, have added to longitudinal studies to gain insights into the brain structural dynamics from early brain developmental alterations to progressive structural brain changes after disease onset.


Asunto(s)
Imagen por Resonancia Magnética , Esquizofrenia , Encéfalo/diagnóstico por imagen , Humanos , Aprendizaje Automático , Neuroimagen , Esquizofrenia/diagnóstico por imagen
6.
J Comput Assist Tomogr ; 44(1): 37-42, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-31939880

RESUMEN

OBJECTIVE: The purpose of this study was to determine whether computed tomography (CT) angiography with machine learning (ML) can be used to predict the rapid growth of abdominal aortic aneurysm (AAA). MATERIALS AND METHODS: This retrospective study was approved by our institutional review board. Fifty consecutive patients (45 men, 5 women, 73.5 years) with small AAA (38.5 ± 6.2 mm) had undergone CT angiography. To be included, patients required at least 2 CT scans a minimum of 6 months apart. Abdominal aortic aneurysm growth, estimated by change per year, was compared between patients with baseline infrarenal aortic minor axis. For each axial image, major axis of AAA, minor axis of AAA, major axis of lumen without intraluminal thrombi (ILT), minor axis of lumen without ILT, AAA area, lumen area without ILT, ILT area, maximum ILT area, and maximum ILT thickness were measured. We developed a prediction model using an ML method (to predict expansion >4 mm/y) and calculated the area under the receiver operating characteristic curve of this model via 10-fold cross-validation. RESULTS: The median aneurysm expansion was 3.0 mm/y. Major axis of AAA and AAA area correlated significantly with future AAA expansion (r = 0.472, 0.416 all P < 0.01). Machine learning and major axis of AAA were a strong predictor of significant AAA expansion (>4 mm/y) (area under the receiver operating characteristic curve were 0.86 and 0.78). CONCLUSIONS: Machine learning is an effective method for the prediction of expansion risk of AAA. Abdominal aortic aneurysm area and major axis of AAA are the important factors to reflect AAA expansion.


Asunto(s)
Aneurisma de la Aorta Abdominal/diagnóstico por imagen , Angiografía por Tomografía Computarizada/métodos , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Anciano , Anciano de 80 o más Años , Progresión de la Enfermedad , Femenino , Humanos , Aprendizaje Automático , Masculino , Estudios Retrospectivos
7.
Radiologe ; 60(1): 6-14, 2020 Jan.
Artículo en Alemán | MEDLINE | ID: mdl-31915840

RESUMEN

METHODICAL ISSUE: Machine learning (ML) algorithms have an increasingly relevant role in radiology tackling tasks such as the automatic detection and segmentation of diagnosis-relevant markers, the quantification of progression and response, and their prediction in individual patients. STANDARD RADIOLOGICAL METHODS: ML algorithms are relevant for all image acquisition techniques from computed tomography (CT) and magnetic resonance imaging (MRI) to ultrasound. However, different modalities result in different challenges with respect to standardization and variability. METHODICAL INNOVATIONS: ML algorithms are increasingly able to analyze longitudinal data for the training of prediction models. This is relevant since it enables the use of comprehensive information for predicting individual progression and response, and the associated support of treatment decisions by ML models. PERFORMANCE: The quality of detection and segmentation algorithms of lesions has reached an acceptable level in several areas. The accuracy of prediction models is still increasing, but is dependent on the availability of representative training data. ACHIEVEMENTS: The development of ML algorithms in radiology is progressing although many solutions are still at a validation stage. It is accompanied by a parallel and increasingly interlinked development of basic methods and techniques which will gradually be put into practice in radiology. PRACTICAL CONSIDERATIONS: Two factors will impact the relevance of ML in radiological practice: the thorough validation of algorithms and solutions, and the creation of representative diverse data for the training and validation in a realistic context.


Asunto(s)
Aprendizaje Automático , Radiología , Algoritmos , Humanos , Terminología como Asunto
8.
Eur J Med Chem ; 188: 111975, 2020 Feb 15.
Artículo en Inglés | MEDLINE | ID: mdl-31940507

RESUMEN

Local changes in the structure of G-protein coupled receptors (GPCR) binders largely affect their pharmacological profile. While the sought efficacy can be empirically obtained by introducing local modifications, the underlining structural explanation can remain elusive. Here, molecular dynamics (MD) simulations of the eticlopride-bound inactive state of the Dopamine D3 Receptor (D3DR) have been clustered using a machine learning-based approach in the attempt to rationalize the efficacy change in four congeneric modulators. Accumulating extended MD trajectories of receptor-ligand complexes, we observed how the increase in ligand flexibility progressively destabilized the crystal structure of the inactivated receptor. To prospectively validate this model, a partial agonist was rationally designed based on structural insights and computational modeling, and eventually synthesized and tested. Results turned out to be in line with the predictions. This case study suggests that the investigation of ligand flexibility in the framework of extended MD simulations can assist and inform drug design strategies, highlighting its potential role as a powerful in silico counterpart to functional assays.


Asunto(s)
Carbamatos/metabolismo , Agonistas de Dopamina/metabolismo , Antagonistas de Dopamina/metabolismo , Piperazinas/metabolismo , Receptores de Dopamina D3/metabolismo , Animales , Sitios de Unión , Células CHO , Carbamatos/química , Cricetulus , Agonistas de Dopamina/química , Antagonistas de Dopamina/química , Diseño de Drogas , Humanos , Ligandos , Aprendizaje Automático , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Piperazinas/química , Conformación Proteica , Receptores de Dopamina D3/química , Salicilamidas/metabolismo
11.
Water Res ; 171: 115454, 2020 Mar 15.
Artículo en Inglés | MEDLINE | ID: mdl-31918388

RESUMEN

The water quality prediction performance of machine learning models may be not only dependent on the models, but also dependent on the parameters in data set chosen for training the learning models. Moreover, the key water parameters should also be identified by the learning models, in order to further reduce prediction costs and improve prediction efficiency. Here we endeavored for the first time to compare the water quality prediction performance of 10 learning models (7 traditional and 3 ensemble models) using big data (33,612 observations) from the major rivers and lakes in China from 2012 to 2018, based on the precision, recall, F1-score, weighted F1-score, and explore the potential key water parameters for future model prediction. Our results showed that the bigger data could improve the performance of learning models in prediction of water quality. Compared to other 7 models, decision tree (DT), random forest (RF) and deep cascade forest (DCF) trained by data sets of pH, DO, CODMn, and NH3-N had significantly better performance in prediction of all 6 Levels of water quality recommended by Chinese government. Moreover, two key water parameter sets (DO, CODMn, and NH3-N; CODMn, and NH3-N) were identified and validated by DT, RF and DCF to be high specificities for perdition water quality. Therefore, DT, RF and DCF with selected key water parameters could be prioritized for future water quality monitoring and providing timely water quality warning.


Asunto(s)
Calidad del Agua , Agua , Macrodatos , China , Aprendizaje Automático
12.
Zhonghua Wei Chang Wai Ke Za Zhi ; 23(1): 33-37, 2020 Jan 25.
Artículo en Chino | MEDLINE | ID: mdl-31958928

RESUMEN

The rapid development of computer technologies brings us great changes in daily life and work. Artificial intelligence is a branch of computer science, which is to allow computers to exercise activities that are normally confined to intelligent life. The broad sense of artificial intelligence includes machine learning and robots. This article mainly focuses on machine learning and related medical fields, and deep learning is an artificial neural network in machine learning. Convolutional neural network (CNN) is a type of deep neural network, that is developed on the basis of deep neural network, further imitating the structure of the visual cortex of the brain and the principle of visual activity. The current machine learning method used in medical big data analysis is mainly CNN. In the next few years, it is the developing trend that artificial intelligence as a conventional tool will enter the relevant departments of medical image interpretation. In addition, this article also shares the progress of the integration of artificial intelligence and biomedicine combined with actual cases, and mainly introduces the current status of CNN application research in pathological diagnosis, imaging diagnosis and endoscopic diagnosis for gastrointestinal diseases.


Asunto(s)
Inteligencia Artificial , Enfermedades Gastrointestinales/diagnóstico , Enfermedades Gastrointestinales/terapia , Diagnóstico por Computador , Humanos , Aprendizaje Automático , Terapia Asistida por Computador
13.
Nat Commun ; 11(1): 577, 2020 Jan 29.
Artículo en Inglés | MEDLINE | ID: mdl-31996669

RESUMEN

The Bruton tyrosine kinase (BTK) inhibitor ibrutinib provides effective treatment for patients with chronic lymphocytic leukemia (CLL), despite extensive heterogeneity in this disease. To define the underlining regulatory dynamics, we analyze high-resolution time courses of ibrutinib treatment in patients with CLL, combining immune-phenotyping, single-cell transcriptome profiling, and chromatin mapping. We identify a consistent regulatory program starting with a sharp decrease of NF-κB binding in CLL cells, which is followed by reduced activity of lineage-defining transcription factors, erosion of CLL cell identity, and acquisition of a quiescence-like gene signature. We observe patient-to-patient variation in the speed of execution of this program, which we exploit to predict patient-specific dynamics in the response to ibrutinib based on the pre-treatment patient samples. In aggregate, our study describes time-dependent cellular, molecular, and regulatory effects for therapeutic inhibition of B cell receptor signaling in CLL, and it establishes a broadly applicable method for epigenome/transcriptome-based treatment monitoring.


Asunto(s)
Agammaglobulinemia Tirosina Quinasa/efectos de los fármacos , Cromatina/genética , Leucemia Linfocítica Crónica de Células B/tratamiento farmacológico , Pirazoles/antagonistas & inhibidores , Pirazoles/metabolismo , Pirazoles/uso terapéutico , Pirimidinas/antagonistas & inhibidores , Pirimidinas/metabolismo , Pirimidinas/uso terapéutico , Epigenómica , Perfilación de la Expresión Génica , Heterogeneidad Genética/efectos de los fármacos , Humanos , Leucemia Linfocítica Crónica de Células B/inmunología , Aprendizaje Automático , Receptores de Antígenos de Linfocitos B/efectos de los fármacos , Análisis de Secuencia de ARN , Factores de Transcripción , Transcriptoma
14.
Nature ; 577(7790): 320-321, 2020 01.
Artículo en Inglés | MEDLINE | ID: mdl-31942058
15.
Ambio ; 49(2): 475-486, 2020 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-31073983

RESUMEN

Comparisons between field data and available maps show that 64% of wet areas in the boreal landscape are missing on current maps. Primarily forested wetlands and wet soils near streams and lakes are missing, making them difficult to manage. One solution is to model missing wet areas from high-resolution digital elevation models, using indices such as topographical wetness index and depth to water. However, when working across large areas with gradients in topography, soils and climate, it is not possible to find one method or one threshold that works everywhere. By using soil moisture data from the National Forest Inventory of Sweden as a training dataset, we show that it is possible to combine information from several indices and thresholds, using machine learners, thereby improving the mapping of wet soils (kappa = 0.65). The new maps can be used to better plan roads and generate riparian buffer zones near surface waters.


Asunto(s)
Bosques , Taiga , Aprendizaje Automático , Suelo , Suecia
17.
J Sports Sci ; 38(1): 106-113, 2020 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-31658883

RESUMEN

The purpose of this study was to develop an automated method for identifying and classifying change of direction (COD) movements in professional tennis using tracking data. Three sport science and strength and conditioning experts coded match-play footage of nineteen professional tennis players (9 male and 10 female) from the Australian Open Grand Slam for COD of medium and high intensity. A total of 1,494 changes were identified and aligned with 2D player position sampled at 25 Hz based on camera tracking data. Several machine learning classifiers were trained and tested on a set of 1,128 time-motion features. A random forest algorithm was found to have the best out-of-sample performance, classifying medium and high intensity changes with an F1-score of 0.729. This research offers a novel and applicable way for utilising player tracking data and machine learning techniques to automatically identify and classify COD movements in professional tennis.


Asunto(s)
Aprendizaje Automático , Destreza Motora/clasificación , Tenis/fisiología , Adulto , Fenómenos Biomecánicos , Conducta Competitiva/clasificación , Conducta Competitiva/fisiología , Estudios Transversales , Femenino , Humanos , Masculino , Destreza Motora/fisiología , Reproducibilidad de los Resultados , Estudios de Tiempo y Movimiento , Adulto Joven
18.
J Sports Sci ; 38(2): 150-158, 2020 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-31699000

RESUMEN

Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC) and movement behaviours in pre-school children using novel analytics. One-hundred and twenty-five children (4.3 ± 0.5y, 1.04 ± 0.05 m, 17.8 ± 3.2 kg, BMI: 16.2 ± 1.9 kg.m2) took part in this study. Measures included accelerometer-derived 24-h activity, MC (Movement Assessment Battery for Children second edition), height, weight and waist circumference, from which zBMI were derived. Self-Organised Map (SOM) analysis was used to classify participants' profiles and a k-means cluster analysis was used to classify the neurons into larger groups according to the input variables. These clusters were used to describe the individuals' characteristics according to their MC and PA compositions. The SOM analysis indicated five profiles according to MC and PA. One cluster was identified as having both the lowest MC and MVPA (profile 2), whilst profiles 4 and 5 show moderate-high values of PA and MC. We present a novel pathway to profiling complex tenets of human movement and behaviour, which has never previously been implemented in pre-school children, highlighting that the focus should change from obesity monitoring, to "moving well".Abbreviations: MC: Motor competence; PA: Physical activity; MVPA: Moderate-to-vigorous physical activity; SOM: Self-organized map; BMI: Body mass index; MABC2: Movement assessment battery for children 2nd edition; MANOVA: Multiple analysis of variance.


Asunto(s)
Ejercicio/fisiología , Aprendizaje Automático , Actividad Motora/fisiología , Acelerometría/instrumentación , Índice de Masa Corporal , Preescolar , Estudios Transversales , Femenino , Monitores de Ejercicio , Humanos , Masculino , Movimiento/fisiología
20.
J Forensic Sci ; 65(1): 266-273, 2020 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-31424577

RESUMEN

Colombian forensic investigators required assistance locating clandestine burials of missing persons related to human right atrocities from 14 years ago. Geoscientific search methods were trialled, including a predictive spatial statistical model, using various input and database information, to select the most likely grave locations in difficult mountainous terrain. Groundwork using forensic geomorphology, near-surface geophysics (ERT) and subsequent probing identified suspect burial positions. One site was in mountainous terrain and the other in former school grounds, both difficult to access and in poor weather conditions. In the mountainous area, a negative resistivity anomaly area was identified and intrusively investigated, found to be a buried rock. In school grounds, after MESP and intelligence were used to identify a burial site, surface depressions were identified, and ERT datasets collected over the highest priority depression; intrusive investigations discovered a hand-dug pit containing animal bones. This approach is suggested for Latin American searches.


Asunto(s)
Entierro , Colombia , Impedancia Eléctrica , Ciencias Forenses/métodos , Sistemas de Información Geográfica , Humanos , Aprendizaje Automático , Modelos Estadísticos , Programas Informáticos
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