Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 20 de 408
Filter
1.
Aval. psicol ; 20(1): 100-110, jan.-mar. 2021. ilus, tab
Article in Portuguese | LILACS, INDEXPSI | ID: biblio-1249049

ABSTRACT

Funções executivas (FE) são habilidades que permitem o autocontrole comportamental e cognitivo e estão relacionadas a diversos desfechos ao longo da vida. O uso de testes informatizados para avaliar as FE pode facilitar a precisão dos registros, a padronização e a análise dos dados. Este estudo objetivou desenvolver um instrumento informatizado para avaliar FE em crianças de 4 a 10 anos e analisar características psicométricas. Foram conduzidas cinco etapas: 1) Definição teórica e metodológica; 2) Construção dos itens; 3) Estudo piloto; 4) Análise de juízes; e 5) Estudos psicométricos de validade e fidedignidade. As tarefas informatizadas mostraram-se adequadas para o público-alvo, conforme avaliação dos juízes. As diferentes tarefas de memória de trabalho, inibição e flexibilidade cognitiva apresentaram correlações significativas entre si e a maioria das medidas no teste-reteste evidenciou estabilidade na mensuração. Portanto, os resultados sugerem viabilidade para uso do instrumento no contexto brasileiro. (AU)


Executive functions (EF) are skills linked to behavioral and cognitive self-control and are related to various outcomes throughout life. The use of computerized tests to evaluate EFs can facilitate the accuracy of records, standardization and data analysis. This study aimed to develop a computerized instrument for the EF assessment of children aged 4 to 10 years, and to seek psychometric evidence. Five steps were carried out: 1) Theoretical and methodological definition; 2) Construction of the items; 3) Pilot study; 4) Analysis of experts; and 5) Psychometric studies of validity and reliability. The computerized tasks proved to be suitable for the target audience according to the expert's evaluation. The results between the different tasks of working memory, inhibition and cognitive flexibility showed significant correlations and most test-retest measures showed stability in the measurement. Therefore, the results indicate the feasibility of using the instrument in the Brazilian context. (AU)


Funciones ejecutivas (FE) son habilidades que permiten el autocontrol conductual y cognitivo y están relacionadas con diversos resultados a lo largo de la vida. El uso de tests informatizados para evaluar las FE puede facilitar la precisión de los registros, la estandarización y el análisis de datos. Este estudio tuvo como objetivo desarrollar un instrumento informatizado para la FE para niños de 4 a 10 años, y analizar evidencias psicométricas. Fueron ejecutados cinco pasos: 1) Definición teórica y metodológica; 2) Construcción de los ítems; 3) Estudio piloto; 4) Análisis de jueces; y 5) Estudios psicométricos de validez y fiabilidad. Las tareas informatizadas demostraron ser adecuadas para el público objetivo según la evaluación de los jueces. Las diferentes tareas de memoria de trabajo, inhibición y flexibilidad cognitiva mostraron correlaciones significativas entre sí y la mayoría de las medidas test-retest presentaron estabilidad en la medición. Por lo tanto, los resultados sugieren la viabilidad del instrumento para el contexto brasileño. AU)


Subject(s)
Humans , Child, Preschool , Child , Diagnosis, Computer-Assisted/psychology , Executive Function , Pilot Projects , Reproducibility of Results
2.
Journal of Biomedical Engineering ; (6): 1054-1061, 2021.
Article in Chinese | WPRIM | ID: wpr-921845

ABSTRACT

Otitis media is one of the common ear diseases, and its accurate diagnosis can prevent the deterioration of conductive hearing loss and avoid the overuse of antibiotics. At present, the diagnosis of otitis media mainly relies on the doctor's visual inspection based on the images fed back by the otoscope equipment. Due to the quality of otoscope equipment pictures and the doctor's diagnosis experience, this subjective examination has a relatively high rate of misdiagnosis. In response to this problem, this paper proposes the use of faster region convolutional neural networks to analyze clinically collected digital otoscope pictures. First, through image data enhancement and preprocessing, the number of samples in the clinical otoscope dataset was expanded. Then, according to the characteristics of the otoscope picture, the convolutional neural network was selected for feature extraction, and the feature pyramid network was added for multi-scale feature extraction to enhance the detection ability. Finally, a faster region convolutional neural network with anchor size optimization and hyperparameter adjustment was used for identification, and the effectiveness of the method was tested through a randomly selected test set. The results showed that the overall recognition accuracy of otoscope pictures in the test samples reached 91.43%. The above studies show that the proposed method effectively improves the accuracy of otoscope picture classification, and is expected to assist clinical diagnosis.


Subject(s)
Computers , Diagnosis, Computer-Assisted , Humans , Neural Networks, Computer , Otitis Media/diagnosis
3.
Article in Chinese | WPRIM | ID: wpr-879246

ABSTRACT

Both feature representation and classifier performance are important factors that determine the performance of computer-aided diagnosis (CAD) systems. In order to improve the performance of ultrasound-based CAD for breast cancers, a novel multiple empirical kernel mapping (MEKM) exclusivity regularized machine (ERM) ensemble classifier algorithm based on self-paced learning (SPL) is proposed, which simultaneously promotes the performance of both feature representation and the classifier. The proposed algorithm first generates multiple groups of features by MEKM to enhance the ability of feature representation, which also work as the kernel transform in multiple support vector machines embedded in ERM. The SPL strategy is then adopted to adaptively select samples from easy to hard so as to gradually train the ERM classifier model with improved performance. This algorithm is verified on a B-mode ultrasound dataset and an elastography ultrasound dataset, respectively. The results show that the classification accuracy, sensitivity and specificity on B-mode ultrasound are (86.36±6.45)%, (88.15±7.12)%, and (84.52±9.38)%, respectively, and the classification accuracy, sensitivity and specificity on elastography ultrasound are (85.97±3.75)%, (85.93±6.09)%, and (86.03±5.88)%, respectively. It indicates that the proposed algorithm can effectively improve the performance of ultrasound-based CAD for breast cancers with the potential for application.


Subject(s)
Algorithms , Breast Neoplasms/diagnostic imaging , Computers , Diagnosis, Computer-Assisted , Humans , Support Vector Machine , Ultrasonography
5.
Rev. cuba. invest. bioméd ; 39(2): e445, abr.-jun. 2020. tab, graf
Article in Spanish | LILACS, CUMED | ID: biblio-1126603

ABSTRACT

Introducción: el nódulo pulmonar solitario es uno de los problemas más frecuentes en la práctica del radiólogo, que constituye un hallazgo incidental habitual en los estudios torácicos realizados durante el ejercicio clínico diario. Objetivo: implementar un sistema de diagnóstico asistido por computadora que facilite la detección del nódulo pulmonar solitario en las series de imágenes de tomografía computarizada multicorte. Métodos: se utilizó Matlab para el desarrollo y evaluación de un conjunto de algoritmos que constituyen elementos necesarios de un sistema de diagnóstico asistido por computadora. En orden: un algoritmo para la extracción de las regiones de interés, algoritmo para la extracción de características y un algoritmo de detección de nódulo pulmonar solitario para el cual se probaron varios clasificadores. La evaluación de los algoritmos fue efectuada en base a las anotaciones realizada por especialistas a la colección de imágenes LIDC-IDRI (Lung Image Database Consortium). Resultados: el método de segmentación empleado para extracción de las regiones de interés permitió generar la adecuada división de las imágenes originales en regiones significativas. El algoritmo utilizado en la detección mostró para el conjunto de prueba además de buena exactitud (de 96,4 por ciento), un buen balance de sensibilidad (91,5 por ciento) para una tasa de 0,84 falsos positivos por imagen. Conclusiones: el trabajo de investigación y la implementación realizada se reflejan en la construcción de una interfaz gráfica en Matlab como prototipo del sistema de diagnóstico asistido por computadora, con el que se puede contribuir a detectar más fácilmente el NPS(AU)


Introduction: solitary pulmonary nodules are one of the most frequent problems in radiographic practice. They are a common incidental finding in chest studies conducted during routine clinical work. Objective: implement a computer-assisted diagnostic system facilitating detection of solitary pulmonary nodules in multicut computerized tomography image series. Methods: Matlab was used to develop and evaluate a set of algorithms constituting necessary components of a computer-assisted diagnostic system. The order was the following: an algorithm to extract regions of interest, another to extract characteristics, and another to detect solitary pulmonary nodules, for which several classifiers were tested. Evaluation of the algorithms was based on notes taken by specialists on the LIDC-IDRI (Lung Image Database Consortium) image collection. Results: the segmentation method used for extraction of regions of interest made it possible to create a suitable division of the original images into significant regions. The algorithm used for detection found that the test set exhibited good accuracy (96.4%), a good sensitivity balance (91.5%), and a 0.84 rate of false positives per image. Conclusions: the research and implementation work done is reflected in the construction of a Matlab graphic interface serving as a prototype for a computer-assisted diagnostic system which may facilitate detection of SPNs.


Subject(s)
Humans , Tomography, X-Ray Computed/methods , Diagnosis, Computer-Assisted/methods , Solitary Pulmonary Nodule/diagnostic imaging , Algorithms
6.
Article in Chinese | WPRIM | ID: wpr-880393

ABSTRACT

A clinical information navigation system based on 3D human body model is designed. The system extracts the key information of diagnosis and treatment of patients by searching the historical medical records, and stores the focus information in a predefined structured patient instance. In addition, the rule mapping is established between the patient instance and the three-dimensional human body model, the focus information is visualized on the three-dimensional human body model, and the trend curve can be drawn according to the change of the focus, meanwhile, the key diagnosis and treatment information and the original report reference function are provided. The system can support the analysis, storage and visualization of various types of reports, improve the efficiency of doctors' retrieval of patient information, and reduce the treatment time.


Subject(s)
Diagnosis, Computer-Assisted , Humans , Medical Informatics Applications , Models, Anatomic , Software
7.
Article in Chinese | WPRIM | ID: wpr-828175

ABSTRACT

Recently, artificial intelligence (AI) has been widely applied in the diagnosis and treatment of urinary diseases with the development of data storage, image processing, pattern recognition and machine learning technologies. Based on the massive biomedical big data of imaging and histopathology, many urinary system diseases (such as urinary tumor, urological calculi, urinary infection, voiding dysfunction and erectile dysfunction) will be diagnosed more accurately and will be treated more individualizedly. However, most of the current AI diagnosis and treatment are in the pre-clinical research stage, and there are still some difficulties in the wide application of AI. This review mainly summarizes the recent advances of AI in the diagnosis of prostate cancer, bladder cancer, kidney cancer, urological calculi, frequent micturition and erectile dysfunction, and discusses the future potential and existing problems.


Subject(s)
Artificial Intelligence , Diagnosis, Computer-Assisted , Humans , Image Processing, Computer-Assisted , Urologic Diseases , Diagnosis
8.
Journal of Biomedical Engineering ; (6): 1037-1044, 2020.
Article in Chinese | WPRIM | ID: wpr-879234

ABSTRACT

To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (


Subject(s)
Adolescent , Brain/diagnostic imaging , Diagnosis, Computer-Assisted , Electroencephalography , Female , Humans , Support Vector Machine
9.
Einstein (Säo Paulo) ; 18: eAO4948, 2020. tab, graf
Article in English | LILACS | ID: biblio-1090075

ABSTRACT

ABSTRACT Objective To develop a computational algorithm applied to magnetic resonance imaging for automatic segmentation of brain tumors. Methods A total of 130 magnetic resonance images were used in the T1c, T2 and FSPRG T1C sequences and in the axial, sagittal and coronal planes of patients with brain cancer. The algorithms employed contrast correction, histogram normalization and binarization techniques to disconnect adjacent structures from the brain and enhance the region of interest. Automatic segmentation was performed through detection by coordinates and arithmetic mean of the area. Morphological operators were used to eliminate undesirable elements and reconstruct the shape and texture of the tumor. The results were compared with manual segmentations by two radiologists to determine the efficacy of the algorithms implemented. Results The correlated correspondence between the segmentation obtained and the gold standard was 89.23%. Conclusion It is possible to locate and define the tumor region automatically with no the need for user interaction, based on two innovative methods to detect brain extreme sites and exclude non-tumor tissues on magnetic resonance images.


RESUMO Objetivo Desenvolver um algoritmo computacional aplicado a imagens de ressonância magnética, para segmentação automática de tumores cerebrais. Métodos Foram utilizadas 130 imagens de ressonância magnética nas sequências T1c, T2 e FSPRG T1c e nos planos axial, sagital e coronal de pacientes acometidos com câncer cerebral. Os algoritmos empregaram técnicas de correção de contraste, normalização de histograma e binarização, para desconectar estruturas adjacentes do cérebro e realçar a região de interesse. A segmentação automática foi realizada por meio da detecção por coordenadas e por média aritmética da área. Operadores morfológicos foram utilizados para eliminar elementos indesejáveis e reconstruir a forma e a textura do tumor. Os resultados foram comparados com as segmentações manuais de dois médicos radiologistas, para determinar a eficácia dos algoritmos implementados. Resultados Os acertos foram de 89,23% na correspondência entre a segmentação obtida e o padrão-ouro. Conclusão É possível localizar e delimitar a região tumoral de forma automática, sem necessidade de interação com o usuário baseado em dois métodos inovadores de detecção dos extremos do cérebro e de exclusão dos tecidos não tumorais em imagens de ressonância magnética.


Subject(s)
Humans , Algorithms , Image Processing, Computer-Assisted/methods , Brain Neoplasms/diagnostic imaging , Magnetic Resonance Imaging/methods , Reference Standards , Brain , Reproducibility of Results , Diagnosis, Computer-Assisted/methods
10.
Adv Rheumatol ; 60: 25, 2020. tab, graf
Article in English | LILACS | ID: biblio-1130789

ABSTRACT

Abstract Background: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologists remains subject to significant intrapersonal and interpersonal variation. This encouraged us to use machine-learning methods for this task. Methods: In this retrospective study including 56 sacroiliac joint MRI exams, 24 patients had positive and 32 had negative findings for inflammatory sacroiliitis according to the ASAS group criteria. The dataset was randomly split with ∼ 80% (46 samples, 20 positive and 26 negative) as training and ∼ 20% as external test (10 samples, 4 positive and 6 negative). After manual segmentation of the images by a musculoskeletal radiologist, multiple features were extracted. The classifiers used were the Support Vector Machine, the Multilayer Perceptron (MLP), and the Instance-Based Algorithm, combined with the Relief and Wrapper methods for feature selection. Results: Based on 10-fold cross-validation using the training dataset, the MLP classifier obtained the best performance with sensitivity = 100%, specificity = 95.6% and accuracy = 84.7%, using 6 features selected by the Wrapper method. Using the test dataset (external validation) the same MLP classifier obtained sensitivity = 100%, specificity = 66.7% and accuracy = 80%. Conclusions: Our results show the potential of machine learning methods to identify SIJ subchondral bone marrow edema in axSpA patients and are promising to aid in the detection of active inflammatory sacroiliitis on MRI STIR sequences. Multilayer Perceptron (MLP) achieved the best results.(AU)


Subject(s)
Humans , Magnetic Resonance Imaging/instrumentation , Sacroiliitis/diagnostic imaging , Machine Learning , Artificial Intelligence , Retrospective Studies , Diagnosis, Computer-Assisted/instrumentation
11.
Rev. bras. oftalmol ; 78(4): 242-245, July-Aug. 2019.
Article in English | LILACS | ID: biblio-1013681

ABSTRACT

ABSTRACT Objective: The goal of the study is to analyze the color vision acuity pattern in undergraduates of health courses and to discuss the impact of these diseases in this population. Color deficiencies interfere significantly in the daily routine of professionals in the health area who need to discern different color hues in several situations of their everyday practice. Methods: Sixty-four volunteers, undergraduates of health courses of the Federal University of Alfenas (UNIFAL-MG), participated in the study. One man was excluded because he did not fit the inclusion criteria. Two groups were analyzed according to sex with the Farnsworth Munsell 100-Hue test. Results: There were no significant differences between the eyes and between the groups analyzed. The color vision acuity pattern is between 35 and 40, according to the Total Error Score. The gender issue does not influence the general pattern of the color vision acuity of the health courses undergraduates when those with color vision disorders are removed. Conclusion: Screenings and guidance should be given to undergraduates of health courses so that, aware of their condition of presenting some type of color disorder, they shall make the appropriate decision on which career to follow so that such limitation does not interfere with the quality of their daily life.


RESUMO Objetivo: O objetivo do estudo é analisar a acuidade visual média para cores de estudantes da área de saúde e discutir o impacto das doenças que a afetam nessa população. Deficiências cromáticas interferem de forma significativa no dia a dia de profissionais da área da saúde que necessitam de discernir diferentes matizes em diversas situações de sua prática profissional. Métodos: Participaram da pesquisa 64 voluntários, estudantes de cursos da área de saúde da Universidade Federal de Alfenas, sendo que 1 homem foi excluído por não se adequar aos critérios de inclusão. Dois grupos foram analisados, de acordo com o sexo, com o teste de Farnsworth Munsell 100-Hue. Resultados: Não houve diferenças significativas entre os olhos e entre os grupos analisados. O padrão de visão de cores encontra-se entre 35 e 40, de acordo com a Pontuação do Erro Total. A questão de gênero não influencia no padrão geral da qualidade de visão de cores de estudantes da área de saúde, quando retirados aqueles que apresentam distúrbios da visão cromática. Conclusão: Devem ser realizadas triagens e orientação para estudantes de cursos da área de saúde para que, cientes da sua condição de apresentar algum tipo de distúrbio cromático, possam tomar a decisão adequada sobre qual carreira seguir para que tal limitação não interfira na qualidade de sua vida diária.


Subject(s)
Humans , Male , Female , Students, Health Occupations , Color Vision Defects/diagnosis , Color Vision Defects/epidemiology , Health Personnel , Color Perception Tests/methods , Professional Competence , Quality of Life , Schools, Health Occupations , Visual Acuity , Vision Screening , Color Vision Defects/psychology , Diagnosis, Computer-Assisted/methods , Color Perception/physiology , Color Vision/physiology
12.
Braz. arch. biol. technol ; 62: e19170821, 2019. tab, graf
Article in English | LILACS | ID: biblio-1055410

ABSTRACT

Abstract: Thyroid nodules are cell growths in the thyroid which might be for in one of two categories benign or malignant. Nodular thyroid disease is common and because of the associated risk of malignancy and hyper-function; these nodules have to be examined thoroughly. Hence diagnosing thyroid nodule malignancy in the early stage can mitigate the possibility of death. This paper presents an intelligent thyroid nodules malignancy diagnosis using texture information in run-length matrix derived from 2- level 2D wavelet transform bands (approximation and details). In this work, ANOVA test has been used to for feature selection to reduce for feature selection about 45 run-length features with and without wavelet generated, before feeding those features which clinical importance to the Support Vector Machine(SVM) and Decision Tree (DT) classifier to perform the automated diagnosis. The validation of this work is activated using 100-thyroid nodule images spliced equally between the two categories (50 Benign and 50 Malignant). The proposed system can detect thyroid nodules malignancy with an average accuracy of about 97% using SVM classifier for the run- length matrix, features derived from spatial domain while the average accuracy is increased to 98% in case of hybrid feature derived from spatial domain and 2-level wavelet decomposition. For the other proposed classifier (DT), the average accuracy in case of spatial domain based features is 93% whereas the average accuracy of the hybrid features system is 97%. Hence the proposed system can be used for the screening of thyroid nodules.


Subject(s)
Diagnosis, Computer-Assisted/instrumentation , Thyroid Nodule/diagnostic imaging , Mass Screening , Analysis of Variance
13.
Clinics ; 74: e908, 2019. tab, graf
Article in English | LILACS | ID: biblio-1011907

ABSTRACT

OBJECTIVES: Approximately one-third of candidates for epilepsy surgery have no visible abnormalities on conventional magnetic resonance imaging. This is extremely discouraging, as these patients have a less favorable prognosis. We aimed to evaluate the utility of quantitative magnetic resonance imaging in patients with drug-resistant neocortical focal epilepsy and negative imaging. METHODS: A prospective study including 46 patients evaluated through individualized postprocessing of five quantitative measures: cortical thickness, white and gray matter junction signal, relaxation rate, magnetization transfer ratio, and mean diffusivity. Scalp video-electroencephalography was used to suggest the epileptogenic zone. A volumetric fluid-attenuated inversion recovery sequence was performed to aid visual inspection. A critical assessment of follow-up was also conducted throughout the study. RESULTS: In the subgroup classified as having an epileptogenic zone, individualized postprocessing detected abnormalities within the region of electroclinical origin in 9.7% to 31.0% of patients. Abnormalities outside the epileptogenic zone were more frequent, up to 51.7%. In five patients initially included with negative imaging, an epileptogenic structural abnormality was identified when a new visual magnetic resonance imaging inspection was guided by information gleaned from postprocessing. In three patients, epileptogenic lesions were detected after visual evaluation with volumetric fluid-attenuated sequence guided by video electroencephalography. CONCLUSION: Although quantitative magnetic resonance imaging analyses may suggest hidden structural lesions, caution is warranted because of the apparent low specificity of these findings for the epileptogenic zone. Conversely, these methods can be used to prevent visible lesions from being ignored, even in referral centers. In parallel, we need to highlight the positive contribution of the volumetric fluid-attenuated sequence.


Subject(s)
Humans , Male , Female , Adult , Middle Aged , Young Adult , Magnetic Resonance Imaging/methods , Drug Resistant Epilepsy/diagnostic imaging , Brain Mapping , Prospective Studies , Diagnosis, Computer-Assisted/methods , Electroencephalography/methods , Multimodal Imaging
14.
Braz. arch. biol. technol ; 62: e19180486, 2019. tab, graf
Article in English | LILACS | ID: biblio-1055380

ABSTRACT

Abstract Breast cancer is the most commonly witnessed cancer amongst women around the world. Computer aided diagnosis (CAD) have been playing a significant role in early detection of breast tumors hence to curb the overall mortality rate. This work presents an enhanced empirical study of impact of dominance-based filtering approach on performances of various state-of-the-art classifiers. The feature dominance level is proportional to the difference in means of benign and malignant tumors. The experiments were done on original Wisconsin Breast Cancer Dataset (WBCD) with total nine features. It is found that the classifiers' performances for top 4 and top 5 dominant-based features are almost equivalent to performances for all nine features. Artificial neural network (ANN) is come forth as the best performing classifier among all with accuracies of 98.9% and 99.6% for top 4 and top 5 dominant features respectively. The error rate of ANN between all nine and top 4 &5 dominant features is less than 2% for four performance evaluation parameters namely sensitivity, specificity, accuracy and AUC. Thus, it can be stated that the dominance-based filtering approach is appropriate for selecting a sound set of features from the feature pool, consequently, helps to reduce computation time with no deterioration in classifier's performance.


Subject(s)
Breast Neoplasms/diagnostic imaging , Diagnosis, Computer-Assisted/instrumentation , Machine Learning , Neural Networks, Computer
15.
Gut and Liver ; : 388-393, 2019.
Article in English | WPRIM | ID: wpr-763862

ABSTRACT

Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice is expected in the near future. Medical image-based diagnoses, such as pathology, radiology, and endoscopy, are expected to be the first in the medical field to be affected by artificial intelligence. A convolutional neural network, a kind of deep-learning method with multilayer perceptrons designed to use minimal preprocessing, was recently reported as being highly beneficial in the field of endoscopy, including esophagogastroduodenoscopy, colonoscopy, and capsule endoscopy. A convolutional neural network-based diagnostic program was challenged to recognize anatomical locations in esophagogastroduodenoscopy images, Helicobacter pylori infection, and gastric cancer for esophagogastroduodenoscopy; to detect and classify colorectal polyps; to recognize celiac disease and hookworm; and to perform small intestine motility characterization of capsule endoscopy images. Artificial intelligence is expected to help endoscopists provide a more accurate diagnosis by automatically detecting and classifying lesions; therefore, it is essential that endoscopists focus on this novel technology. In this review, we describe the effects of artificial intelligence on gastroenterology with a special focus on automatic diagnosis, based on endoscopic findings.


Subject(s)
Ancylostomatoidea , Artificial Intelligence , Capsule Endoscopy , Celiac Disease , Colonoscopy , Diagnosis , Diagnosis, Computer-Assisted , Endoscopy , Endoscopy, Digestive System , Endoscopy, Gastrointestinal , Gastroenterology , Helicobacter pylori , Humans , Intestine, Small , Learning , Methods , Neural Networks, Computer , Pathology , Polyps , Stomach Neoplasms
16.
Article in Chinese | WPRIM | ID: wpr-772485

ABSTRACT

Based on the developing situation of Computer Aided Diagnosis/Detection (CAD) software, considering the domestic and international regulation of CAD software, according to current Medical Device Classification Catalog and related laws of China Food and Drug Administration (CFDA), this paper investigated and analyzed the classification of CAD software, and provided technical suggestion on classifying principle of CAD software applying Artificial Intelligence (AI) or other advanced technology from medical device regulation scope, for the reference of regulatory and technical departments.


Subject(s)
Artificial Intelligence , China , Diagnosis, Computer-Assisted , Radiographic Image Interpretation, Computer-Assisted , Software
17.
Article in Chinese | WPRIM | ID: wpr-772116

ABSTRACT

OBJECTIVE@#To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography.@*METHODS@#The data of full-filed digital mammography in both craniocaudal view and mediolateral oblique view from 106 patients with breast neoplasms were analyzed. Twenty-three handcrafted features (HCF) were extracted from the images of the breast tumors and a suitable feature set of HCF was selected using -test. The deep features (DF) were extracted from the 3 pre-trained deep learning models, namely AlexNet, VGG16 and GoogLeNet. With abundant breast tumor information from the craniocaudal view and mediolateral oblique view, we combined the two extracted features (DF and HCF) as the two-view features. A multi-classifier model was finally constructed based on the combined HCF and DF sets. The classification ability of different deep learning networks was evaluated.@*RESULTS@#Quantitative evaluation results showed that the proposed HCF+DF model outperformed HCF model, and AlexNet produced the best performances among the 3 deep learning models.@*CONCLUSIONS@#The proposed model that combines DF and HCF sets of breast tumors can effectively distinguish benign and malignant breast lesions on full-filed digital mammography.


Subject(s)
Breast Neoplasms , Classification , Diagnostic Imaging , Deep Learning , Diagnosis, Computer-Assisted , Methods , Female , Humans , Mammography , Methods
18.
Neuroscience Bulletin ; (6): 679-690, 2018.
Article in English | WPRIM | ID: wpr-775505

ABSTRACT

Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised (CRS-R). Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in patients with disorders of consciousness (DOCs). Brain-computer interface (BCI) can be used to improve clinical assessment because it directly detects the brain response to an external stimulus in the absence of behavioral expression. In this study, we designed a BCI system to assist the visual fixation assessment of DOC patients. The results from 15 patients indicated that three showed visual fixation in both CRS-R and BCI assessments and one did not show such behavior in the CRS-R assessment but achieved significant online accuracy in the BCI assessment. The results revealed that electroencephalography-based BCI can detect the brain response for visual fixation. Therefore, the proposed BCI may provide a promising method for assisting behavioral assessment using the CRS-R.


Subject(s)
Adolescent , Adult , Aged , Brain , Brain-Computer Interfaces , Consciousness Disorders , Diagnosis , Diagnosis, Computer-Assisted , Methods , Electroencephalography , Methods , Evoked Potentials , Female , Fixation, Ocular , Physiology , Humans , Male , Middle Aged , Neurologic Examination , Pilot Projects , Severity of Illness Index , User-Computer Interface
19.
Biomedical Engineering Letters ; (4): 321-327, 2018.
Article in English | WPRIM | ID: wpr-716354

ABSTRACT

In the field of computational histopathology, computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis for various diseases and help precision medicine. Therefore, many studies on automatic analysis methods for digital pathology images have been reported. In this work, we discuss an automatic feature extraction and disease stage classification method for glioblastoma multiforme (GBM) histopathological images. In this paper, we use deep convolutional neural networks (Deep CNNs) to acquire feature descriptors and a classification scheme simultaneously. Further, comparisons with other popular CNNs objectively as well as quantitatively in this challenging classification problem is undertaken. The experiments using Glioma images from The Cancer Genome Atlas shows that we obtain 96:5% average classification accuracy for our network and for higher cross validation folds other networks perform similarly with a higher accuracy of 98:0%. Deep CNNs could extract significant features from the GBM histopathology images with high accuracy. Overall, the disease stage classification of GBM from histopathological images with deep CNNs is very promising and with the availability of large scale histopathological image data the deep CNNs are well suited in tackling this challenging problem.


Subject(s)
Classification , Diagnosis , Diagnosis, Computer-Assisted , Genome , Glioblastoma , Glioma , Methods , Pathology , Precision Medicine , Subject Headings
20.
Ultrasonography ; : 217-225, 2018.
Article in English | WPRIM | ID: wpr-731144

ABSTRACT

PURPOSE: The purpose of this study was to evaluate the usefulness of applying computer-aided diagnosis (CAD) to breast ultrasound (US), depending on the reader's experience with breast imaging. METHODS: Between October 2015 and January 2016, two experienced readers obtained and analyzed the grayscale US images of 200 cases according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and categories. They additionally applied CAD (S-Detect) to analyze the lesions and made a diagnostic decision subjectively, based on grayscale US with CAD. For the same cases, two inexperienced readers analyzed the grayscale US images using the BI-RADS lexicon and categories, added CAD, and came to a subjective diagnostic conclusion. We then compared the diagnostic performance depending on the reader's experience with breast imaging. RESULTS: The sensitivity values for the experienced readers, inexperienced readers, and CAD (for experienced and inexperienced readers) were 91.7%, 75.0%, 75.0%, and 66.7%, respectively. The specificity values for the experienced readers, inexperienced readers, and CAD (for experienced and inexperienced readers) were 76.6%, 71.8%, 78.2%, and 76.1%, respectively. When diagnoses were made subjectively in combination with CAD, the specificity significantly improved (76.6% to 80.3%) without a change in the sensitivity (91.7%) in the experienced readers. After subjective combination with CAD, both of the sensitivity and specificity improved in the inexperienced readers (75.0% to 83.3% and 71.8% to 77.1%). In addition, the area under the curve improved for both the experienced and inexperienced readers (0.84 to 0.86 and 0.73 to 0.80) after the addition of CAD. CONCLUSION: CAD is more useful for less experienced readers. Combining CAD with breast US led to improved specificity for both experienced and inexperienced readers.


Subject(s)
Breast Neoplasms , Breast , Diagnosis , Diagnosis, Computer-Assisted , Information Systems , Sensitivity and Specificity , Ultrasonography
SELECTION OF CITATIONS
SEARCH DETAIL