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1.
Medicine (Baltimore) ; 99(8): e19123, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-32080088

RESUMO

World Health Organization tumor classifications of the central nervous system differentiate glioblastoma multiforme (GBM) into wild-type (WT) and mutant isocitrate dehydrogenase (IDH) genotypes. This study proposes a noninvasive computer-aided diagnosis to interpret the status of IDH in glioblastomas from transformed magnetic resonance imaging patterns. The collected image database was composed of 32 WT and 7 mutant IDH cases. For each image, a ranklet transformation which changed the original pixel values into relative coefficients was 1st applied to reduce the effects of different scanning parameters and machines on the underlying patterns. Extracting various textural features from the transformed ranklet images and combining them in a logistic regression classifier allowed an IDH prediction. We achieved an accuracy of 90%, a sensitivity of 57%, and a specificity of 97%. Four of the selected textural features in the classifier (homogeneity, difference entropy, information measure of correlation, and inverse difference normalized) were significant (P < .05), and the other 2 were close to being significant (P = .06). The proposed computer-aided diagnosis system based on radiomic textural features from ranklet-transformed images using relative rankings of pixel values as intensity-invariant coefficients is a promising noninvasive solution to provide recommendations about the IDH status in GBM across different healthcare institutions.


Assuntos
Neoplasias Encefálicas/genética , Diagnóstico por Computador/métodos , Glioblastoma/genética , Isocitrato Desidrogenase/genética , Adulto , Idoso , Algoritmos , Neoplasias Encefálicas/diagnóstico por imagem , Feminino , Genótipo , Glioblastoma/diagnóstico por imagem , Humanos , Imagem por Ressonância Magnética/métodos , Masculino , Pessoa de Meia-Idade , Mutação , Valor Preditivo dos Testes , Período Pré-Operatório , Sensibilidade e Especificidade
2.
Medicine (Baltimore) ; 99(8): e19218, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-32080115

RESUMO

To develop a classification model for accurately discriminating common infectious diseases in Zhejiang province, China.Symptoms and signs, abnormal lab test results, epidemiological features, as well as the incidence rates were treated as predictors, and were collected from the published literature and a national surveillance system of infectious disease. A classification model was established using naïve Bayesian classifier. Dataset from historical outbreaks was applied for model validation, while sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC) and M-index were presented.A total of 146 predictors were included in the classification model, for discriminating 25 common infectious diseases. The sensitivity ranged from 44.44% for hepatitis E to 96.67% for measles. The specificity varied from 96.36% for dengue fever to 100% for 5 diseases. The median of total accuracy was 97.41% (range: 93.85%-99.04%). The AUCs exceeded 0.98 in 11 of 12 diseases, except in dengue fever (0.613). The M-index was 0.960 (95%CI 0.941-0.978).A novel classification model was constructed based on Bayesian approach to discriminate common infectious diseases in Zhejiang province, China. After entering symptoms and signs, abnormal lab test results, epidemiological features and city of disease origin, an output list of possible diseases ranked according to the calculated probabilities can be provided. The discrimination performance was reasonably good, making it useful in epidemiological applications.


Assuntos
Inteligência Artificial , Teorema de Bayes , Doenças Transmissíveis/epidemiologia , Doenças Transmissíveis/fisiopatologia , China/epidemiologia , Doenças Transmissíveis/diagnóstico , Diagnóstico por Computador/métodos , Humanos , Incidência , Reprodutibilidade dos Testes
3.
Eur Radiol ; 30(1): 261-271, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31418085

RESUMO

OBJECTIVE: To investigate the performance of Liver Imaging Reporting and Data System (LI-RADS) v2017 treatment response algorithm for predicting hepatocellular carcinoma (HCC) viability after locoregional therapy (LRT) using the liver explant as reference. METHODS: One hundred fourteen patients with 206 HCCs who underwent liver transplantation (LT) after LRT for HCCs were included in this retrospective study. Two radiologists independently evaluated tumor viability using the LI-RADS and modified RECIST (mRECIST) with CT and MRI, respectively. The sensitivity and specificity of arterial phase hyperenhancement (APHE) and LR-TR viable criteria (any of three findings: APHE, washout, and enhancement pattern similar to pretreatment imaging) were compared using logistic regression. Receiver operating characteristics (ROC) analysis was used to compare the diagnostic performance between LI-RADS and mRECIST and between CT and MRI. RESULTS: The sensitivity and specificity for diagnosing viable tumor were not significantly different between APHE alone and LR-TR viable criteria on CT (p = 0.054 and p = 0.317) and MRI (p = 0.093 and p = 0.603). On CT, the area under the ROC curve (AUC) of LI-RADS was significantly higher than that of mRECIST (0.733 vs. 0.657, p < 0.001). On MRI, there was no significant difference in AUCs between LI-RADS and mRECIST (0.802 vs. 0.791, p = 0.500). Intra-individual comparison of CT and MRI showed comparable AUCs using LI-RADS (0.783 vs. 0.795, p = 0.776). CONCLUSIONS: LI-RADS v2017 treatment response algorithm showed better diagnostic performance than mRECIST on CT. With LI-RADS, CT and MRI were comparable to diagnose tumor viability of HCC after LRT. KEY POINTS: • Using Liver Imaging Reporting and Data System (LI-RADS) v2017 treatment response algorithm, the viability of hepatocellular carcinoma (HCC) after locoregional therapy (LRT) can be accurately diagnosed. • LI-RADS v2017 treatment response algorithm is superior to modified Response Evaluation Criteria in Solid Tumors for evaluating HCC viability using CT. • Either CT or MRI can be performed to assess tumor viability after LRT using LI-RADS v2017 treatment response algorithm.


Assuntos
Algoritmos , Carcinoma Hepatocelular/diagnóstico , Diagnóstico por Computador/métodos , Neoplasias Hepáticas/diagnóstico , Imagem por Ressonância Magnética/métodos , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , Carcinoma Hepatocelular/patologia , Carcinoma Hepatocelular/cirurgia , Meios de Contraste/administração & dosagem , Feminino , Humanos , Neoplasias Hepáticas/patologia , Neoplasias Hepáticas/cirurgia , Transplante de Fígado , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Curva ROC , Projetos de Pesquisa , Critérios de Avaliação de Resposta em Tumores Sólidos , Estudos Retrospectivos , Sensibilidade e Especificidade
5.
Gastroenterology ; 158(1): 76-94.e2, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31593701

RESUMO

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.


Assuntos
Inteligência Artificial , Diagnóstico por Computador/métodos , Gastroenterologia/métodos , Gastroenteropatias/diagnóstico , Hepatopatias/diagnóstico , Tomada de Decisão Clínica/métodos , Sistemas de Apoio a Decisões Clínicas , Árvores de Decisões , Gastroenteropatias/mortalidade , Gastroenteropatias/terapia , Humanos , Hepatopatias/mortalidade , Hepatopatias/terapia , Prognóstico , Resultado do Tratamento
6.
Medicine (Baltimore) ; 98(50): e18324, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31852123

RESUMO

BACKGROUND: Although many machine learning algorithms have been developed to detect anterior cruciate ligament (ACL) injury based on magnetic resonance imaging (MRI), the performance of different algorithms required further investigation. The objectives of this current systematic review are to evaluate the diagnostic accuracy of machine-learning-assisted detection for ACL injury based on MRI and find the current best algorithm. METHOD: We will conduct a comprehensive database search for clinical diagnostic tests in PubMed, EMBASE, Cochrane Library, and Web of science without restrictions on publication status and language. The reference lists of the included articles will also be checked to identify additional studies for potential inclusion. Two reviewers will independently review all literature for inclusion and assess their methodological quality using Quality Assessment of Diagnostic Accuracy Studies version 2. Clinical diagnostic tests exploring the efficacy of machine-learning-assisted system for detecting ACL injury based on MRI will be considered for inclusion. Another 2 reviewers will independently extract data from eligible studies based on a pre-designed standardized form. Any disagreements will be resolved by consensus. RevMan 5.3 and Stata SE 12.0 software will be used for data synthesis. If appropriate, we will calculate the summary sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of machine-learning-assisted diagnosis system for ACL injury detection. A hierarchical summary receiver operating characteristic (HSROC) curve will also be plotted, and the area under the ROC curve (AUC) is going to calculated using the bivariate model. If the pooling of results is considered inappropriate, we will present and describe our findings in diagrams and tables and describe them narratively. RESULT: This is the first systematic assessment of machine learning system for the detection of ACL injury based on MRI. We predict it will provide highquality synthesis of existing evidence for the diagnostic accuracy of machine-learning-assisted detection for ACL injury and a relatively comprehensive reference for clinical practice and development of interdisciplinary field of artificial intelligence and medicine. CONCLUSION: This protocol outlined the significance and methodologically details of a systematic review of machine-learning-assisted detection for ACL injury based on MRI. The ongoing systematic review will provide high-quality synthesis of current evidence of machine learning system for detecting ACL injury. REGISTRATION: The meta-analysis has been prospectively registered in PROSPERO (CRD42019136581).


Assuntos
Lesões do Ligamento Cruzado Anterior/diagnóstico por imagem , Diagnóstico por Computador/estatística & dados numéricos , Aprendizado de Máquina , Imagem por Ressonância Magnética/estatística & dados numéricos , Diagnóstico por Computador/métodos , Humanos , Imagem por Ressonância Magnética/métodos , Metanálise como Assunto , Curva ROC , Projetos de Pesquisa , Sensibilidade e Especificidade , Revisão Sistemática como Assunto
7.
Medicine (Baltimore) ; 98(42): e17596, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31626135

RESUMO

To date, consumer health tools available over the web suffer from serious limitations that lead to low quality health- related information. While health data in our world are abundant, access to it is limited because of liability and privacy constraints.The objective of the present study was to develop and evaluate an algorithm-based tool which aims at providing the public with reliable, data-driven information based and personalized information regarding their symptoms, to help them and their physicians to make better informed decisions, based on statistics describing "people like you", who have experienced similar symptoms.We studied anonymized medical records of Maccabi Health Care. The data were analyzed by employing machine learning methodology and Natural Language Processing (NLP) tools. The NLP tools were developed to extract information from unstructured free-text written by Maccabi's physicians.Using machine learning and NLP on over 670 million notes of patients' visits with Maccabi physicians accrued since 1993, we developed predictors for medical conditions based on patterns of symptoms and personal characteristics.The algorithm was launched for Maccabi insured members on January 7, 2018 and for members of Integrity Family Care program in Alabama on May 1, 2018.The App. invites the user to describe her/ his main symptom or several symptoms, and this prompts a series of questions along the path developed by the algorithm, based on the analysis of 70 million patients' visits to their physicians.Users started dialogues with 225 different types of symptoms, answering on average 22 questions before seeing how people similar to them were diagnosed. Users usually described between 3 and 4 symptoms (mean 3.2) in the health dialogue.In response to the question "conditions verified by your doctor", 82.4% of responders (895/1085) in Maccabi reported that the diagnoses suggested by K's health dialogues were in agreement with their doctor's final diagnosis. In Integrity Health Services, 85.4% of responders (111/130) were in agreement with the physicians' diagnosis.While the program achieves very high approval rates by its users, its primary achievement is the 85% accuracy in identifying the most likely diagnosis, with the gold standard being the final diagnosis made by the personal physician in each individual case. Moreover, the machine learning algorithm continues to update itself with the feedback given by users.


Assuntos
Algoritmos , Apendicite/diagnóstico , Tomada de Decisões , Diagnóstico por Computador/métodos , Aprendizado de Máquina , Complicações na Gravidez/diagnóstico , Adulto , Apendicectomia , Apendicite/cirurgia , Feminino , Humanos , Gravidez , Smartphone
8.
Phys Med ; 64: 1-9, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31515007

RESUMO

BACKGROUND: Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool. METHODS: In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules. RESULTS: The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78% with 2.87 false positives per image. CONCLUSIONS: Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.


Assuntos
Calcinose/diagnóstico por imagem , Diagnóstico por Computador/métodos , Mamografia/métodos , Adulto , Idoso , Algoritmos , Automação , Neoplasias da Mama/complicações , Neoplasias da Mama/diagnóstico por imagem , Calcinose/complicações , Reações Falso-Positivas , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Pessoa de Meia-Idade
10.
Med Ultrason ; 21(3): 239-245, 2019 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-31476202

RESUMO

AIM: To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions. MATERIALS AND METHODS: Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the nested experiment design, five inexperienced readers were asked to select the appropriate BI-RADS lexicons, categories, CAD results, and combination results for each of the 100 lesions, retrospectively. Diagnostic performance of experienced and inexperienced radiologists and CAD were assessed. For each case, agreements in the lexicons and categories were analyzed among the experienced reader, inexperienced reader and CAD. RESULTS: Indicators of the diagnostic performance for breast malignancy of the experienced group (AUC=0.83, 95%CI [0.80, 0.86]) were similar or higher than those of CAD (AUC = 0.79, 95%CI[0.74, 0.83], p=0.101), except for specificity. Conversely, indicators of diagnostic performance of inexperienced group (AUC=0.65, 95%CI[0.58, 0.71]) did not differ from or were lower than those of CAD(AUC=0.73, 95%CI[0.67, 0.78], p=0.013). Also, the diagnostic performance of the inexperienced group after combination with the CAD result was significantly improved (0.71, 95% CI [0.65, 0.77], p=0.001), whereas that of the experienced group did not change after combination with the CAD result, except for specificity and positive predictive value (PPV). Kappa values for the agreement of the categorization between CAD and each radiologist group were increased after applying the CAD result to their result of general US. Especially, the increase of the Kappa value was higher in the inexperienced group than in the experienced group. Also, for all the lexicons, the Kappa values between the experienced group and CAD were higher than those between the inexperienced group and CAD. CONCLUSION: By using the CAD system for classification of breast lesions, diagnostic performance of the inexperienced radiologists for malignancy was significantly improved, and better agreement was observed in lexicons between the experienced group and CAD than between the inexperienced group and CAD. CAD may be beneficial and educational for the inexperienced group.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Competência Clínica , Diagnóstico por Computador/métodos , Ultrassonografia Mamária/métodos , Mama/diagnóstico por imagem , Feminino , Humanos , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sensibilidade e Especificidade
12.
Medicine (Baltimore) ; 98(32): e16379, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31393347

RESUMO

BACKGROUND: More and more automated efficient ultrasound image analysis techniques, such as ultrasound-based computer-aided diagnosis system (CAD), were developed to obtain accurate, reproducible, and more objective diagnosis results for thyroid nodules. So far, whether the diagnostic performance of existing CAD systems can reach the diagnostic level of experienced radiologists is still controversial. The aim of the meta-analysis was to evaluate the accuracy of CAD for thyroid nodules' diagnosis by reviewing current literatures and summarizing the research status. METHODS: A detailed literature search on PubMed, Embase, and Cochrane Libraries for articles published until December 2018 was carried out. The diagnostic performances of CAD systems vs radiologist were evaluated by meta-analysis. We determined the sensitivity and the specificity across studies, calculated positive and negative likelihood ratios and constructed summary receiver-operating characteristic (SROC) curves. Meta-analysis of studies was performed using a mixed-effect, hierarchical logistic regression model. RESULTS: Five studies with 536 patients and 723 thyroid nodules were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR) for CAD system were 0.87 (95% confidence interval [CI], 0.73-0.94), 0.79 (95% CI 0.63-0.89), 4.1 (95% CI 2.5-6.9), 0.17 (95% CI 0.09-0.32), and 25 (95% CI 15-42), respectively. The SROC curve indicated that the area under the curve was 0.90 (95% CI 0.87-0.92). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and DOR for experienced radiologists were 0.82 (95% CI 0.69-0.91), 0.83 (95% CI 0.76-0.89), 4.9 (95% CI 3.4-7.0), 0.22 (95% CI 0.12-0.38), and 23 (95% CI 11-46), respectively. The SROC curve indicated that the area under the curve was 0.96 (95% CI 0.94-0.97). CONCLUSION: The sensitivity of the CAD system in the diagnosis of thyroid nodules was similar to that of experienced radiologists. However, the CAD system had lower specificity and DOR than experienced radiologists. The CAD system may play the potential role as a decision-making assistant alongside radiologists in the thyroid nodules' diagnosis. Future technical improvements would be helpful to increase the accuracy as well as diagnostic efficiency.


Assuntos
Diagnóstico por Computador/métodos , Diagnóstico por Computador/normas , Radiologistas/normas , Nódulo da Glândula Tireoide/diagnóstico , Inteligência Artificial , Diagnóstico Diferencial , Humanos , Curva ROC , Sensibilidade e Especificidade , Neoplasias da Glândula Tireoide/diagnóstico , Neoplasias da Glândula Tireoide/patologia , Nódulo da Glândula Tireoide/diagnóstico por imagem , Nódulo da Glândula Tireoide/patologia , Ultrassonografia
13.
Undersea Hyperb Med ; 46(3): 245-249, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31394595

RESUMO

Functional magnetic resonance imaging (fMRI) has been available commercially for clinical diagnostic use for many years. However, both clinical interpretation of fMRI by a neuroradiologist and quantitative analysis of fMRI data can require significant personnel resources that exceed reimbursement. In this report, a fully automated computer-based quantification methodology (Enumerated Auditory Response, EAR) has been developed to provide an auditory fMRI assessment of patients who have suffered a mild traumatic brain injury. Fifty-five study participants with interpretable auditory fMRI sequence data were assessed by EAR analysis, as well as both clinical radiologist fMRI interpretation and voxelwise general linear model (GLM) analysis. Comparison between the clinical interpretation and the two computer analysis methods resulted in 67% concordance (identical), 32% nearconcordance (one level difference), and 1% discordant. Comparison between the clinical computer-based quantification (EAR) and GLM analysis yielded significant correlations in right and left ear responses (p⟨0.05) for the full subject group. Automated fMRI quantification analysis equivalent to EAR might be appropriate for both future research projects with constrained resources, as well as possible routine clinical use.


Assuntos
Doenças Auditivas Centrais/diagnóstico por imagem , Concussão Encefálica/fisiopatologia , Diagnóstico por Computador/métodos , Técnicas de Diagnóstico Otológico , Imagem por Ressonância Magnética/métodos , Doenças Auditivas Centrais/fisiopatologia , Concussão Encefálica/diagnóstico por imagem , Feminino , Humanos , Modelos Lineares , Masculino , Militares , Veteranos
14.
J Med Syst ; 43(9): 302, 2019 Aug 09.
Artigo em Inglês | MEDLINE | ID: mdl-31396722

RESUMO

The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student's t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer's diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.


Assuntos
Doença de Alzheimer/diagnóstico por imagem , Encéfalo/patologia , Diagnóstico por Computador/métodos , Reconhecimento Automatizado de Padrão/métodos , Doença de Alzheimer/classificação , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética/métodos
15.
Med Hypotheses ; 129: 109242, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31371092

RESUMO

Microaneurysms are lesions in the shape of small circular dilations which result from thinning in peripheral retinal blood vessels due to diabetes and increasing intra-retinal blood pressure. Because it is considered as the most important clinical finding in the diagnosis of diabetic retinopathy, accurate detection of these lesions bear utmost importance in the early diagnosis of diabetic retinopathy. The present study aims to accurately, effectively and automatically detect microaneurysms which are difficult to detect in color fundus images in early stage. To this aim, ant colony algorithm, which is an important optimization method, was used instead of conventional image processing techniques. First, retinal vascular structure was extracted from color fundus images in Messidor and DiaretDB1 data sets. Afterwards, the segmentation of microaneurysms was effectively carried out using ant colony algorithm. The same procedure was also applied to five different image processing and clustering algorithms (watershed, random walker, k-means, maximum entropy and region growing) in order to compare the performance of the proposed method with other methods. Microaneurysm images manually detected by a specialist eye doctor were used to measure the performances of above-mentioned methods. The similarities among microaneurysms which were automatically and manually segmented were tested using Dice and Jaccard similarity index values. Dice index values obtained from the study vary between 0.52 and 0.98 in maximum entropy, 0.55 and 0.88 in watershed, 0.75 and 0.86 in region growing, 0.55 and 0.78 in k-means, and 0.66 and 0.83 in random walker, and 0.81 and 0.9 in ant colony. Similar performance values were also obtained in Jaccard index. The results show that different performances were observed in the conventional segmentation of microaneurysms depending on the image quality. On the other hand, the ant colony based method proposed in this paper displays a more stabilized and higher performance irrespective of image contrast. Therefore, it is evident that the proposed method successfully detects microaneurysms even in low quality images, thus helping specialists diagnose them in an easier way.


Assuntos
Retinopatia Diabética/diagnóstico por imagem , Diagnóstico por Computador/métodos , Processamento de Imagem Assistida por Computador/métodos , Microaneurisma/diagnóstico por imagem , Reconhecimento Automatizado de Padrão/métodos , Algoritmos , Cor , Diabetes Mellitus/fisiopatologia , Fundo de Olho , Humanos , Modelos Estatísticos , Reprodutibilidade dos Testes , Vasos Retinianos/patologia
16.
Med Hypotheses ; 130: 109251, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31383333

RESUMO

Vectorcardiogram (VCG) is a recurring, near-periodic pattern of cardiac dynamics that graphically represents the trajectory of the tip of cardiac vectors in three dimensional space with varying time. VCG is constructed by drawing the instantaneous vectors from a zero reference point according to direction, magnitude and polarity in the space. It is more informative and sensitive than conventional ECG as an evaluation tool of the physiology of cardiac dynamics. Each heart cycle consists of three loops corresponding to P, QRS, and T wave activities in VCG. The morphological assessment of the QRS loop was carried out in order to analyze the spatial vectors of the ventricles and their patho-physiological correlation in various cardiac diseases. It was found that, the three dimensional QRS loop in healthy individuals lies in a plane. It is rather surprising that the normal spatial QRS loop lies in a single plane, considering the complex structure of the ventricular musculature together with the numerous possible pathways along which the depolarization impulse passes. The highly curious phenomenon of planarity of the spatial QRS loop was explained by uniform double layer (UDL) theory, which postulates the phenomenon of activation wave-fronts that propagate with a constant & uniform rate throughout the myocardium. Acute myocardial infarction results in loss of structural and functional integrity of the different layers of heart, perturbation of the uniformity in wave propagation due to the disturbance in directional symmetry, development of nonlinear relationships among the concerned variables, loss of homogeneity and complete loss of planarity of the 3D-QRS loop. The planarity of the 3D-QRS loop is a highly restricted and sensitive parameter and a characteristic feature of normal heart and can be utilized as a test for diagnostic screening of cardiac normalcy and the loss of planarity may be a conspicuous feature of AMI. It will be reasonable to study the morphology of the spatial QRS loop in patients of AMI throughout the course of disease and also in a regular interval through the long-term follow up period. It is expected that with the reperfusion, recovery and salvage of the diseased myocardium; the homogeneity and the intensity of the line density of the membrane current of the UDL would gradually recover with retrieval of the planarity of the spatial QRS loop. The temporal pattern of characteristics alteration of the QRS loop planarity with the natural course of the disease requires intensive evaluation. We propose that, the planarity of the spatial QRS loop, its loss, involution and reversal is a temporal series of events in AMI and also a crucial diagnostic and prognostic parameter.


Assuntos
Arritmias Cardíacas/diagnóstico , Eletrocardiografia/métodos , Coração/fisiologia , Infarto do Miocárdio/diagnóstico , Diagnóstico por Computador/métodos , Coração/fisiopatologia , Ventrículos do Coração/fisiopatologia , Humanos , Miocárdio/patologia , Prognóstico , Reprodutibilidade dos Testes , Processamento de Sinais Assistido por Computador
18.
Comput Methods Programs Biomed ; 178: 247-263, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31416553

RESUMO

BACKGROUND AND OBJECTIVE: Conventional information systems are built on top of a relational database. The main weakness of these systems is impossibility to define stable data schema ahead when the knowledge of the system is evolving and dynamic. The widely accepted alternatives to relational databases are ontologies that can be used for designing information systems. Many research papers describe various methods for improving reliability and precision in generating the type of the Lenke classification based on the image processing techniques or a computer program, but all of them require radiograph images. The main objective of this paper is to demonstrate the development of an ontology-based module of the information system ScolioMedIS for adolescent idiopathic scoliosis (AIS) diagnosis and monitoring, which uses optical 3D methods to determine the Lenke classification of AIS and to avoid harmful effects of traditional radiation diagnosis. METHODS: For creating an ontology-based module of the ScolioMedIS we used the following steps: specification, conceptualization, formalization and implementation. In the specification and conceptualization phase we performed data collection and analysis to define domain, concepts and relationships for ontology design. In the formalization and implementation stage we developed the OBR-Scolio ontology and the ontology-based module of the ScolioMedIS. The module employs the Protégé-OWL API, as a collection of Java interfaces for the OBR-Scolio ontology, which enables the creating, deleting, and editing of the basic elements of the OBR-Scolio ontology, as well as the querying of the ontology. RESULTS: The ontology-based module of ScolioMedIS is tested on the datasets of 20 female and 15 male patients with AIS between the ages of 11 and 18, to categorize spinal curvatures and to automatically generate statistical indicators about the frequency of the basic spinal curvatures, degree of progression or regression of deformity and statistical indicators about curvature characteristics according to the Lenke classification system and Lenke scoliosis types. Results are then compared with analysis of the Lenke classification of 315 observed patients, performed using traditional radiation techniques. CONCLUSIONS: This part of the system allows continuous monitoring of the progression/regression of spinal curvatures for each registered patient, which may provide a better management of scoliosis (diagnosis and treatment).


Assuntos
Diagnóstico por Computador/métodos , Imagem Tridimensional/métodos , Escoliose/diagnóstico por imagem , Adolescente , Algoritmos , Criança , Gráficos por Computador , Sistemas de Computação , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Vértebras Lombares/diagnóstico por imagem , Masculino , Informática Médica , Linguagens de Programação , Reprodutibilidade dos Testes , Software , Vértebras Torácicas/diagnóstico por imagem , Interface Usuário-Computador
19.
Comput Methods Programs Biomed ; 178: 289-301, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31416556

RESUMO

BACKGROUND AND OBJECTIVE: Efficient segmentation of skin lesion in dermoscopy images can improve the classification accuracy of skin diseases, which provides a powerful approach for the dermatologists in examining pigmented skin lesions. However, the segmentation is challenging due to the low contrast of skin lesions from a captured image, fuzzy and indistinct lesion boundaries, huge variety of interclass variation of melanomas, the existence of artifacts, etc. In this work, an efficient and accurate melanoma region segmentation method is proposed for computer-aided diagnostic systems. METHOD: A skin lesion segmentation (SLS) method based on the separable-Unet with stochastic weight averaging is proposed in this work. Specifically, the proposed Separable-Unet framework takes advantage of the separable convolutional block and U-Net architectures, which can extremely capture the context feature channel correlation and higher semantic feature information to enhance the pixel-level discriminative representation capability of fully convolutional networks (FCN). Further, considering that the over-fitting is a local optimum (or sub-optimum) problem, a scheme based on stochastic weight averaging is introduced, which can obtain much broader optimum and better generalization. RESULTS: The proposed method is evaluated in three publicly available datasets. The experimental results showed that the proposed approach segmented the skin lesions with an average Dice coefficient of 93.03% and Jaccard index of 89.25% for the International Skin Imaging Collaboration (ISIC) 2016 Skin Lesion Challenge (SLC) dataset, 86.93% and 79.26% for the ISIC 2017 SLC, and 94.13% and 89.40% for the PH2 dataset, respectively. The proposed approach is compared with other state-of-the-art methods, and the results demonstrate that the proposed approach outperforms them for SLS on both melanoma and non-melanoma cases. Segmentation of a potential lesion with the proposed approach in a dermoscopy image requires less than 0.05 s of processing time, which is roughly 30 times faster than the second best method (regarding the value of Jaccard index) for the ISIC 2017 dataset with the same hardware configuration. CONCLUSIONS: We concluded that using the separable convolutional block and U-Net architectures with stochastic weight averaging strategy could enable to obtain better pixel-level discriminative representation capability. Moreover, the considerably decreased computation time suggests that the proposed approach has potential for practical computer-aided diagnose systems, besides provides a segmentation for the specific analysis with improved segmentation performance.


Assuntos
Diagnóstico por Computador/métodos , Reconhecimento Automatizado de Padrão , Dermatopatias/diagnóstico por imagem , Pele/diagnóstico por imagem , Algoritmos , Artefatos , Bases de Dados Factuais , Dermoscopia/métodos , Reações Falso-Positivas , Humanos , Processamento de Imagem Assistida por Computador/métodos , Melanoma/diagnóstico por imagem , Reprodutibilidade dos Testes , Neoplasias Cutâneas/diagnóstico por imagem , Processos Estocásticos
20.
Psychol Assess ; 31(11): 1377-1382, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31414853

RESUMO

Computerized cognitive screening tools, such as the self-administered Computerized Assessment of Memory Cognitive Impairment (CAMCI), require little training and ensure standardized administration and could be an ideal test for primary care settings. We conducted a secondary analysis of a data set including 887 older adults (M age = 72.7 years, SD = 7.1 years; 32.1% male; M years education = 13.4, SD = 2.7 years) with CAMCI scores and independent diagnoses of mild cognitive impairment (MCI). A study by the CAMCI developers used a portion of this data set with a machine learning decision tree model and suggested that the CAMCI had high classification accuracy for MCI (sensitivity = 0.86, specificity = 0.94). We found similar support for accuracy (sensitivity = 0.94, specificity = 0.94) by overfitting a decision tree model, but we found evidence of lower accuracy in a cross-validation sample (sensitivity = 0.62, specificity = 0.66). A logistic regression model, however, discriminated modestly in both training (sensitivity = 0.72, specificity = 0.80) and cross-validation data sets (sensitivity = 0.69, specificity = 0.74). Evidence for strong accuracy when overfitting a decision tree model and substantially reduced accuracy in cross-validation samples was replicated across 500 bootstrapped samples. In contrast, the evidence for accuracy of the logistic regression model was similar in the training and cross-validation samples. The logistic regression model produced accuracy estimates consistent with other published CAMCI studies, suggesting evidence for classification accuracy of the CAMCI for MCI is likely modest. This case study illustrates the general need for cross-validation and careful evaluation of the generalizability of machine learning models. (PsycINFO Database Record (c) 2019 APA, all rights reserved).


Assuntos
Algoritmos , Disfunção Cognitiva/diagnóstico , Diagnóstico por Computador/métodos , Testes Neuropsicológicos/normas , Idoso , Idoso de 80 Anos ou mais , Disfunção Cognitiva/psicologia , Computadores , Feminino , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
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