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1.
Clin EEG Neurosci ; 52(1): 38-51, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-32491928

RESUMO

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and ß, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.


Assuntos
Encéfalo/fisiopatologia , Aprendizado Profundo , Transtorno Depressivo Maior/fisiopatologia , Eletroencefalografia , Adulto , Interfaces Cérebro-Computador/psicologia , Diagnóstico por Computador/métodos , Eletroencefalografia/métodos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação
2.
Ultrasonics ; 110: 106271, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33166786

RESUMO

Accurate breast mass segmentation of automated breast ultrasound (ABUS) is a great help to breast cancer diagnosis and treatment. However, the lack of clear boundary and significant variation in mass shapes make the automatic segmentation very challenging. In this paper, a novel automatic tumor segmentation method SC-FCN-BLSTM is proposed by incorporating bi-directional long short-term memory (BLSTM) and spatial-channel attention (SC-attention) module into fully convolutional network (FCN). In order to decrease performance degradation caused by ambiguous boundaries and varying tumor sizes, an SC-attention module is designed to integrate both finer-grained spatial information and rich semantic information. Since ABUS is three-dimensional data, utilizing inter-slice context can improve segmentation performance. A BLSTM module with SC-attention is constructed to model the correlation between slices, which employs inter-slice context to assist segmentation for false positive elimination. The proposed method is verified on our private ABUS dataset of 124 patients with 170 volumes, including 3636 2D labeled slices. The Dice similarity coefficient (DSC), Recall, Precision and Hausdorff distance (HD) of the proposed method are 0.8178, 0.8067, 0.8292 and 11.1367. Experimental results demonstrate that the proposed method offered improved segmentation results compared with existing deep learning-based methods.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Ultrassonografia Mamária/métodos , Algoritmos , Diagnóstico por Computador , Feminino , Humanos
4.
Medicine (Baltimore) ; 99(51): e23685, 2020 Dec 18.
Artigo em Inglês | MEDLINE | ID: mdl-33371110

RESUMO

ABSTRACT: Because of endoscopist fatigue, the time of colonoscopy have been shown to influence adenoma detection rate (ADR). Computer-aided detection (CADe) provides simultaneous visual alerts on polyps during colonoscopy and thus to increase adenoma detection rate. This is attributable to the strengthening of endoscopists diagnostic level and alleviation of fatigue. The aim of the study was to investigate whether CADe colonoscopy could eliminate the influence of the afternoon fatigue on ADR.We retrospectively analyzed the recorded data of patients who were performed CADe colonoscopy from September 2017 to February 2019 in Endoscopy Center of Sichuan Provincial People's Hospital. Patients demographic as well as baseline data recorded during colonoscopy were used for the analysis. Morning colonoscopy was defined as colonoscopic procedures starting between 8:00 am and 12:00 noon. Afternoon colonoscopy was defined as procedures starting at 2:00 pm and thereafter. The primary outcome was ADR. Univariate analysis and multivariate regression analysis were also performed.A total of 484 CADe colonoscopies were performed by 4 endoscopists in the study. The overall polyp detection rate was 52% and overall ADR was 35.5%. The mean number of adenomas detected per colonoscopy (0.62 vs 0.61, P > .05) and ADR (0.36 vs 0.35, P > .05) were similar in the am and pm group. Multivariable analysis shows that the ADR of CADe colonoscopy was influenced by the age (P < .001), gender (P = .004) and withdrawal time (P < .001), no correlation was found regarding bowel preparation (P = .993) and endoscopist experience (P = .804).CADe colonoscopy could eliminate the influence of the afternoon fatigue on ADR. The ADR during CADe colonoscopy is significantly affected by age, gender and withdrawal time.


Assuntos
Adenoma/diagnóstico , Neoplasias do Colo/diagnóstico , Colonoscopia/estatística & dados numéricos , Diagnóstico por Computador , Adulto , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Fatores de Tempo
5.
J Healthc Eng ; 2020: 6648574, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33343851

RESUMO

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.


Assuntos
Aprendizado Profundo , Diagnóstico por Computador , Internet das Coisas , Leucemia/classificação , Leucemia/diagnóstico , Reconhecimento Automatizado de Padrão , Algoritmos , Computação em Nuvem , Bases de Dados Factuais , Diagnóstico por Imagem , Humanos , Leucemia Linfocítica Crônica de Células B/diagnóstico , Leucemia Mielogênica Crônica BCR-ABL Positiva/diagnóstico , Leucemia Mieloide Aguda/diagnóstico , Aprendizado de Máquina , Redes Neurais de Computação , Leucemia-Linfoma Linfoblástico de Células Precursoras/diagnóstico , Telemedicina
7.
PLoS One ; 15(12): e0242712, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33290403

RESUMO

Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.


Assuntos
Doença de Alzheimer/diagnóstico por imagem , Tonsila do Cerebelo/diagnóstico por imagem , Córtex Cerebral/diagnóstico por imagem , Disfunção Cognitiva/diagnóstico por imagem , Demência/diagnóstico por imagem , Hipocampo/diagnóstico por imagem , Rede Nervosa/diagnóstico por imagem , Idoso , Idoso de 80 Anos ou mais , Doença de Alzheimer/patologia , Tonsila do Cerebelo/patologia , Córtex Cerebral/patologia , Disfunção Cognitiva/patologia , Bases de Dados Factuais , Conjuntos de Dados como Assunto , Demência/patologia , Diagnóstico por Computador/métodos , Feminino , Hipocampo/patologia , Humanos , Imagem por Ressonância Magnética/métodos , Imagem por Ressonância Magnética/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Rede Nervosa/patologia , Neuroimagem/métodos , Neuroimagem/estatística & dados numéricos
8.
Scand J Trauma Resusc Emerg Med ; 28(1): 113, 2020 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-33261629

RESUMO

BACKGROUND: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments. METHODS: This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol. RESULTS: Among 199 patients subject to study (median [interquartile range] age 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity. CONCLUSION: Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.


Assuntos
/diagnóstico , Diagnóstico por Computador , Aprendizado de Máquina , Software , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Sensibilidade e Especificidade
9.
Lancet ; 396(10266): 1874, 2020 12 12.
Artigo em Inglês | MEDLINE | ID: mdl-33308459
10.
Nat Commun ; 11(1): 5595, 2020 11 05.
Artigo em Inglês | MEDLINE | ID: mdl-33154370

RESUMO

Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these models require time-consuming preprocessing steps to remove artifacts, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn representations from raw data. However, their effectiveness decreases when the number of available training samples is small, which is a common situation in medicine. In this work, we investigate transfer learning on 1D-CNNs, then we develop a cumulative learning method when transfer learning is not powerful enough. We propose to train the same model through several classification tasks over various small datasets to accumulate knowledge in the resulting representation. By using rat brain as the initial training dataset, a cumulative learning approach can have a classification accuracy exceeding 98% for 1D clinical MS-data. We show the use of cumulative learning using datasets generated in different biological contexts, on different organisms, and acquired by different instruments. Here we show a promising strategy for improving MS data classification accuracy when only small numbers of samples are available.


Assuntos
Aprendizado Profundo , Espectrometria de Massas/métodos , Redes Neurais de Computação , Animais , Bases de Dados Factuais , Diagnóstico por Computador , Humanos , Aprendizado de Máquina , Espectrometria de Massas/estatística & dados numéricos
11.
PLoS One ; 15(11): e0242806, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33237975

RESUMO

PURPOSE: To investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAFV600E mutation in thyroid cancer. METHODS: 469 thyroid cancers in 469 patients were included in this retrospective study. A CAD program recently developed using the deep CNN provided risks of malignancy (0-100%) as well as binary results (cancer or not). Using the CAD program, we calculated the risk of malignancy based on a US image of each thyroid nodule (CAD value). Univariate and multivariate logistic regression analyses were performed including patient demographics, the American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TIRADS) categories and risks of malignancy calculated through CAD to identify independent predictive factors for the BRAFV600E mutation in thyroid cancer. The predictive power of the CAD value and final multivariable model for the BRAFV600E mutation in thyroid cancer were measured using the area under the receiver operating characteristic (ROC) curves. RESULTS: In this study, 380 (81%) patients were positive and 89 (19%) patients were negative for the BRAFV600E mutation. On multivariate analysis, older age (OR = 1.025, p = 0.018), smaller size (OR = 0.963, p = 0.006), and higher CAD value (OR = 1.016, p = 0.004) were significantly associated with the BRAFV600E mutation. The CAD value yielded an AUC of 0.646 (95% CI: 0.576, 0.716) for predicting the BRAFV600E mutation, while the multivariable model yielded an AUC of 0.706 (95% CI: 0.576, 0.716). The multivariable model showed significantly better performance than the CAD value alone (p = 0.004). CONCLUSION: Deep learning-based CAD for thyroid US can help us predict the BRAFV600E mutation in thyroid cancer. More multi-center studies with more cases are needed to further validate our study results.


Assuntos
Inteligência Artificial , Carcinoma Papilar/genética , Proteínas Proto-Oncogênicas B-raf/genética , Neoplasias da Glândula Tireoide/genética , Adulto , Idoso , Carcinoma Papilar/diagnóstico , Carcinoma Papilar/epidemiologia , Carcinoma Papilar/patologia , Diagnóstico por Computador , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Mutação/genética , Glândula Tireoide/diagnóstico por imagem , Glândula Tireoide/patologia , Neoplasias da Glândula Tireoide/diagnóstico por imagem , Neoplasias da Glândula Tireoide/epidemiologia , Neoplasias da Glândula Tireoide/patologia , Nódulo da Glândula Tireoide , Tomografia Computadorizada por Raios X
12.
PLoS One ; 15(11): e0242013, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33166371

RESUMO

BACKGROUND: Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be benefit for the patients in the underdevelopment areas lack of the experienced radiologists. In recent years, with the development of large neural network architectures and medical imaging datasets, deep learning methods have become a methodology of choice for analyzing medical images. The objective of this study was to the construct convolutional neural networks to localize the pneumothorax lesions in chest radiographs. METHODS AND FINDINGS: We developed a convolutional neural network, called CheXLocNet, for the segmentation of pneumothorax lesions. The SIIM-ACR Pneumothorax Segmentation dataset was used to train and validate CheXLocNets. The training dataset contained 2079 radiographs with the annotated lesion areas. We trained six CheXLocNets with various hyperparameters. Another 300 annotated radiographs were used to select parameters of these CheXLocNets as the validation set. We determined the optimal parameters by the AP50 (average precision at the intersection over union (IoU) equal to 0.50), a segmentation evaluation metric used by several well-known competitions. Then CheXLocNets were evaluated by a test set (1082 normal radiographs and 290 disease radiographs), based on the classification metrics: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV); segmentation metrics: IoU and Dice score. For the classification, CheXLocNet with best sensitivity produced an AUC of 0.87, sensitivity of 0.78 (95% CI 0.73-0.83), and specificity of 0.78 (95% CI 0.76-0.81). CheXLocNet with best specificity produced an AUC of 0.79, sensitivity of 0.46 (95% CI 0.40-0.52), and specificity of 0.92 (95% CI 0.90-0.94). For the segmentation, CheXLocNet with best sensitivity produced an IoU of 0.69 and Dice score of 0.72. CheXLocNet with best specificity produced an IoU of 0.77 and Dice score of 0.79. We combined them to form an ensemble CheXLocNet. The ensemble CheXLocNet produced an IoU of 0.81 and Dice score of 0.82. Our CheXLocNet succeeded in automatically detecting pneumothorax lesions, without any human guidance. CONCLUSIONS: In this study, we proposed a deep learning network, called, CheXLocNet, for the automatic segmentation of chest radiographs to detect pneumothorax. Our CheXLocNets generated accurate classification results and high-quality segmentation masks for the pneumothorax at the same time. This technology has the potential to improve healthcare delivery and increase access to chest radiograph expertise for the detection of diseases. Furthermore, the segmentation results can offer comprehensive geometric information of lesions, which can benefit monitoring the sequential development of lesions with high accuracy. Thus, CheXLocNets can be further extended to be a reliable clinical decision support tool. Although we used transfer learning in training CheXLocNet, the parameters of CheXLocNet was still large for the radiograph dataset. Further work is necessary to prune CheXLocNet suitable for the radiograph dataset.


Assuntos
Redes Neurais de Computação , Pneumotórax/diagnóstico por imagem , Aprendizado Profundo , Diagnóstico por Computador/métodos , Humanos , Processamento de Imagem Assistida por Computador/métodos , Radiografia/métodos
13.
J Anim Sci ; 98(12)2020 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-33247918

RESUMO

Bovine respiratory disease (BRD) is the most significant disease affecting feedlot cattle. Indicators of BRD often used in feedlots such as visual signs, rectal temperature, computer-assisted lung auscultation (CALA) score, the number of BRD treatments, presence of viral pathogens, viral seroconversion, and lung damage at slaughter vary in their ability to predict an animal's BRD outcome, and no studies have been published determining how a combination of these BRD indicators may define the number of BRD disease outcome groups. The objectives of the current study were (1) to identify BRD outcome groups using BRD indicators collected during the feeding phase and at slaughter through latent class analysis (LCA) and (2) to determine the importance of these BRD indicators to predict disease outcome. Animals with BRD (n = 127) were identified by visual signs and removed from production pens for further examination. Control animals displaying no visual signs of BRD (n = 143) were also removed and examined. Blood, nasal swab samples, and clinical measurements were collected. Lung and pleural lesions indicative of BRD were scored at slaughter. LCA was applied to identify possible outcome groups. Three latent classes were identified in the best model fit, categorized as non-BRD, mild BRD, and severe BRD. Animals in the mild BRD group had a higher probability of having visual signs of BRD compared with non-BRD and severe BRD animals. Animals in the severe BRD group were more likely to require more than 1 treatment for BRD and have ≥40 °C rectal temperature, ≥10% total lung consolidation, and severe pleural lesions at slaughter. Animals in the severe BRD group were also more likely to be naïve at feedlot entry and the first BRD pull for Bovine Viral Diarrhoea Virus, Bovine Parainfluenza 3 Virus, and Bovine Adenovirus and have a positive nasal swab result for Bovine Herpesvirus Type 1 and Bovine Coronavirus. Animals with severe BRD had 0.9 and 0.6 kg/d lower overall ADG (average daily gain) compared with non-BRD animals and mild BRD animals (P < 0.001). These results demonstrate that there are important indicators of BRD severity. Using this information to predict an animal's BRD outcome would greatly enhance treatment efficacy and aid in better management of animals at risk of suffering from severe BRD.


Assuntos
Complexo Respiratório Bovino/diagnóstico , Análise de Classes Latentes , Animais , Auscultação/veterinária , Temperatura Corporal , Complexo Respiratório Bovino/tratamento farmacológico , Complexo Respiratório Bovino/epidemiologia , Complexo Respiratório Bovino/patologia , Bovinos , Estudos de Coortes , Diagnóstico por Computador/veterinária , Pulmão/patologia , Masculino , Mucosa Nasal/virologia , New South Wales/epidemiologia , Resultado do Tratamento
14.
Bone Joint J ; 102-B(11): 1574-1581, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33135455

RESUMO

AIMS: The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. METHODS: In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into 'dislocation' (dislocation and subluxation) and 'non-dislocation' (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots. RESULTS: In all, 1,138 patients (242 males; 896 females; mean age 1.5 years (SD 1.79; 0 to 10) were included in this study. The area under the receiver operating characteristic curve, sensitivity, and specificity of the deep learning system for diagnosing hip dislocation were 0.975, 276/289 (95.5%), and 1,978/1,987 (99.5%), respectively. Compared with clinical diagnoses, the Bland-Altman 95% limits of agreement for acetabular index, as determined by the deep learning system from the radiographs of non-dislocated and dislocated hips, were -3.27° - 2.94° and -7.36° - 5.36°, respectively (p < 0.001). CONCLUSION: The deep learning system was highly consistent, more convenient, and more effective for diagnosing DDH compared with clinician-led diagnoses. Deep learning systems should be considered for analysis of anteroposterior pelvic radiographs when diagnosing DDH. The deep learning system will improve the current artificially complicated screening referral process. Cite this article: Bone Joint J 2020;102-B(11):1574-1581.


Assuntos
Inteligência Artificial , Diagnóstico por Computador , Luxação Congênita de Quadril/diagnóstico por imagem , Pré-Escolar , Feminino , Luxação Congênita de Quadril/diagnóstico , Humanos , Interpretação de Imagem Assistida por Computador , Lactente , Recém-Nascido , Masculino
15.
BMC Med Inform Decis Mak ; 20(1): 264, 2020 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-33059709

RESUMO

BACKGROUND: Syndrome differentiation aims at dividing patients into several types according to their clinical symptoms and signs, which is essential for traditional Chinese medicine (TCM). Several previous works were devoted to employing the classical algorithms to classify the syndrome and achieved delightful results. However, the presence of ambiguous symptoms substantially disturbed the performance of syndrome differentiation, This disturbance is always due to the diversity and complexity of the patients' symptoms. METHODS: To alleviate this issue, we proposed an algorithm based on the multilayer perceptron model with an attention mechanism (ATT-MLP). In particular, we first introduced an attention mechanism to assign different weights for different symptoms among the symptomatic features. In this manner, the symptoms of major significance were highlighted and ambiguous symptoms were restrained. Subsequently, those weighted features were further fed into an MLP to predict the syndrome type of AIDS. RESULTS: Experimental results for a real-world AIDS dataset show that our framework achieves significant and consistent improvements compared to other methods. Besides, our model can also capture the key symptoms corresponding to each type of syndrome. CONCLUSION: In conclusion, our proposed method can learn these intrinsic correlations between symptoms and types of syndromes. Our model is able to learn the core cluster of symptoms for each type of syndrome from limited data, while assisting medical doctors to diagnose patients efficiently.


Assuntos
Síndrome de Imunodeficiência Adquirida/diagnóstico , Diagnóstico por Computador/métodos , Medicina Tradicional Chinesa/métodos , Redes Neurais de Computação , Algoritmos , Atenção , Humanos
16.
Sci Rep ; 10(1): 16331, 2020 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-33004907

RESUMO

Standard 12-lead electrocardiography (ECG) is used as the primary clinical tool to diagnose changes in heart function. The value of automated 12-lead ECG diagnostic approaches lies in their ability to screen the general population and to provide a second opinion for doctors. Yet, the clinical utility of automated ECG interpretations remains limited. We introduce a two-way approach to an automated cardiac disease identification system using standard digital or image 12-lead ECG recordings. Two different network architectures, one trained using digital signals (CNN-dig) and one trained using images (CNN-ima), were generated. An open-source dataset of 41,830 classified standard ECG recordings from patients and volunteers was generated. CNN-ima was trained to identify atrial fibrillation (AF) using 12-lead ECG digital signals and images that were also transformed to mimic mobile device camera-acquired ECG plot snapshots. CNN-dig accurately (92.9-100%) identified every possible combination of the eight most-common cardiac conditions. Both CNN-dig and CNN-ima accurately (98%) detected AF from standard 12-lead ECG digital signals and images, respectively. Similar classification accuracy was achieved with images containing smartphone camera acquisition artifacts. Automated detection of cardiac conditions in standard digital or image 12-lead ECG signals is feasible and may improve current diagnostic methods.


Assuntos
Diagnóstico por Computador , Eletrocardiografia/métodos , Cardiopatias/diagnóstico , Interpretação de Imagem Assistida por Computador , Fibrilação Atrial/diagnóstico , Fibrilação Atrial/fisiopatologia , Automação/métodos , Diagnóstico por Computador/métodos , Feminino , Coração/fisiologia , Coração/fisiopatologia , Cardiopatias/fisiopatologia , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Masculino , Redes Neurais de Computação , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Aprendizado de Máquina Supervisionado
17.
BMC Med Inform Decis Mak ; 20(1): 250, 2020 10 02.
Artigo em Inglês | MEDLINE | ID: mdl-33008388

RESUMO

BACKGROUND: Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning models and high dimensional data sources such as electronic health records, magnetic resonance imaging scans, cardiotocograms, etc. These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years because it addresses the interpretability and trust concerns of critical decision makers, including those in clinical and medical practice. METHODS: In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge - to explain AdaBoost classification - with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, Adaptive-Weighted High Importance Path Snippets (Ada-WHIPS), makes use of AdaBoost's adaptive classifier weights. Using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes of the internal decision trees of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model's decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure stability that is better suited to the XAI setting. RESULTS: Experiments on 9 CAD-related data sets showed that Ada-WHIPS explanations consistently generalise better (mean coverage 15%-68%) than the state of the art while remaining competitive for specificity (mean precision 80%-99%). A very small trade-off in specificity is shown to guard against over-fitting which is a known problem in the state of the art methods. CONCLUSIONS: The experimental results demonstrate the benefits of using our novel algorithm for explaining CAD AdaBoost classifiers widely found in the literature. Our tightly coupled, AdaBoost-specific approach outperforms model-agnostic explanation methods and should be considered by practitioners looking for an XAI solution for this class of models.


Assuntos
Algoritmos , Inteligência Artificial , Tomada de Decisões Assistida por Computador , Sistemas de Apoio a Decisões Clínicas , Diagnóstico por Computador , Humanos , Aprendizado de Máquina , Imagem por Ressonância Magnética
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1520-1523, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018280

RESUMO

Multiparametric magnetic resonance (mpMR) images are increasingly being used for diagnosis and monitoring of prostate cancer. Detection of malignancy from prostate mpMR images requires expertise, is time consuming and prone to human error. The recent developments of U-net have demonstrated promising detection results in many medical applications. Straightforward use of U-net tends to result in over-detection in mpMR images. The recently developed attention mechanism can help retain only features relevant for malignancy detection, thus improving the detection accuracy. In this work, we propose a U-net architecture that is enhanced by the attention mechanism to detect malignancy in prostate mpMR images. This approach resulted in improved performance in terms of higher Dice score and reduced over-detection when compared to U-net in detecting malignancy.


Assuntos
Imagem por Ressonância Magnética , Neoplasias da Próstata , Diagnóstico por Computador , Humanos , Masculino , Neoplasias da Próstata/diagnóstico por imagem
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1524-1527, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018281

RESUMO

Developing a fast and accurate classifier is an important part of a computer-aided diagnosis system for skin cancer. Melanoma is the most dangerous form of skin cancer which has a high mortality rate. Early detection and prognosis of melanoma can improve survival rates. In this paper, we propose a deep convolutional neural network for automated melanoma detection that is scalable to accommodate a variety of hardware and software constraints. Dermoscopic skin images collected from open sources were used for training the network. The trained network was then tested on a dataset of 2150 malignant or benign images. Overall, the classifier achieved high average values for accuracy, sensitivity, and specificity of 82.95%, 82.99%, and 83.89% respectively. It outperfomed other exisitng networks using the same dataset.


Assuntos
Diagnóstico por Computador , Melanoma , Neoplasias Cutâneas , Dermoscopia , Humanos , Melanoma/diagnóstico por imagem , Redes Neurais de Computação , Neoplasias Cutâneas/diagnóstico por imagem
20.
PLoS One ; 15(10): e0240048, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33031408

RESUMO

BACKGROUND: The detection of wheezes as an exacerbation sign is important in certain respiratory diseases. However, few highly accurate clinical methods are available for automatic detection of wheezes in children. This study aimed to develop a wheeze detection algorithm for practical implementation in children. METHODS: A wheeze recognition algorithm was developed based on wheezes features following the Computerized Respiratory Sound Analysis guidelines. Wheezes can be detected by auscultation with a stethoscope and using an automatic computerized lung sound analysis. Lung sounds were recorded for 30 s in 214 children aged 2 months to 12 years and 11 months in a pediatric consultation room. Files containing recorded lung sounds were assessed by two specialist physicians and divided into two groups: 65 were designated as "wheeze" files, and 149 were designated as "no-wheeze" files. All lung sound judgments were agreed between two specialist physicians. We compared wheeze recognition between the specialist physicians and using the wheeze recognition algorithm and calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files to evaluate the influence of age on the wheeze detection sensitivity. RESULTS: The detection of wheezes was not influenced by age. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 100%, 95.7%, 90.3%, and 100%, respectively. CONCLUSIONS: The wheeze recognition algorithm could identify wheezes in sound files and therefore may be useful in the practical implementation of respiratory illness management at home using properly developed devices.


Assuntos
Algoritmos , Pneumopatias/diagnóstico , Sons Respiratórios/fisiologia , Auscultação , Criança , Pré-Escolar , Diagnóstico por Computador/métodos , Feminino , Humanos , Lactente , Masculino , Sensibilidade e Especificidade
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