Dataset Information

Using Deep Learning for the Classification of Images Generated by Multifocal Visual Evoked Potential.

ABSTRACT: Multifocal visual evoked potential (mfVEP) is used for assessing visual functions in patients with pituitary adenomas. Images generated by mfVEP facilitate evaluation of visual pathway integrity. However, lack of healthy controls, and high time consumption for analyzing data restrict the use of mfVEP in clinical settings; moreover, low signal-noise-ratio (SNR) in some images further increases the difficulty of analysis. I hypothesized that automated workflow with deep learning could facilitate analysis and correct classification of these images. A total of 9,120 images were used in this study. The automated workflow included clustering ideal and noisy images, denoising images using an autoencoder algorithm, and classifying normal and abnormal images using a convolutional neural network. The area under the receiver operating curve (AUC) of the initial algorithm (built on all the images) was 0.801 with an accuracy of 79.9%. The model built on denoised images had an AUC of 0.795 (95% CI: 0.773-0.817) and an accuracy of 78.6% (95% CI: 76.8-80.0%). The model built on ideal images had an AUC of 0.985 (95% CI: 0.976-0.994) and an accuracy of 94.6% (95% CI: 93.6-95.6%). The ensemble model achieved an AUC of 0.908 and an accuracy of 90.8% (sensitivity: 94.3%; specificity: 87.7%). The automated workflow for analyzing mfVEP plots achieved high AUC and accuracy, which suggests its possible clinical use.

SUBMITTER: Qiao N

PROVIDER: S-EPMC6085441 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Using Deep Learning for the Classification of Images Generated by Multifocal Visual Evoked Potential.

Qiao Nidan N

Frontiers in neurology 20180803

Multifocal visual evoked potential (mfVEP) is used for assessing visual functions in patients with pituitary adenomas. Images generated by mfVEP facilitate evaluation of visual pathway integrity. However, lack of healthy controls, and high time consumption for analyzing data restrict the use of mfVEP in clinical settings; moreover, low signal-noise-ratio (SNR) in some images further increases the difficulty of analysis. I hypothesized that automated workflow with deep learning could facilitate a ...[more]

PMID: 30123181

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Project description:BackgroundMultifocal visual evoked potential (MF-VEP) assesses a wider visual field than full-field VEP (FF-VEP) and potentially offers a more precise analysis of optic nerve injury and repair following optic neuritis. MF-VEP may offer advantages over FF-VEP as an endpoint in clinical trials of remyelinating therapies.ObjectiveMF-VEP testing was used to study changes in visual pathways in 48% of RENEW [phase II, opicinumab (anti-LINGO-1; BIIB033) vs. placebo after first acute unilateral optic neuritis] participants.MethodsThis exploratory MF-VEP RENEW substudy compared mean outcomes at weeks 24 and 32 among participants in the intent-to-treat (ITT; n = 39; 72% female; mean age: 32.3 years) and per-protocol (PP; n = 31; 71% female; mean age: 32.2 years) populations in affected and fellow eye latency from fellow eye baseline latency and affected and fellow eye amplitude from their own baselines. Treatment differences were evaluated using analysis of covariance (week 24) and a mixed-effect model of repeated measures (week 32). Last observation carried forward was used to impute missing data at week 24.ResultsA trend for improvement in affected eye MF-VEP latency with opicinumab versus placebo was seen in the ITT and PP populations at weeks 24 and 32. Both treatment groups in the ITT population experienced partial recovery of amplitude in the affected eye at week 32. Notably, the mean change in fellow eye amplitude at weeks 24 and 32 was - 17.57 and - 31.41 nanovolts (nV) in placebo but only - 0.59 and 1.93 nV in the opicinumab group [differences at weeks 24 and 32: 16.98 nV (p = 0.050) and 33.33 nV (p < 0.01), respectively].ConclusionResults from this substudy showed advantages of MF-VEP over FF-VEP in multicenter studies of central nervous system reparative therapies and provide novel evidence that fellow eye visual pathway amplitude loss occurs after optic neuritis but can potentially be prevented by opicinumab treatment.RegistrationClinicalTrials.gov identifier NCT01721161.

Project description:ObjectivesThis study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this is the first study to evaluate the application of deep learning for dental caries in CBCT images.MethodsThe CBCT image dataset comprised 382 molar teeth with caries and 403 noncarious molar cases. The dataset was divided into a development set for training and validation and test set. Three images were obtained for each case, including axial, sagittal, and coronal. The test dataset was provided to a multiple-input convolutional neural network (CNN). The network made predictions regarding the presence or absence of dental decay and classified the lesions according to their depths and types for the provided samples. Accuracy, sensitivity, specificity, and F1 score values were measured for dental caries detection and classification.ResultsThe diagnostic accuracy, sensitivity, specificity, and F1 score for caries detection in carious molar teeth were 95.3%, 92.1%, 96.3%, and 93.2%, respectively, and for noncarious molar teeth were 94.8%, 94.3%, 95.8%, and 94.6%. The CNN network showed high sensitivity, specificity, and accuracy in classifying caries extensions and locations.ConclusionsThis research demonstrates that deep learning models can accurately identify dental caries and classify their depths and types with high accuracy, sensitivity, and specificity. The successful application of deep learning in this field will undoubtedly assist dental practitioners and patients in improving diagnostic and treatment planning in dentistry.Clinical significanceThis study showed that deep learning can accurately detect and classify dental caries. Deep learning can provide dental caries detection accurately. Considering the shortage of dentists in certain areas, using CNNs can lead to broader geographic coverage in detecting dental caries.

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Project description:ObjectivesTo develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images.MethodsIn total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images. Our network is composed of one shared encoder and two inference branches used for segmentation and classification and takes the concatenation of an input image and two Euclidean distance maps of foreground and background clicks provided by a user as input. The performance of hepatic lesion segmentation was evaluated based on the Jaccard index (JI), and the performance of classification was based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC).ResultsWe achieved performance improvements by jointly conducting segmentation and classification. In the segmentation only system, the mean JI was 68.5%. In the classification only system, the accuracy of classifying four types of hepatic lesions was 79.8%. The mean JI and classification accuracy were 68.5% and 82.2%, respectively, for the proposed joint system. The optimal sensitivity and specificity and the AUROC of classifying benign and malignant hepatic lesions of the joint system were 95.0%, 86.0%, and 0.970, respectively. The respective sensitivity, specificity, and the AUROC for classifying four hepatic lesions of the joint system were 86.7%, 89.7%, and 0.947.ConclusionsThe proposed joint system exhibited fair performance compared to segmentation only and classification only systems.Key points• The joint segmentation and classification system using deep learning accurately segmented and classified hepatic lesions selected by user clicks in US examination. • The joint segmentation and classification system for hepatic lesions in US images exhibited higher performance than segmentation only and classification only systems. • The joint segmentation and classification system could assist radiologists with minimal experience in US imaging by characterizing hepatic lesions.

Dataset Information

Using Deep Learning for the Classification of Images Generated by Multifocal Visual Evoked Potential.

Publications

Using Deep Learning for the Classification of Images Generated by Multifocal Visual Evoked Potential.

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