BackgroundOtologic diseases are often difficult to diagnose accurately for primary care providers. Deep learning methods have been applied with great success in many areas of medicine, often outperforming well trained human observers. The aim of this work was to develop and evaluate an automatic software prototype to identify otologic abnormalities using a deep convolutional neural network.Material and methodsA database of 734 unique otoscopic images of various ear pathologies, including 63 cerumen impactions, 120 tympanostomy tubes, and 346 normal tympanic membranes were acquired. 80% of the images were used for the training of a convolutional neural network and the remaining 20% were used for algorithm validation. Image augmentation was employed on the training dataset to increase the number of training images. The general network architecture consisted of three convolutional layers plus batch normalization and dropout layers to avoid over fitting.ResultsThe validation based on 45 datasets not used for model training revealed that the proposed deep convolutional neural network is capable of identifying and differentiating between normal tympanic membranes, tympanostomy tubes, and cerumen impactions with an overall accuracy of 84.4%.ConclusionOur study shows that deep convolutional neural networks hold immense potential as a diagnostic adjunct for otologic disease management.
Background: Patients with Parkinson's disease (PD) and progressive supranuclear palsy Richardson's syndrome (PSP-RS) often show overlapping clinical features, leading to misdiagnoses. The objective of this study was to investigate the feasibility and utility of using multi-modal MRI datasets for an automatic differentiation of PD patients, PSP-RS patients, and healthy control (HC) subjects.Material and Methods: T1-weighted, T2-weighted, and diffusion-tensor (DTI) MRI datasets from 45 PD patients, 20 PSP-RS patients, and 38 HC subjects were available for this study. Using an atlas-based approach, regional values of brain morphology (T1-weighted), brain iron metabolism (T2-weighted), and microstructural integrity (DTI) were measured and employed for feature selection and subsequent classification using combinations of various established machine learning methods.Results: The optimal machine learning model using regional morphology features only achieved a classification accuracy of 65% (67/103 correct classifications) differentiating PD patients, PSP-RS patients, and HC subjects. The optimal machine learning model using only quantitative T2 values performed slightly better and achieved an accuracy of 75.7% (78/103). The optimal classifier using DTI features alone performed considerably better with 95.1% accuracy (98/103). The optimal multi-modal classifier using all features also achieved an accuracy of 95.1% but required more features and achieved a slightly lower F1-score compared to the optimal model using DTI features alone.Conclusion: Machine learning models using multi-modal MRI perform significantly better than uni-modal machine learning models using morphological parameters based on T1-weighted MRI datasets alone or brain iron metabolism markers based on T2-weighted MRI datasets alone. However, machine learnig models using regional brain microstructural integrity metrics computed from DTI datasets perform similar to the optimal multi-modal machine learning model. Thus, given the results from this study cohort, it appears that morphology and brain iron metabolism markers may not provide additional value for classification compared to using DTI metrics alone.
The assessment of mCTA-venous permits a more accurate detection of early ischemia than NCCT and mCTA-rLMC score and is predictive of clinical outcome. We would recommend the inclusion of mCTA perfusion lesion in future endovascular trials aiming at extending current AHA guidelines for EVT in stroke patients with low ASPECTS.
BackgroundParkinson's disease (PD) and progressive supranuclear palsy – Richardson's syndrome (PSP-RS) are often represented by similar clinical symptoms, which may challenge diagnostic accuracy. The objective of this study was to investigate and compare regional cerebral diffusion properties in PD and PSP-RS subjects and evaluate the use of these metrics for an automatic classification framework.Material and methodsDiffusion-tensor MRI datasets from 52 PD and 21 PSP-RS subjects were employed for this study. Using an atlas-based approach, regional median values of mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) were measured and employed for feature selection using RELIEFF and subsequent classification using a support vector machine.ResultsAccording to RELIEFF, the top 17 diffusion values consisting of deep gray matter structures, the brainstem, and frontal cortex were found to be especially informative for an automatic classification. A MANCOVA analysis performed on these diffusion values as dependent variables revealed that PSP-RS and PD subjects differ significantly (p < .001). Generally, PSP-RS subjects exhibit reduced FA, and increased MD, RD, and AD values in nearly all brain structures analyzed compared to PD subjects. The leave-one-out cross-validation of the support vector machine classifier revealed that the classifier can differentiate PD and PSP-RS subjects with an accuracy of 87.7%. More precisely, six PD subjects were wrongly classified as PSP-RS and three PSP-RS subjects were wrongly classified as PD.ConclusionThe results of this study demonstrate that PSP-RS subjects exhibit widespread and more severe diffusion alterations compared to PD patients, which appears valuable for an automatic computer-aided diagnosis approach.
Objectives: This study aimed to explore cytokine alterations following pediatric sports-related concussion (SRC) and whether a specific cytokine profile could predict symptom burden and time to return to sports (RTS). Setting: Sports Medicine Clinic. Participants: Youth ice hockey participants (aged 12-17 years) were recruited prior to the 2013-2016 hockey season. Design: Prospective exploratory cohort study. Main Measure: Following SRC, saliva samples were collected and a Sport Concussion Assessment Tool version 3 (SCAT3) was administered within 72 hours of injury and analyzed for cytokines. Additive regression of decision stumps was used to model symptom burden and length to RTS based on cytokine and clinical features. RRelieFF feature selection was used to determine the predictive value of each cytokine and clinical feature, as well as to identify the optimal cytokine profile for the symptom burden and RTS. Results: Thirty-six participants provided samples post-SRC (81% male; age 14.4 ± 1.3 years). Of these, 10 features, sex, number of previous concussions, and 8 cytokines, were identified to lead to the best prediction of symptom severity (r = 0.505, P = .002), while 12 cytokines, age, and history of previous concussions predicted the number of symptoms best (r = 0.637, P < .001). The prediction of RTS led to the worst results, requiring 21 cytokines, age, sex, and number of previous concussions as features (r = −0.320, P = .076). Conclusions: In pediatric ice hockey participants following SRC, there is evidence of saliva cytokine profiles that are associated with increased symptom burden. However, further studies are needed.
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