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Radiology Radiology

An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI.

In Frontiers in aging neuroscience ; h5-index 64.0

Purpose: To develop and validate an integrative nomogram based on white matter (WM) radiomics biomarkers and nonmotor symptoms for the identification of early-stage Parkinson's disease (PD). Methods: The brain magnetic resonance imaging (MRI) and clinical characteristics of 336 subjects, including 168 patients with PD, were collected from the Parkinson's Progress Markers Initiative (PPMI) database. All subjects were randomly divided into training and test sets. According to the baseline MRI scans of patients in the training set, the WM was segmented to extract the radiomic features of each patient and develop radiomics biomarkers, which were then combined with nonmotor symptoms to build an integrative nomogram using machine learning. Finally, the diagnostic accuracy and reliability of the nomogram were evaluated using a receiver operating characteristic curve and test data, respectively. In addition, we investigated 58 patients with atypical PD who had imaging scans without evidence of dopaminergic deficit (SWEDD) to verify whether the nomogram was able to distinguish patients with typical PD from patients with SWEDD. A decision curve analysis was also performed to validate the clinical practicality of the nomogram. Results: The area under the curve values of the integrative nomogram for the training, testing and verification sets were 0.937, 0.922, and 0.836, respectively; the specificity values were 83.8, 88.2, and 91.38%, respectively; and the sensitivity values were 84.6, 82.4, and 70.69%, respectively. A significant difference in the number of patients with PD was observed between the high-risk group and the low-risk group based on the nomogram (P < 0.05). Conclusion: This integrative nomogram is a new potential method to identify patients with early-stage PD.

Shu Zhenyu, Pang Peipei, Wu Xiao, Cui Sijia, Xu Yuyun, Zhang Minming


“Parkinsons disease”, machine learning, magnetic resonance imaging, radiomics, white matter

General General

A National US Survey of Pediatric Emergency Department Coronavirus Pandemic Preparedness.

In Pediatric emergency care

OBJECTIVE : We aim to describe the current coronavirus disease 2019 (COVID-19) preparedness efforts among a diverse set of pediatric emergency departments (PEDs) within the United States.

METHODS : We conducted a prospective multicenter survey of PED medical director(s) from selected children's hospitals recruited through a long established national research network. The questionnaire was developed by physicians with expertise in pediatric emergency medicine, disaster readiness, human factors, and survey development. Thirty-five children's hospitals were identified for recruitment through an established national research network.

RESULTS : We report on survey responses from 25 (71%) of 35 PEDs, of which 64% were located within academic children's hospitals. All PEDs witnessed decreases in non-COVID-19 patients, 60% had COVID-19-dedicated units, and 32% changed their unit pediatric patient age to include adult patients. All PEDs implemented changes to their staffing model, with the most common change impacting their physician staffing (80%) and triaging model (76%). All PEDs conducted training for appropriate donning and doffing of personal protective equipment (PPE), and 62% reported shortages in PPE. The majority implemented changes in the airway management protocols (84%) and cardiac arrest management in COVID patients (76%). The most common training modalities were video/teleconference (84%) and simulation-based training (72%). The most common learning objectives were team dynamics (60%), and PPE and individual procedural skills (56%).

CONCLUSIONS : This national survey provides insight into PED preparedness efforts, training innovations, and practice changes implemented during the start of COVID-19 pandemic. Pediatric emergency departments implemented broad strategies including modifications to staffing, workflow, and clinical practice while using video/teleconference and simulation as preferred training modalities. Further research is needed to advance the level of preparedness and support deep learning about which preparedness actions were effective for future pandemics.

Auerbach Marc A, Abulebda Kamal, Bona Anna Mary, Falvo Lauren, Hughes Patrick G, Wagner Michael, Barach Paul R, Ahmed Rami A


Dermatology Dermatology

A One-Dimensional CNN-LSTM Model for Epileptic Seizure Recognition Using EEG Signal Analysis.

In Frontiers in neuroscience ; h5-index 72.0

Frequent epileptic seizures cause damage to the human brain, resulting in memory impairment, mental decline, and so on. Therefore, it is important to detect epileptic seizures and provide medical treatment in a timely manner. Currently, medical experts recognize epileptic seizure activity through the visual inspection of electroencephalographic (EEG) signal recordings of patients based on their experience, which takes much time and effort. In view of this, this paper proposes a one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) model for automatic recognition of epileptic seizures through EEG signal analysis. Firstly, the raw EEG signal data are pre-processed and normalized. Then, a 1D convolutional neural network (CNN) is designed to effectively extract the features of the normalized EEG sequence data. In addition, the extracted features are then processed by the LSTM layers in order to further extract the temporal features. After that, the output features are fed into several fully connected layers for final epileptic seizure recognition. The performance of the proposed 1D CNN-LSTM model is verified on the public UCI epileptic seizure recognition data set. Experiments results show that the proposed method achieves high recognition accuracies of 99.39% and 82.00% on the binary and five-class epileptic seizure recognition tasks, respectively. Comparing results with traditional machine learning methods including k-nearest neighbors, support vector machines, and decision trees, other deep learning methods including standard deep neural network and CNN further verify the superiority of the proposed method.

Xu Gaowei, Ren Tianhe, Chen Yu, Che Wenliang


convolutional neural network, electroencephalographic, epileptic seizure recognition, long short-term memory, signal analysis

General General

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia.

In Journal of visualized experiments : JoVE

Segmentation is a complex task, faced by radiologists and researchers as radiomics and machine learning grow in potentiality. The process can either be automatic, semi-automatic, or manual, the first often not being sufficiently precise or easily reproducible, and the last being excessively time consuming when involving large districts with high-resolution acquisitions. A high-resolution CT of the chest is composed of hundreds of images, and this makes the manual approach excessively time consuming. Furthermore, the parenchymal alterations require an expert evaluation to be discerned from the normal appearance; thus, a semi-automatic approach to the segmentation process is, to the best of our knowledge, the most suitable when segmenting pneumonias, especially when their features are still unknown. For the studies conducted in our institute on the imaging of COVID-19, we adopted 3D Slicer, a freeware software produced by the Harvard University, and combined the threshold with the paint brush instruments to achieve fast and precise segmentation of aerated lung, ground glass opacities, and consolidations. When facing complex cases, this method still requires a considerable amount of time for proper manual adjustments, but provides an extremely efficient mean to define segments to use for further analysis, such as the calculation of the percentage of the affected lung parenchyma or texture analysis of the ground glass areas.

Cattabriga Arrigo, Cocozza Maria Adriana, Vara Giulio, Coppola Francesca, Golfieri Rita


General General

Unilateral above-knee amputees achieve symmetric mediolateral ground reaction impulse in walking using an asymmetric gait strategy.

In Journal of biomechanics

The ability to sustain steady straight-ahead walking is one goal of gait rehabilitation for individuals with unilateral above-knee (UAK) amputation. Despite the morphological and musculoskeletal asymmetry resulting from unilateral limb loss, the mediolateral ground-reaction-impulse (GRI) should be counterbalanced between the affected and unaffected limbs during straight-ahead walking. Therefore, we investigated the strategies of mediolateral ground-reaction-force (GRF) generation adopted by UAK prosthesis users walking along a straight path. GRFs of 15 participants with UAK amputation were measured during straight-ahead walking. Then, the mediolateral GRI, stance time, and mean mediolateral GRF during the stance phase of the affected and unaffected limbs were compared. To better understand the GRF generation strategy, statistical-parametric-mapping (SPM) was applied to assess the phase-dependent difference of the mediolateral GRFs between two limbs. The results showed that UAK prosthesis users can achieve symmetric mediolateral GRI during straight-ahead walking by adopting an asymmetric gait strategy: shorter stance time and higher mean mediolateral GRF over the stance phase for the affected than for the unaffected limb. In addition, the analysis using SPM revealed that the affected limb generates a higher mean medial GRF component than the unaffected limb, especially during the single-support phase. Thus, a higher medial GRF during the single-support phase of the affected limb may allow UAK prosthesis users to achieve mediolateral GRI that are similar to those of the unaffected limb. Further insights on these mechanics may serve as guidelines on the improved design of prosthetic devices and the rehabilitation needs of UAK prosthesis users.

Hisano Genki, Hashizume Satoru, Kobayashi Toshiki, Major Matthew J, Nakashima Motomu, Hobara Hiroaki


Above-knee amputee, Amputee locomotion, Gait, Ground reaction impulse, Straight-ahead walking

Ophthalmology Ophthalmology

Development of a deep learning-based image eligibility verification system for detecting and filtering out ineligible fundus images: A multicentre study.

In International journal of medical informatics ; h5-index 49.0

BACKGROUND : Recent advances in artificial intelligence (AI) have shown great promise in detecting some diseases based on medical images. Most studies developed AI diagnostic systems only using eligible images. However, in real-world settings, ineligible images (including poor-quality and poor-location images) that can compromise downstream analysis are inevitable, leading to uncertainty about the performance of these AI systems. This study aims to develop a deep learning-based image eligibility verification system (DLIEVS) for detecting and filtering out ineligible fundus images.

METHODS : A total of 18,031 fundus images (9,188 subjects) collected from 4 clinical centres were used to develop and evaluate the DLIEVS for detecting eligible, poor-location, and poor-quality fundus images. Four deep learning algorithms (AlexNet, DenseNet121, Inception V3, and ResNet50) were leveraged to train models to obtain the best model for the DLIEVS. The performance of the DLIEVS was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard determined by retina experts.

RESULTS : In the internal test dataset, the best algorithm (DenseNet121) achieved AUCs of 1.000, 0.999, and 1.000 for the classification of eligible, poor-location, and poor-quality images, respectively. In the external test datasets, the AUCs of the best algorithm (DenseNet121) for detecting eligible, poor-location, and poor-quality images were ranged from 0.999-1.000, 0.997-1.000, and 0.997-0.999, respectively.

CONCLUSIONS : Our DLIEVS can accurately discriminate poor-quality and poor-location images from eligible images. This system has the potential to serve as a pre-screening technique to filter out ineligible images obtained from real-world settings, ensuring only eligible images will be applied in the subsequent image-based AI diagnostic analyses.

Li Zhongwen, Jiang Jiewei, Zhou Heding, Zheng Qinxiang, Liu Xiaotian, Chen Kuan, Weng Hongfei, Chen Wei


Artificial intelligence, Deep learning, Eligibility, fundus image